Lung Nodule Detection & Character is at Ion on Multi Slice CT

Lung nodule detection and characterization with

multislice CT

Jane P. Ko, MD*, David P. Naidich, MD

Department of Radiology, New York University Medical Center, 560 1st Avenue, New York, NY 10016, USA

Pulmonary nodules remain a diagnostic dilemma.

Nodules are frequently incidentally detected in pa-

tients undergoing chest radiography for unrelated

symptoms. Not infrequently, patients undergoing diag-

nostic chest CT for nodules identified on radiograph

are found to have more nodules of smaller size [1–3].

A pulmonary nodule is generally defined as a

rounded opacity, at least moderately well marginated

and no greater than 3 cm in maximum diameter [4].

The most common causes for nodules detected by

chest radiograph are granulomatous disease and lung

cancer [5]. Other etiologies include solitary pulmo-

nary metastases, hamartomas, and carcinoid tumors

[6,7]. Approximately 30% to 40% of solitary pul-

monary nodules identified by chest radiography are

malignant [6,7]. The likelihood of a nodule represent-

ing malignancy is dependent on the overall relative

prevalence of disease. Patients with nodules in en-

demic areas with fungi have a lower likelihood that a

nodule represents cancer.

CT plays a major role in the detection and further

characterization of pulmonary nodules. On CT, as

also true for chest radiography [8], attempts to

differentiate nodules as benign versus malignant have

relied on classifications focused on attenuation,

enhancement characteristics, morphology, and size.

The ability to obtain high-resolution imaging is

vital for maximizing the ability to characterize pul-

monary nodules and manage them with subsequent

assessment of growth. Recently, multislice CT

(MSCT) technology has facilitated nodule evaluation

by enabling one to obtain contiguous thin sections on

the order of 0.5 to 1 mm while minimizing respiratory

and cardiac motion artifact.

MSCT technique

The MSCT protocols for lung parenchyma imag-

ing attempt to balance the need for Z-axis coverage

with obtaining high-resolution sections. It is important

to understand the capabilities of the MSCT scanner

before determining a protocol for nodule imaging.

CT imaging typically uses kilovolt potentials

(kVp) ranging between 120 and 140 and a 0.5- to

1-second scan time. By using shorter scan times,

motion artifact from cardiac pulsation and respiratory

motion is reduced, or increased coverage in the Z axis

can be obtained. Table speed and pitch are variable

depending on the smallest desired slice thickness but

typically pitch ranges between 1.5 and 2. The thorax

is imaged from the lung apices to the upper abdomen

to include the costophrenic angles. For confirmation

of a suspected pulmonary nodule on chest radiograph,

intravenous contrast administration is not mandatory,

although helpful for delineating a nodule when sus-

pected to lie adjacent to the mediastinum or hilum.

Typical clinical review of images by radiologists

entails reconstruction of the data into 5- to 7-mm

sections using a 512� 512 matrix. Reconstructions

for evaluating the lung parenchyma are best performed

using a high-frequency algorithm, which enhances the

interfaces of structures of differing attenuation but

increases image noise. A low-frequency algorithm

should be used to minimize image noise and hetero-

geneity when evaluating nodule density, particularly

when axial sections less than 5 mm are used (Fig. 1).

0033-8389/03/$ – see front matter D 2003, Elsevier Inc. All rights reserved.

doi:10.1016/S0033-8389(03)00031-9

* Corresponding author.

E-mail address: [email protected] (J.P. Ko).

Radiol Clin N Am 41 (2003) 575–597

The field of view (FOV) chosen for general image

reconstruction should maximize the size of the lung

parenchyma while including most soft tissues of the

thorax. Typical FOVs range between 25 and 35 cm,

depending on the patient’s size. For high-resolution

imaging of nodules, targeted reconstructions per-

formed with smaller FOVs between 10 and 20 cm

are important for assessing nodule morphology in

terms of shape and border characteristics in addition

to attenuation. Additionally, decreasing the FOV

decreases the size of each pixel in the 512� 512

matrix and aids computerized methods for nodule

size measurements.

Typically, diagnostic CT is performed using tube

currents between 200 and 240 milliampere in adults.

For low-dose CT technique currently being investi-

gated for screening high-risk populations for lung

cancer, the protocol is adjusted by decreasing tube

currents to 20 to 50 mA [9–12]. With subsecond

imaging times provided by newer CT scanners [13],

it is important to remember that the tube current is

higher than the tube current time, which is expressed in

milliampere seconds (mAs). For example, using

20 mAs at 0.5-second gantry rotation times leads to a

40-mA tube current [14]. Effective radiation doses for

low-dose CT are equivalent to 1.3 to 2.2 two-view

chest radiographs for men and women, respectively

[15]. Patient effective dose from screening CT, using a

pitch of 2 and tube current of 25 mA, is approximately

0.3 mSv (30 mrem) for men and 0.55 mSv (55 mrem)

for women, whereas chest radiography effective dose

ranges from 0.06 to 0.25 mSv (6 to 25 mrem) [15]. For

diagnostic-quality CT, patients typically receive 3 to

27 mSv (300 to 2700 mrem) of effective dose equiv-

alents or 10 times the dose of screening CT [15]. New

CT technology allows modulation of the tube current

depending on the thickness of the thorax in different

locations, leading to relatively smaller milliampere

seconds [16–18].

MSCT technology enables retrospective recon-

struction of CT data into 1- to 1.25-mm sections and

potentially smaller sections given new 8 and 16 de-

tector row scanners. Thin sections are especially bene-

ficial for both screening and diagnostic scenarios. The

ability to obtain retrospective high-resolution images

eliminates the need for a patient to return for thin-

section imaging of a nodule. Additionally, low mil-

liampere technique may replace the use of diagnostic

CT technique when following incidentally detected

nodules on low milliampere studies.

Fig. 1. Reconstruction algorithm and slice thickness. (A) One-millimeter section reconstructed using a high-frequency algorithm

(1 mm, HFA, top) has greater image noise, as seen on soft tissue windows, than when reconstructed using a low-frequency

algorithm (1 mm, LFA, top). Subtle low-attenuation areas within the nodule are assessed more readily on the low-frequency

algorithm image (1 mm, LFA image, top). The nodule margin is better assessed on the image reconstructed with the high-

frequency algorithm (1 mm, HFA, bottom), because of the higher spatial resolution. The same nodule reconstructed using 3-mm

sections and a low-frequency algorithm is affected by partial volume effect, leading to decreased spatial resolution on lung

windows (3 mm, LFA, bottom). (B) A region of interest placed on the nodule in the low-attenuation region demonstrates fat

attenuation consistent with a hamartoma.

J.P. Ko, D.P. Naidich / Radiol Clin N Am 41 (2003) 575–597576

Detection of pulmonary nodules

CT has been shown to be more sensitive than

chest radiography for detecting pulmonary nodules

[1,2,10,19]. CT provides better contrast between the

nodule and lung and eliminates overlying structures,

such as the chest wall, mediastinum, diaphragm, and

vessels. Some limitations in nodule identification on

CT have been noted, however, which may translate

to missed cancers [20–22]. White et al [22] demon-

strated missed nodules on CT to be in an endobron-

chial or lower lobe location. On screening CT,

Kakinuma et al [20] showed that overlooked nodules

were small, on the order of 4 to 6 mm; faint

in attenuation; adjacent to vessels; and adjacent

to findings of prior tuberculosis. In its follow-up of

patients screened for lung cancer, the Early Lung

Cancer Action Project (ELCAP) reported that 22 of

63 newly detected nodules were retrospectively evi-

dent on the initial prevalence CT [23]. The diagnosis

of these initially overlooked nodules may relate to

the thinner 5-mm sections or the higher milliampere

technique used on the follow-up CTs. Several strat-

egies have been proposed to offset these limitations.

These strategies are based on knowledge of the

factors affecting nodule recognition that include

reader experience and variability, CT technique and

viewing conditions, and nodule characteristics.

It has been demonstrated that interobserver and

intraobserver variability in nodule detection is a factor

in the number of nodules identified [24–26]. Wor-

manns et al [26] reported interobserver variation in the

recognition and measurement of pulmonary nodules

on CT. On 5-mm sections at 3-mm overlapping

intervals, from a total of 230 nodules that were found

by either of two readers, only 45% (103 or 230) of

nodules were found by both readers, with 29% (66 of

230) of nodules being graded as definite by one reader

and missed by the other. The number of overlooked

nodules [24] and the number of false-positive diag-

noses [27] have been shown to differ between readers

with greater and less experience.

An understanding of the effect of technical

imaging parameters on nodule detection is useful.

Helical technique, as compared with conventional

axial CT, has been shown to improve the identifica-

tion of nodules [3,19,28]. Helical CT enables volu-

metric imaging and minimizes missed nodules

secondary to respiratory excursion. In a study by

Wright et al [29] on single-row helical CT, no

significant difference in nodule recognition was

identified between pitches of 1, 1.2, 1.5, and 2,

although a tendency to undercount lesions increased

with increasing pitch. The benefit of helical CT is

maximized with reconstruction in overlapping sec-

tions, which are determined retrospectively and do

not affect patient dose. Without overlapping recon-

struction intervals, a mild degradation in the slice-

sensitivity profile, which is broadened because of

table motion, decreases contrast resolution for small

lesions and spatial resolution in the image plane

[28,30]. For some MSCT scanners, broadening of

the slice-sensitivity profile is independent of pitch up

to 2 and has a smaller role in image quality [13].

Wormanns et al [26] determined that overlapping

reconstructions improved nodule detection. For data

reconstructed in 5-mm nonoverlapping sections,

fewer nodules (205) were perceived by either of

the two readers than on 5-mm sections reconstructed

at 3-mm intervals (230), and only 47% (96 of 205)

of nodules were identified by both readers. A high-

er percentage (78% [80 of 103]) of nodules was

recorded as definite findings by both readers on

the 3-mm reconstructions as compared with the

5-mm reconstructions (69% [66 of 96]). Other stud-

ies have demonstrated the benefit of overlapping

reconstructions [27,31], because readers detected

more nodules of smaller sizes without a decrease

in reader confidence.

Recent studies have also evaluated low-dose CT

in relation to diagnostic CT for nodule detection

[25,32]. They demonstrated no decrease in recog-

nition of small nodules on the order of 3 to 5 mm at

20 mAs on 10-mm sections and 30 mAs on 8-mm

sections on low-dose CT [25,32]. These data provide

a rationale for using low-dose technique for lung

cancer screening. Helical CT also reduces cardiac

motion artifacts in comparison with conventional CT

[3]. The increasing image acquisition speed enabled

by currently available four-detector row MSCTs and

by the newer 8- and 16-detector row scanners further

minimizes the amount of cardiac and respiratory

motion that degrades image quality, particularly in

the lower lobes.

Methods of viewing CT studies have also been

shown to affect interpretation. In a study by Seltzer

et al [33], lung nodule detection decreased as image

size was reduced and viewing distance remained fixed,

although adjustment of viewing distances compen-

sated for the reduction in acuity. Nodule recogni-

tion is further aided by cine viewing of images on

a workstation. Cine viewing facilitates differentiation

of vessels from nodules, and a significant advantage

was demonstrated in particular for detecting nodules

less than or equal to 5mm [34]. Similarly, cine viewing

of postprocessed data in maximum-intensity projec-

tion (MIP) images may improve nodule perception

[24,35,36]. The MIP technique takes advantage of the

J.P. Ko, D.P. Naidich / Radiol Clin N Am 41 (2003) 575–597 577

spatial resolution benefits provided by high-resolution

volumetric data that can now be obtained with MSCT

[37]. Viewing of MIP may minimize the need for the

radiologist to review large data sets, which can

approach 300 images if the thorax is reconstructed

into 1-mm sections, subsequently adversely affecting

interpretation time. The MIP technique entails iden-

tifying the brightest pixel in a selected volume of data

along a ray projection and displaying this brightness

value in the final image [38].MIP can be created in any

plane. The MIP technique enables visualization of

small vessels and other structures with the speed and

convenience of thick slabs (Fig. 2). Small 2-mm

nodules, for example, that may exist on two contigu-

ous axial nonoverlapped 1-mm sections can be seen

readily on a thick slab of approximately 10 mm,

because these slabs are advanced incrementally. Gru-

den et al [24], using 10-mm sliding slabs obtained from

3.75-mm reconstructions, minimized the effect of

observer experience for peripheral nodules and

improved the detection of central nodules. The MIP

slab, however, is not optimal for visualization of the

airways and needs to be viewed in conjunction with

conventional axial sections.

In terms of nodule characteristics, the size, loca-

tion, and attenuation affect the ability to perceive

nodules on CT. In a study by Rusinek et al [25] using

low-dose 10-mm sections, nodules on the order of

Fig. 2. Maximum-intensity projection (MIP) technique and lung nodules. (A) Axial 7-mm CT section demonstrates multiple

ground-glass nodules in the right lung (some marked with arrows). A large part ground-glass, part solid nodule in the left lung

(curved arrow) is adjacent to other ground-glass nodules. (B) Axial 9-mm MIP slabs reconstructed from 1.25-mm axial sections

better demonstrate the ground-glass nodules in the right lung (arrows). (C) On the axial 9-mm MIP slab, a small ovoid nodule

(arrow) is difficult to separate from a vessel. (D) Coronal 9-mm MIP slab confirms nodule to lie above a small vessel (white

arrow) and other small nodules (curved white arrows, black arrow) are evident.


3 mm were detected 37% of the time as compared

with 62% detection for 5-mm nodules. Decreased

perception is related to partial volume effect, which is

accentuated as nodules become smaller and image

sections larger. It is intuitive that decreasing axial

section thickness likely improves nodule identifica-

tion by radiologists [39]. Central nodules, secondary

to their close proximity to vessels of larger caliber,

are more difficult to identify than nodules in the lung

periphery, where vessels are smaller and less

crowded. It is particularly easier to identify nodules

that abut the pleura, because vessels are typically not

visualized on CT within 5 to 10 mm of the pleura.

Decreased recognition of nodules in the perihilar and

central regions as compared with the lung periphery

had been demonstrated on low-dose (46%, 58%, and

74% for perihilar, central, and peripheral regions,

respectively) and diagnostic (85%, 62%, and 43%)

CT [25]. Particularly on thicker axial sections, the

faint density of ground-glass nodules has been asso-

ciated with missed lung cancers [20].

Characterization of nodules

Nodules can be characterized according to their

morphology, densitometry, size, and growth. The

evaluation of pulmonary nodules in the past has been

performed primarily on chest radiography; however,

nodule characterization has been facilitated by CT,

which has higher spatial and contrast resolution. On

chest radiography, the assessment of nodule morphol-

ogy is limited particularly by its lower contrast

resolution and overlying structures. Calcifications

on chest radiographs are difficult to assess particu-

larly for nodules greater than 5 mm [40]. Addition-

ally, whereas nodule size and growth had been

primarily studied on chest radiography [41–43], the

sensitivity for detecting change has been increased

with the use of CT.

Morphology

Nodules morphology can be characterized in terms

of border, shape, and internal characteristics. Char-

acterization of a nodule’s border and contour has been

advanced by CT and MSCT. The study of nodule

morphology has primarily concerned edge analysis on

chest CT [6,7]. The margins of pulmonary nodules can

be characterized as spiculated, lobulated, or smooth

[7]. In an early landmark article, Siegelman et al [6]

assessed the borders of pulmonary nodules with CT

and showed that 88.5% of nodules with mild or grossly

irregular spiculations were malignant as compared

with a 21.8% and 57.7% for nodules with a smooth

andmoderately smooth border, respectively. Similarly,

Zerhouni et al [7] demonstrated that most primary lung

cancers (73 of 120 or 61%) had spiculated margins,

unlike carcinoid tumors, metastatic disease, and

benign lesions (18 of 175 or 10%). Lobulated or

smooth margins, however, did not preclude malig-

nancy, particularly metastatic disease; 41 of 130 nod-

ules classified as smooth and 26 of 48 nodules

classified as lobulated were primary or secondary

cancers in the parenchyma. It is anticipated that

volume-rendering techniques will enable improved

three-dimensional contour evaluation of nodules.

Computer classification schemes may improve clas-

sification of nodules in terms of roundness, circularity,

and compactness and subsequently increase under-

standing of the clinical significance of these charac-

teristics [44].

The feeding vessel sign has been described in a

number of other entities, such as metastases [45] and

infarcts [46,47]; however, to assess this sign properly,

thin sections should be obtained and can be facilitated

by retrospective reconstruction of MSCT data. High-

resolution reconstructions combined with the ability

to time contrast bolus injections to opacify the

pulmonary arteries optimally facilitate the diagnosis

of arteriovenous malformations and other suspected

vascular lesions. The identification of dilated feeding

and draining vessels is characteristic for an arterio-

venous malformation (Fig. 3).

Internal characteristics have been helpful for

characterization, particularly when nodules demon-

strate pseudocavitation or air bronchograms. Pseu-

docavitation is a term used to describe small

lucencies within a nodule (Fig. 4). Rather than rep-

resenting necrosis and cavitation, the small lucencies

have been shown to represent lepidic growth, defined

as growth of tumor cells around alveolar walls with

sparing of expanded air-containing regions and

dilated bronchioles. These findings, in addition to

the presence of air bronchograms, have been asso-

ciated with the bronchoalveolar subtype of adenocar-

cinoma [48,49].

The halo sign is defined as ground-glass opacity

surrounding the circumference of a nodule or mass

[4]. The halo sign was first described by Kuhlman

et al [50] in neutropenic, immunocompromised

patients with early angioinvasive pulmonary asper-

gillosis. In their study, the ground-glass halo corre-

lated with pulmonary hemorrhage. The halo sign has

also been described with other infections, vasculitis,

and neoplasms. More recently, smaller, subtle nod-

ules with ground-glass borders or internal compo-

nents are identified more frequently with the use of


high-resolution sections. The significance of such

ground-glass–containing nodules has increased along

with the awareness of the spectrum of adenocarci-

noma and preneoplasia and has lately been termed

subsolid nodules [51]. The term subsolid emphasizes

the similarity between nodules with both solid and

ground-glass features (part solid) and those com-

prised of ground-glass attenuation only (nonsolid)

(Fig. 5).

Recently, close correspondence has been estab-

lished between the CT findings of subsolid nodules

and the histologic spectrum of adenocarcinoma. The

Noguchi pathologic classification has been used to

describe the spectrum of bronchoalveolar features in

adenocarcinoma. Types A and B represent localized

bronchoalveolar carcinoma (BAC) with and without

structural collapse, respectively, and are located at

one end of the spectrum. Type C correlates with

localized BAC with active fibroblastic proliferation,

whereas types D, E, and F represent poorly differ-

entiated, tubular, and papillary carcinoma, respec-

tively, with a compressive growth pattern [52]. The

ground-glass components in subsolid nodules origi-

nating from adenocarcinoma have been associated

with a lepidic growth pattern or mucin production

[48,53]. The degree of ground-glass opacity in rela-

tion to solid components relates to the likelihood of

malignancy and correlates with prognosis. Using CT

and histopathologic correlation, Aoki et al [54] identi-

fied that the development of solid components within

a ground-glass nodule was associated with more

invasive behavior (Noguchi types C, D, E, and F)

Fig. 3. Arteriovenous malformation. (A) Axial contrast-enhanced CT, soft tissue window, demonstrating intensely enhancing

nodule (arrow) the same attenuation as the heart chambers that abuts the pleura. A high-attenuation left hemothorax is present in

this pregnant woman. (B) Lung window demonstrates characteristic large feeding artery (arrow) leading to nodule. (C)

Arteriogram demonstrating aneurysm.


in the spectrum of small peripheral adenocarcinomas

with bronchoalveolar components. Kim et al [55]

showed that greater ground-glass opacity on CT in

small, less than 3 cm peripheral adenocarcinomas that

were resected was significantly greater in patients

without recurrence. In a screened population, part-

solid nodules correlated with a higher malignancy

rate (63%) than nonsolid nodules (8%) and solid

nodules (7%) [51].

Atypical adenomatous hyperplasia

It should be emphasized that ground-glass opacity

in a nodule may also represent atypical adenomatous

hyperplasia (AAH) (high and low grade) in addition

to BAC. Although initially dismissed as an incidental

finding in resection specimens of lung cancer, AAH

is now believed to be a precursor of BAC. This

concept results from studies on tumor markers that

are enabled by refinements in microdissection and

polymerase chain reaction amplification [56,57]. A

number of candidate markers for malignant trans-

formation, such as mutations in the p53 tumor sup-

pressor gene and the K-ras oncogene, which is

involved in signal transduction and cellular prolifera-

tion, have been identified through immunohistochem-

ical and molecular analysis of specimens obtained

from direct resection or biopsy of nodules [58],

biopsy of airways [59], and serum [60]. The results

of tumor marker studies confirm AAH as a premalig-

nant lesion in the spectrum of adenocarcinoma. This

realization has led to a revision of the World Health

Organization Histologic Classification of Lung and

Pleural Tumors in 1999 to incorporate AAH, squa-

mous dysplasia-carcinoma in situ, and diffuse idio-

pathic pulmonary neuroendocrine cell hyperplasia

under a category of ‘‘preinvasive lesions’’ [61].

The differentiation of AAH from BAC by imaging

and pathology is difficult. Histologically, AAH was

demonstrated to have a smaller mean nuclear diam-

eter, less nuclear atypia, smaller nucleoli, more

lepidic growth, no invasion of the basement mem-

brane, and smaller cell size than BAC [58,62,63]. On

imaging, both AAH and BAC have significant

ground-glass components. In their analysis of pathol-

ogy specimens, Kitamura et al [58] reported a dis-

tinction of BAC from AAH when a lesion size of

5 mm was used. They also noted that high-grade

AAHs were slightly larger than low-grade AAHs.

Kawakami et al [64] described AAH in nine patients

on CT. The AAHs were round, ground-glass nodules

between 6 and 17 mm (mean, 8.8 mm) with smooth,

distinct borders and no pleural indentation. It is

Fig. 4. Bronchoalveolar carcinoma. MSCT axial 1.25-mm section. Note the small lucency within the nodule with solid

attenuation consistent with pseudocavitation.


difficult to identify if malignant transformation has

occurred in subtle ground-glass nodules, although

size may be an indicator.

Densitometry

Nodule attenuation was first studied on chest

radiography, primarily emphasizing patterns of cal-

cification [65,66]. Calcification in lamellated, target,

and central patterns has been associated with benign

granulomatous disease, whereas popcorn calcification

has been described as diagnostic of hamartomas [66].

Stippled and eccentric calcifications have been asso-

ciated with malignancy (Fig. 6). Rarely, homoge-

neous calcification may be identified in metastatic

disease, but in this setting the nodules are typically

Fig. 5. Subsolid nodules. MSCT axial 1.25-mm sections. (A, B) Axial sections with magnification view, respectively,

demonstrate the faint increased attenuation of a pure ground-glass nodule (arrow) in the right upper lobe through which vessels

pass. (C) Axial 1.25-mm sections enable easy identification of the solid (curved arrows) and ground-glass (straight arrows)

components of two subsolid nodules in the right upper lobe.


multiple. Unfortunately, calcification patterns are not

reliably detected on chest radiograph [65]. In addi-

tion, other attenuation characteristics, such as fat,

ground-glass opacity, and fluid components, cannot

be determined reliably using chest radiography, sec-

ondary to its low contrast.

Using CT, nodule density or attenuation can be

assessed both with and without intravenous contrast

with increased confidence. The understanding of

nodule attenuation on CT and its relationship to

malignancy was established by Siegelman et al

[6,67] and Zerhouni et al [7]. Identical to radiog-

raphy, nodules without a demonstrated benign cal-

cification pattern on CT are considered indeterminate,

unless associated with fat. Fat-containing nodules

are typically benign and include lipoid pneumonia

(Fig. 7) and hamartomas (see Fig. 1). Rarely, lipo-

sarcomas can lead to nodular metastases with fat, but

are usually multiple [66]. In nodules in which

calcium is not identified confidently by visual inspec-

tion, quantitative nodule densitometry may also be

used [6,67]. As shown by Siegelman et al [6],

indeterminate nodules comprised about 70% to 80%

of all nodules detected on chest radiography [7].

Initially, a threshold of 164 HU was proposed to

separate benign from malignant nodules; however,

reproducibility of these results proved difficult

because of variables that affected nodule density

measurements. These variables related to differences

between CT scanners; location of nodules within the

thorax (thoracic geometry); and reconstruction algo-

rithms [68,69]. To overcome these variations, a chest

CT phantom with nodules was developed to serve as

a standard to which a patient’s nodule could be

Fig. 6. Nodule calcification patterns: benign and malignant.

Central (top, left), popcorn (top, right), solid (center, left),

and lamellated (center, right) calcification patterns have

been associated with benign disease. Stippled (bottom, left)

or eccentric (bottom, right) calcifications are suspicious

for malignancy.

Fig. 7. Lipoid pneumonia. MSCT 1.25-mm axial sections. (A) A spiculated nodule is present in the left lower lobe (arrow). A

mass is noted in the right lower lobe. (B) Soft tissue window settings readily demonstrate the low attenuation within the nodule

correlating with fat, consistent with the patient’s history of lipoid pneumonia. Similar low-attenuation areas corresponding to fat

are seen in the right lower lobe mass.


compared [7,70]. Using a standard phantom, 66 of

384 nodules in a multicenter study were CT-deter-

mined benign nodules; only one grew on follow-up

and subsequently was verified as an adenocarcinoma

[7]. Of the 65 benign nodules that remained stable on

follow-up, the reference phantom was needed in

28 cases. This process of standardization, however,

proved cumbersome and did not gain acceptance.

Recently, thin-section imaging provided by MSCT

has facilitated characterization of small nodules on

the order of 3 mm as calcified by reducing partial

volume effect (Fig. 8).

Contrast enhancement

The study of nodule attenuation following the

administration of intravenous contrast was first

explored as an indicator of malignancy by Littleton

et al [71]. Thin-section CT and nodule enhancement

were further investigated by Swensen et al [72,73].

In a multicenter study, Swensen et al [72,73] dem-

onstrated 98% sensitivity and 58% specificity for

benignity using less than 15 HU as the maximal

amount of enhancement from precontrast images.

This technique entails the use of sections 3mm or

less. A helical series of images prior to contrast

Fig. 8. Granuloma characterization using MSCT. (A) MSCTwas performed using a 1-mm collimator and data reconstructed into

a 7-mm section and viewed under lung window settings. A less than 3-mm nodule in the right lower lobe appears noncalcified

(arrow). (B, C) Same MSCT data reconstructed into 1.25-mm sections and viewed under lung and soft tissue windows,

respectively, demonstrate the nodule to be calcified (arrow).


administration is followed by serial helical acqui-

sitions at 1, 2, 3, and 4 minutes after intravenous

administration of contrast (300 mg of iodine per

milliliter) at 2 mL/second. Densitometry measure-

ments should be performed on mediastinal windows

to minimize partial volume effects and are obtained

by placing regions of interests to occupy approxi-

mately 70% of the lung nodule’s short and long axis

dimension. Use of a soft tissue reconstruction algo-

rithm decreases image noise and standard deviation

of densitometry measurements.

Technical pitfalls and limitations have been re-

ported. Difficulties pertain to measuring nodule at-

tenuation because of small nodule size, respiratory

motion on imaging, and nonspecific heterogeneous

patterns of enhancement. In the study by Swensen et al

[73], nodule dimensions ranged between 5 and 40mm;

however, most of the mean diameters of the nodules

were greater than 10 mm (means, 14 and 17 mm for

benign and malignant nodules, respectively). For

smaller nodules, placement of regions of interest

may be problematic. When there is respiratory motion,

streak artifacts may either lower or raise attenuation

values. Heterogeneous enhancement may occur par-

ticularly in larger lesions with necrosis. Consequently,

the technique should be applied only to those lesions

3 cm or less in size, and regions of interest should be

placed to avoid areas of necrosis. Some of these

limitations can be overcome by MSCT, which enables

nodule enhancement analysis to be performed effec-

tively on smaller nodules. Furthermore, faster scan

times minimize the risk of motion artifacts, and the

ability to image using 1-mm sections facilitates the use

of this technique on nodules less than 1 cm (Fig. 9).

Growth

Interest in assessing nodule growth as a means to

differentiate benign from malignant nodules began

when evaluating nodules on radiography [74]. Size

was assessed using a ruler to measure the maximal

dimensions on posterior-anterior or lateral radiographs

[42]. Other methods entailed matching circular stan-

dards printed on a transparency with the outer margin

of a nodule, selecting the standard that matched the

best, and converting into diameter for the maximal di-

mension [42]. The performance of thesemeasurements

was often difficult, and hence some difficult cases

were excluded from studies on nodule growth [42].

How fast growth occurs is typically expressed in

terms of the time for a nodule to double in volume,

termed the tumor volume doubling time (VDT). The

concept of VDT arose from the understanding that

cancer cells grow exponentially, unlike benign pro-

cesses. For a cancer cell 10 microns in diameter to

grow to a 1-mm nodule, 20 doublings are needed. For

a nodule to grow to a detectable size of 5 mm,

about 25 doublings are needed, and to 1 cm, about

30 doublings [41]. A large number of doublings

occur before a tumor is of detectable size radiograph-

ically. To obtain a VDT, the diameters of the nodules

are converted into volume, assuming the nodule has

the shape of a sphere [41,42]. VDT can be calculated

if the time difference (t), initial volume (V0), and

volume at time t (Vt) are known using the following

relationship [75]:

VDT ¼ ½t� log2�=logðVt=V0Þ:

From studies on radiography, VDTs for lung

cancer were shown to range between 20 and 400 days

[41–43], whereas infections and very rarely meta-

static disease from testicular tumors and sarcomas

had shorter VDTs of less than 20 days [41,42].

Rarely, carcinoid tumors exhibited long doubling

times [42]. Hamartomas and granulomas were asso-

ciated with longer doubling times ( > 400 days). From

these studies, it was determined that stability on

radiograph for 2 years implied benignity.

CT technology, particularly MSCT, enables the

identification of a larger number of nodules of smaller

dimensions and has renewed interest in evaluating

nodule growth, in the hope that nodule growth can be

identified at earlier stages. In addition to the continu-

ing need to assess and quantify nodule growth in

oncology patients undergoing therapy for known

pulmonary metastases, interest in the use of screening

chest CT for the early detection of lung cancer has

also provided impetus for the assessment of nodule

dimensions and growth. CT has provided new

information that questions the premise that nodules

with doubling times longer than 1 to 2 years are

benign. Recent CT studies identified BACs with

mean VDTs on the order of 800 days (range, 662 to

1486 days) for localized BACs and localized BACs

with foci of structural collapse of the alveoli [54,76].

There may be two different time frames for assessing

growth, depending on whether a nodule may repre-

sent a slow-growing BAC (ie, if it was purely ground

glass) or if it could represent a more aggressive

lesion, such as a solid nodule or mixed ground-glass

and solid nodule.

Accurate assessment of nodule growth requires

determining when and how frequently a nodule

should be followed on CT. Specific guidelines have

not been established. Currently, many institutions use

CT to follow nodules at 3, 6, 12, and 24 months.

Variations in follow-up protocols typically relate to


Fig. 9. Small carcinoid tumor. (A, B) MSCT sections of 1.25mm were obtained before and 2 minutes after intravenous contrast

administration. Images were reconstructed using a low-frequency algorithm and viewed using soft tissue windows. The

precontrast attenuation was 93 HU, and 2 minutes after the administration of intravenous contrast, the attenuation maximally

increased by 69 HU to 162 HU, consistent with a hypervascular lesion. (C) A 1.25-mm axial section, lung window setting,

demonstrates the nodule along the course of the lateral segmental bronchus in the right middle lobe, consistent with the

carcinoid’s endobronchial location.


nodule size. For example, in the recently published

Mayo Clinic CT screening study, the protocol recom-

mended follow-up in 3 months for nodules greater

than 3 mm and 6 months for nodules less than or

equal to 3 mm [12]. Differences in nodule size are

difficult to detect visually, particularly for small

nodules (Fig. 10). For example, a 3-mm nodule,

when doubled in volume, should measure 3.8 mm,

a difference that may be difficult to discern visually.

Two volume doublings to 4.8 mm potentially may be

detectable at 40 days using the fastest growth sce-

nario or at 200 days (approximately 6 months) for

nodules with intermediate VDTs. At this size, only

the rapidly growing nodules are detected before or at

a 3-month follow-up, requiring more careful surveil-

lance with follow-up studies obtained in another 3 to

6 months. Using the knowledge that some BACs

have doubling times of 800 days, for a 5-mm nodule

to double in volume to 6.3 mm, it takes 800 days

or 2.2 years. A difference visible to the radiologist’s

eye may be seen only after one more doubling or

4.4 years, when the nodule is 7.9 mm.

Volume quantification on CT

Interest in measuring nodule growth on CT has

increased the need for accurate volume quantifica-

tion. Nodule size on CT traditionally has been

expressed as bidimensional perpendicular measure-

ments (the largest dimension and its perpendicular

dimension) that are then multiplied to obtain a

bidimensional cross product, as recommended by

the World Health Organization criteria [77]. Size

has also been recorded in terms of the largest dimen-

sion, as suggested in the more recent Response

Evaluation Criteria in Solid Tumors Guidelines [77].

A large amount of interobserver error occurs, how-

ever, when small nodules are measured using man-

ual calipers in combination with film scales or

electronic calipers [26,78]. Schwartz et al [78]

reported that a semimanual autocontour method for

obtaining bidimensional measurements decreased

interobserver variation.

Volume measurement can be quantified using

two- or three-dimensional methods that can be man-

ual, semiautomated, or automated. Two-dimensional

methods require an assumption of a nodule’s shape.

The largest nodule dimension is converted into nod-

ule volume by assuming a spherical shape, or the

greatest dimension and its perpendicular dimension

are used to calculate volume with the presumption

that a nodule is an ellipse. Three-dimensional volume

measurement entails using the entire CT data set in

which the nodule is encoded to calculate nodule

volume. The superiority of three-dimensional meth-

Fig. 10. Nodule doubling. Illustration demonstrates how a doubling in volume for a nodule of smaller dimensions (top nodule) is

more difficult to discern in comparison with a nodule twice the diameter (bottom nodule).


ods was demonstrated by Yankelevitz et al [75],

particularly for deformed nonspherical nodules.

Three-dimensional methods measure the volume of

a nodule on each axial section and sum the volumes

to obtain total nodule volume and may account for

irregularly shaped nodules.

Nodule volume quantification has the potential to

detect smaller differences in nodule size at earlier

intervals than simply relying on cross-sectional

dimensions. There are, however, a number of

obstacles to performing automated or semiautomated

volume quantification. The major problem is the

reproducibility of volume measurements. Partial vol-

ume effects play a major role generating errors in

measurement. Threshold-based methods are fre-

quently used to separate or segment nodules from

the surrounding lung parenchyma. Voxel attenua-

tions above and below an attenuation demarcation

(or threshold value) are considered, respectively, as

nodule or lung parenchyma. If a nodule does not fill

an entire voxel, the nodule’s attenuation is averaged

with the surrounding lung parenchyma. Depending

on the threshold chosen, the voxel may or may not

be considered as part of the nodule and subsequently

affect the number of voxels determined to lie within

the nodule (Figs. 11, 12). Validation of these meth-

ods is important. There are two issues involved in

volume measurement. The first is how accurate or

close to the true volumes the system used can

measure volumes, sometimes termed bias; the sec-

ond is the reproducibility or precision in measure-

ment. Using synthetic nodules imaged in air and

two- and three-dimensional quantitative methods for

volume measurement, Yankelevitz et al [75] demon-

strated that 0.5-mm axial sections were associated

with smaller errors as compared with nodule volume

measurements performed on 1-mm sections [79]. It

is important to understand the error in measurement

methods so that identification of change in nodule

volume can be interpreted with knowledge of the

limitations of a measurement system, whether auto-

mated or semiautomated.

Certain factors make difficult the measurement of

nodule volume in patients. These include lung pathol-

ogy, such as emphysema; consolidation; or infiltrative

lung disease in addition to adjacent normal paren-

chymal structures, such as bronchi and vessels. Auto-

mated segmentation of nodules from vasculature

has been addressed recently by Zhao et al [79,80].

Three-dimensional volume measurement methods

may use two- and three-dimensional criteria for

segmentation [79,80]. Automated segmentation tech-

niques are difficult to validate, because there is no

gold standard for segmentation accuracy. Zhao et al

[79], however, demonstrated that a two-dimensional

multicriterion method for segmenting nodules from

adjacent structures did not significantly differ from

a radiologist’s segmentation (mean difference of

0.87 pixels). Additionally, their automated method

resulted in the same area for a given nodule section,

with a standard error of 0 pixels compared with the

4.80 pixel difference between areas obtained from

two separate segmentations by the radiologist.

Yankelevitz et al [75], when applying their quantita-

tive methods to 13 patients with nodules initially less

than 1 cm and a known diagnosis, demonstrated that

malignant nodules had a mean doubling time of

177 days as opposed to the 396 days for benign

Fig. 11. The effect of volume averaging on apparent size of a nodule: illustration of the voxel attenuations comprising a nodule.

The nodule when imaged using thin sections (left image) appears smaller when imaged using thicker sections (right image). The

voxels in the nodule periphery (arrows), which are more likely to be comprised of lung and nodule, are particularly susceptible to

volume averaging when section thickness increases, leading to a decrease in the overall voxel attenuation.


nodules. It is important also to mention that seg-

mentation of nodules from surrounding structures

may not be necessary, particularly if changes in

nodule volume are of interest [81].

Future developments

Computer-aided diagnosis

Given the number of factors that may affect

nodule detection, including interobserver and intra-

observer variation, nodule characteristics, imaging

technique, and viewing methods, computer-aided

diagnosis (CAD) may play a crucial role in ensuring

that abnormalities are not overlooked. This is of

particular interest when considering the immense size

of data sets generated by MSCT. The concept of

using CAD as a second reader began with screening

mammography; however, the use of such a tool can

be applied to a number of areas, particularly chest CT,

given its potential role in screening for lung cancer

and its frequent use to survey patients with known

malignancies. The overall goal of CAD is to identify

nodules as accurately as possible in a clinically timely

fashion. Use of CAD as a second reader may not only

decrease the number of missed nodules, but also

improve clinical efficiency. Volume and morphologic

analysis of nodules are also facilitated by computer-

ized techniques.

Computer-aided diagnosis for nodule detection

was initially applied to chest radiography and has

been supported by the development of digital chest

radiography [82–88]. In comparison with CT, lung

nodule perception on radiographs is more difficult

because of the lower contrast of a nodule with the

lung and superimposition of densities. The benefit of

CAD for assisting radiologists with radiographic

interpretation has been demonstrated, leading to

recent approval by the Food and Drug Administra-

tion. MacMahon [89] reported improved accuracy for

nodule diagnosis on chest radiographs with a group

of 146 chest radiologists, other radiologists, radiology

residents, and nonradiologists.

Interest in applying CAD to CT for nodule detec-

tion began a decade ago and has continued to

increase, particularly with the growing interest in

lung cancer screening [90–98]. To help radiologists,

CAD can analyze high-resolution CT data while the

radiologist analyzes more clinically practical thicker

sections. CAD programs need to accomplish a num-

ber of processes to succeed. Typically, the thorax is

identified within the FOV of an image, and then the

lungs are segmented from the thorax (Fig. 13).

Regions that may represent normal structures or

nodules in the lung are then identified and differ-

entiated. One major limitation has concerned iden-

tifying the demarcation of lung from the remaining

thorax, particularly when parenchymal abnormalities

abut the pleura. Methods to overcome this obstacle

have been proposed, including a ‘‘rolling-ball filter’’

[91] and comparing slopes at different points along

the lung border (see Fig. 13) [95].

The concept of CAD can be expanded to that of

an integrated computer system that supports nodule

identification, analysis of nodule size and morphol-

ogy, and database documentation and management

of the findings [99–102]. With computer-aided ana-

lytical techniques, one is more capable of studying

the internal architecture of nodules through texture

analysis [44,103]. McNitt-Gray et al [44] used texture

measures to identify nodules with uniform attenu-

ation from those with inhomogeneous attenuation.

This could be applied to noncontrast and contrast CT

studies of nodules. Integration of the results of

computer analysis with a database management sys-

tem may not only assist in daily clinical activities but

also provide indispensable data for research. For

example, associations between certain nodule char-

acteristics and the rate of malignancy may be

revealed and facilitated through the analysis of large

high-resolution MSCT data sets.

Image registration

The follow-up of pulmonary nodules emphasizes

the need for image registration techniques. Image

registration entails superimposing image data or

determining the spatial alignment between different

images from the same modality at different points in

time (intramodality registration) or between different

imaging modalities, such as CT, MR imaging, and

positron emission tomography (intermodality regis-

tration). To correlate a nodule accurately on a given

CT study with its matching counterpart on a sub-

sequent CT, global registration of the thorax and local

registration of nodules and smaller structures need to

be performed (Fig. 14).

A large number of reports concerning image

registration have been published, primarily in the

brain [104–107] and to a lesser degree in other organ

systems [108–111]. Within the chest, primary interest

has focused on image registration between nuclear

medicine studies, especially positron emission tomog-

raphy, which has low spatial resolution but pro-

vides functional or metabolic information, and CT

[112–114]. Study of registration of chest CT on

postprocessed CT data for virtual bronchoscopy has


Fig. 12. Threshold-based technique for nodule volume measurement. (A) High-resolution sections at time of initial (left image)

and follow-up study (right image) demonstrate nodule in the right lower lobe with growth. (B) Nodule volume calculated using

thresholds at time of initial study demonstrates different volumes (arrows) depending on threshold selected. (C) Similarly,

volume quantification performed on follow-up study demonstrates different volumes depending on threshold used. Both

thresholds, however, demonstrate interval growth of the nodule between initial and follow-up study. (Courtesy of Siemens

Corporate Research, Princeton, NJ.)


been performed [115]. More recently, because of the

recent attention focusing on screening CT and the

need to measure and compare nodule size better,

significant interest in comparing CT studies has

emerged [92,95].

Image registration techniques include rigid body,

affine, and elastic image methods [116]. On a CT

scan, translational differences may occur in the x, y,

or z position without rotation or distortion. Rota-

tional differences occur when the torso is rotated in

the axial plane (x, y rotation) and rotated out of

the axial plane (z rotation). Rigid body transfor-

mation methods account for these rotational and

translational differences. Image distortion, termed

skewing, from nonuniform image reconstruction or

changes in perspective, affects two-dimensional

radiographic images more than CT. Global skewing

can be introduced when different gantry tilts are

used, however, and is particularly relevant for head

CTs. Skewing is introduced as the patient exhales

and the thorax deforms. Global scaling factors are

related to the FOV and slice thickness on CT.

Affine transformations address differences in scaling

and skewing in addition to rigid body parameters

and globally represent the differences between two

data sets.

The lung is a deformable structure that differs in

shape and volume related to the degree of patient

inspiration. There is a difference in the amount of

lung deflation among different lobes and within the

same lobe. For example, when a patient exhales, the

lingula and right middle lobe do not increase in

attenuation as much as the dependent lower lobes

[117–119]. Webb et al [119] demonstrated that the

percent decrease in lung cross-sectional area was

greatest in the upper lung zones. Each lobe may have

a distinct deformation or strain pattern in response to

varying inspiratory volumes that may translate to

different shapes and attenuation on CT [120].

Deformable models are a potential area of investiga-

tion that may ultimately compensate for global and

local differences in thorax shape; however, deform-

Fig. 12 (continued ).


able models have been primarily studied in cardiac

models [121]. Additionally, pathology, such as ate-

lectasis, which can change the shape of the thorax and

shift anatomic structures, and any disease distorting

the normal contours of structures within the bony

thorax, could pose difficulties.

In the authors’ opinion, the ultimate goal is an

interactive system that enables easy identification of

corresponding structures on initial and subsequent CT

studies, recording of nodules and their characteristics

including volume, and storage of image results for

future analysis and documentation. This would prove

vital to the follow-up of a large number of nodules in

patients with a known malignancy affecting the lungs

and those patients with incidentally detected nodules.

Low-dose CT for lung cancer screening

In the early 1990s, interest arose in using chest CT

at lower radiation doses for both screening adults at

high risk for lung cancer and imaging pediatric

patients [14]. Although randomized controlled clin-

ical trials on screening chest radiography for lung

cancer were unable to demonstrate a decrease in

mortality for screened populations, chest CT, which

has higher spatial and contrast resolution, has been

proposed as an alternative screening method. Clinical

trials using low-dose chest CT for lung cancer

screening began with the Japanese [11,122–124].

In the United States, interest in screening began

with ELCAP [10,23] and continues with more re-

Fig. 13. Computer-aided diagnosis methodology for nodule detection: automated segmentation of the thorax and resultant

identification of candidate regions and nodule. (A) To identify the thorax within the field of view and select initiation points for

drawing of the lung border (stars), a chest CT image is downsampled and thresholded, and pixels are identified as black or white

(above or below a designated threshold value, respectively). (B) Illustration of a border correction method in which the lung

border has been drawn initially to exclude the nodule from the lung parenchyma. The border correction method compares slopes

at different points along the lung border, and the border is subsequently adjusted to include the nodule. (C) Axial CT image

demonstrates the lung border and candidate regions, some of which represent nodules (white) and vessels (black). (From Ko JP,

Betke M. Automated nodule detection and assessment of change over time: preliminary experience. Radiology 2001;218:267–

73; with permission.)


cent studies [9,12]. An 8-year randomized National

Lung Screening Trial under the auspices of the

American College of Radiology Imaging Network

is currently studying the utility of spiral CT for lung

cancer screening.

Regardless of whether performed for diagnostic or

screening purposes, there is no doubt that CT is more

sensitive than radiography for detecting nodules. The

ELCAP study screened 1000 asymptomatic patients

who were 60 years or greater in age and had at least

10 pack-years of cigarette smoking. CT demonstrated

233 (23%) of participants with one to six noncalcified

nodules, whereas chest radiography revealed nodules

in 7%. The prevalence of malignancy was 2.7% (27

of 1000) for the entire population using CT and 0.7%

using chest radiography. Moreover, 85% (23 of 27) of

the malignancies were diagnosed while still stage I

[10]. Furthermore, on follow-up incidence study,

ELCAP demonstrated a 2.5% (30 of 1184) positive

rate for new or growing nodules in their 1184 repeat

CT screenings [23].

A number of issues regarding lung cancer screen-

ing with CT need to be resolved. Foremost is the need

to assess disease-specific mortality. In this regard,

attention has focused particularly in the potential

overdiagnosis [125]. Additional concerns relate to

the large numbers of false-positive studies, especially

on initial prevalence baseline screenings. Clearly, a

method is needed to identify incidental benign nod-

ules without the need for invasive testing. While full

discussion of these and other issues pertaining to lung

cancer screening is outside the scope of this article, it

is worthwhile to note that, pending proved efficacy,

low-dose lung cancer screening has already provided

a rich source of data that will expand current under-

standing of the range and behavior of lung nodules

and the methods for quantitative and morphologic

nodule evaluation.

Fig. 14. CT registration results. A 1.25-mm MSCT data from two studies on a patient with nodules was registered. The wire

cages represent portions of the lung surfaces from an initial CT study. The nodules on initial (crosses) and follow-up studies

(squares) were correlated automatically by a computer system after global registration of the lungs. Note the offset in nodule

location between the two CT studies.


References

[1] Mitchell DM, Shah SH, Edwards D, et al. Incidence

of pulmonary nodules detected by computed tomog-

raphy in patients with bronchial carcinoma. Clin Ra-

diol 1986;37:151–2.

[2] Muhm JR, Brown LR, Crowe JK. Detection of pul-

monary nodules by computed tomography. AJR Am J

Roentgenol 1977;128:267–70.

[3] Remy-Jardin M, Remy J, Giraud F, et al. Pulmonary

nodules: detection with thick-section spiral CT versus

conventional CT. Radiology 1993;187:513–20.

[4] Austin JH, Muller NL, Friedman PJ, et al. Glossary of

terms for CT of the lungs: recommendations of the

Nomenclature Committee of the Fleischner Society.

Radiology 1996;200:327–31.

[5] Keagy BA, Starek PJ, Murray GF, et al. Major pul-

monary resection for suspected but unconfirmed ma-

lignancy. Ann Thorac Surg 1984;38:314–6.

[6] Siegelman SS, Khouri NF, Leo FP, et al. Solitary pul-

monary nodules: CT assessment. Radiology 1986;

160:307–12.

[7] Zerhouni EA, Stitik FP, Siegelman SS, et al. CT of the

pulmonary nodule: a cooperative study. Radiology

1986;160:319–27.

[8] Berger WG, Erly WK, Krupinski EA, et al. The soli-

tary pulmonary nodule on chest radiography: can we

really tell if the nodule is calcified? AJR Am J

Roentgenol 2001;176:201–4.

[9] Diederich S, Wormanns D, Semik M, et al. Screening

for early lung cancer with low-dose spiral CT: prev-

alence in 817 asymptomatic smokers. Radiology

2002;222:773–81.

[10] Henschke CI, McCauley DI, Yankelevitz DF, et al.

Early lung cancer action project: overall design and

findings from baseline screening. Lancet 1999;354:

99–105.

[11] Sone S, Li F, Yang ZG, et al. Results of three-year

mass screening programme for lung cancer using mo-

bile low-dose spiral computed tomography scanner.

Br J Cancer 2001;84:25–32.

[12] Swensen SJ, Jett JR, Sloan JA, et al. Screening for

lung cancer with low-dose spiral computed tomogra-

phy. Am J Respir Crit Care Med 2002;165:508–13.

[13] Klingenbeck-Regn K, Schaller S, Flohr T, et al. Sub-

second multi-slice computed tomography: basics and

applications. Eur J Radiol 1999;31:110–24.

[14] Naidich DP, Marshall CH, Gribbin C, et al. Low-dose

CT of the lungs: preliminary observations. Radiology

1990;175:729–31.

[15] Diederich S, Lenzen H. Radiation exposure associated

with imaging of the chest: comparison of different

radiographic and computed tomography techniques.

Cancer 2000;89:2457–60.

[16] Gies M, Kalender WA, Wolf H, et al. Dose reduction

in CT by anatomically adapted tube current mod-

ulation. I. Simulation studies. Med Phys 1999;26:

2235–47.

[17] Kalender WA, Wolf H, Suess C. Dose reduction in

CT by anatomically adapted tube current modulation.

II. Phantom measurements. Med Phys 1999;26:

2248–53.

[18] Mastora I, Remy-Jardin M, Suess C, et al. Dose re-

duction in spiral CT angiography of thoracic outlet

syndrome by anatomically adapted tube current mod-

ulation. Eur Radiol 2001;11:590–6.

[19] Diederich S, Semik M, Lentschig MG, et al. Helical

CT of pulmonary nodules in patients with extrathora-

cic malignancy: CT-surgical correlation. AJR Am J

Roentgenol 1999;172:353–60.

[20] Kakinuma R, Ohmatsu H, Kaneko M, et al. Detection

failures in spiral CT screening for lung cancer: anal-

ysis of CT findings. Radiology 1999;212:61–6.

[21] Tsubamoto M, Kuriyama K, Kido S, et al. Detection

of lung cancer on chest radiographs: analysis on the

basis of size and extent of ground-glass opacity at

thin-section CT. Radiology 2002;224:139–44.

[22] White CS, Romney BM, Mason AC, et al. Primary

carcinoma of the lung overlooked at CT: analysis of

findings in 14 patients. Radiology 1996;199:109–15.

[23] Henschke CI, Naidich DP, Yankelevitz DF, et al.

Early lung cancer action project: initial findings on

repeat screenings. Cancer 2001;1:153–9.

[24] Gruden JF, Ouanounou S, Tigges S, et al. Incremental

benefit of maximum-intensity-projection images on

observer detection of small pulmonary nodules re-

vealed by multidetector CT. AJR Am J Roentgenol

2002;179:149–57.

[25] Rusinek H, Naidich DP, McGuinness G, et al. Pulmo-

nary nodule detection: low-dose versus conventional

CT. Radiology 1998;209:243–9.

[26] Wormanns D, Diederich S, Lentschig MG, et al. Spi-

ral CT of pulmonary nodules: interobserver variation

in assessment of lesion size. Eur Radiol 2000;10:

710–3.

[27] Buckley JA, Scott Jr. WW, Siegelman SS, et al. Pul-

monary nodules: effect of increased data sampling on

detection with spiral CT and confidence in diagnosis.

Radiology 1995;196:395–400.

[28] Kalender WA, Polacin A, Suss C. A comparison of

conventional and spiral CT: an experimental study on

the detection of spherical lesions. J Comput Assist

Tomogr 1994;18:167–76.

[29] Wright AR, Collie DA, Williams JR, et al. Pulmo-

nary nodules: effect on detection of spiral CT pitch

[see comments]. Radiology 1996;199:837–41.

[30] Brink JA, Heiken JP, Balfe DM, et al. Spiral CT:

decreased spatial resolution in vivo due to broadening

of section-sensitivity profile. Radiology 1992;185:

469–74.

[31] Diederich S, Lentschig MG, Winter F, et al. Detection

of pulmonary nodules with overlapping vs non-over-

lapping image reconstruction at spiral CT. Eur Radiol

1999;9:281–6.

[32] Gartenschlager M, Schweden F, Gast K, et al. Pulmo-

nary nodules: detection with low-dose vs convention-

al-dose spiral CT. Eur Radiol 1998;8:609–14.

[33] Seltzer SE, Judy PF, Feldman U, et al. Influence of


CT image size and format on accuracy of lung nodule

detection. Radiology 1998;206:617–22.

[34] Tillich M, Kammerhuber F, Reittner P, et al. Detec-

tion of pulmonary nodules with helical CT: compar-

ison of cine and film-based viewing. AJR Am J

Roentgenol 1997;169:1611–4.

[35] Diederich S, Lentschig MG, Overbeck TR, Wormanns

D, Heindel W. Detection of pulmonary nodules at spi-

ral CT: comparison of maximal intensity projection

sliding slabs and single-image reporting. Eur Radiol

2001;11:1345–50.

[36] Coakley FV, Cohen MD, Johnson MS, et al. Maxi-

mum intensity projection images in the detection of

simulated pulmonary nodules by spiral CT. Br J Ra-

diol 1998;71:135–40.

[37] Napel S, Rubin GD, Jeffrey Jr. RB. STS-MIP: a new

reconstruction technique for CT of the chest. J Com-

put Assist Tomogr 1993;17:832–8.

[38] Calhoun PS, Kuszyk BS, Heath DG, et al. Three-

dimensional volume rendering of spiral CT data:

theory and method. Radiographics 1999;19:745–64.

[39] Paranjpe DV, Bergin CJ. Spiral CT of the lungs:

optimal technique and resolution compared with

conventional CT. AJR Am J Roentgenol 1994;162:

561–7.

[40] Ketai L, Malby M, Jordan K, et al. Small nodules

detected on chest radiography: does size predict cal-

cification? Chest 2000;118:610–4.

[41] Collins VP, Loeffler RK, Tivey H. Observations on

growth rates of human tumors. AJR Am J Roentgenol

1956;76:988–1000.

[42] Nathan MH, Collins VP, Adams RA. Growth rates in

benign and malignant pulmonary nodules. Radiology

1962;79:221–31.

[43] Steele JD, Buell P. Asymptomatic solitary pulmonary

nodules: host survival, tumor size, and growth rate.

J Thorac Cardiovasc Surg 1973;65:140–51.

[44] McNitt-Gray MF, Hart EM, Wyckoff N, et al. A pat-

tern classification approach to characterizing solitary

pulmonary nodules imaged on high resolution CT:

preliminary results. Med Phys 1999;26:880–8.

[45] Milne EN, Zerhouni EA. Blood supply of pulmonary

metastases. J Thorac Imaging 1987;2:15–23.

[46] Breatnach E, Stanley RJ. CT diagnosis of segmental

pulmonary artery embolus. J Comput Assist Tomogr

1984;8:762–4.

[47] Greaves SM, Hart EM, Brown K, et al. Pulmonary

thromboembolism: spectrum of findings on CT. AJR

Am J Roentgenol 1995;165:1359–63.

[48] Mirtcheva RM, Vazquez M, Yankelevitz DF, et al.

Bronchioloalveolar carcinoma and adenocarci-

noma with bronchioloalveolar features presenting as

ground-glass opacities on CT. Clin Imaging 2002;

26:95–100.

[49] Zwirewich CV, Vedal S, Miller RR, et al. Solitary

pulmonary nodule: high-resolution CT and radio-

logic-pathologic correlation. Radiology 1991;179:

469–76.

[50] Kuhlman JE, Fishman EK, Siegelman SS. Invasive

pulmonary aspergillosis in acute leukemia: character-

istic findings on CT, the CT halo sign, and the role of

CT in early diagnosis. Radiology 1985;157:611–4.

[51] Henschke CI, Yankelevitz DF, Mirtcheva R, et al. CT

screening for lung cancer: frequency and significance

of part-solid and nonsolid nodules. AJR Am J Roent-

genol 2002;178:1053–7.

[52] Noguchi M, Shimosato Y. The development and pro-

gression of adenocarcinoma of the lung. Cancer Treat

Res 1995;72:131–42.

[53] Gaeta M, Caruso R, Barone M, et al. Ground-glass

attenuation in nodular bronchioloalveolar carcinoma:

CT patterns and prognostic value. J Comput Assist

Tomogr 1998;22:215–9.

[54] Aoki T, Nakata H, Watanabe H, et al. Evolution of

peripheral lung adenocarcinomas: CT findings corre-

lated with histology and tumor doubling time. AJR

Am J Roentgenol 2000;174:763–8.

[55] Kim EA, Johkoh T, Lee KS, et al. Quantification of

ground-glass opacity on high-resolution CT of small

peripheral adenocarcinoma of the lung: pathologic

and prognostic implications. AJR Am J Roentgenol

2001;177:1417–22.

[56] Greenberg AK, Yee H, Rom WN. Preneoplastic le-

sions of the lung. Respir Res 2002;3:20–9.

[57] Westra WH. Early glandular neoplasia of the lung.

Respir Res 2000;1:163–9.

[58] Kitamura H, Kameda Y, Nakamura N, et al. Atypical

adenomatous hyperplasia and bronchoalveolar lung

carcinoma: analysis by morphometry and the expres-

sions of p53 and carcinoembryonic antigen. Am J

Surg Pathol 1996;20:553–62.

[59] Keith RL, Miller YE, Gemmill RM, et al. Angiogenic

squamous dysplasia in bronchi of individuals at

high risk for lung cancer. Clin Cancer Res 2000;6:

1616–25.

[60] Aloia T, Bepler G, Harpole D, et al. Integration of

peripheral blood biomarkers with computed tomogra-

phy to differentiate benign from malignant pulmonary

opacities. Cancer Detect Prev 2001;25:336–43.

[61] Franklin WA. Diagnosis of lung cancer: pathology of

invasive and preinvasive neoplasia. Chest 2000;117:

80S–9S.

[62] Kitamura H, Kameda Y, Nakamura N, et al. Prolifer-

ative potential and p53 overexpression in precursor

and early stage lesions of bronchioloalveolar lung

carcinoma. Am J Pathol 1995;146:876–87.

[63] Kumaki F, Matsui K, Kawai T, et al. Expression of

matrix metalloproteinases in invasive pulmonary ad-

enocarcinoma with bronchioloalveolar component

and atypical adenomatous hyperplasia. Am J Pathol

2001;159:2125–35.

[64] Kawakami S, Sone S, Takashima S, et al. Atypical

adenomatous hyperplasia of the lung: correlation be-

tween high-resolution CT findings and histopatho-

logic features. Eur Radiol 2001;11:811–4.

[65] O’Keefe ME, Good CA, McDonald JR. Calcification

in solitary nodules of the lung. AJR Am J Roentgenol

1957;77:1023–33.


[66] Webb WR. Radiologic evaluation of the solitary pul-

monary nodule. AJR Am J Roentgenol 1990;154:

701–8.

[67] Siegelman SS, Zerhouni EA, Leo FP, et al. CT of the

solitary pulmonary nodule. AJR Am J Roentgenol

1980;135:1–13.

[68] McCullough EC, Morin RL. CT-number variability in

thoracic geometry. AJR Am J Roentgenol 1983;141:

135–40.

[69] Zerhouni EA, Spivey JF, Morgan RH, et al. Factors

influencing quantitative CT measurements of solitary

pulmonary nodules. J Comput Assist Tomogr 1982;

6:1075–87.

[70] Zerhouni EA, Boukadoum M, Siddiky MA, et al. A

standard phantom for quantitative CT analysis of pul-

monary nodules. Radiology 1983;149:767–73.

[71] Littleton JT, Durizch ML, Moeller G, et al. Pulmo-

nary masses: contrast enhancement. Radiology 1990;

177:861–71.

[72] Swensen SJ, Morin RL, Schueler BA, et al. Solitary

pulmonary nodule: CT evaluation of enhancement

with iodinated contrast material–a preliminary report.

Radiology 1992;182:343–7.

[73] Swensen SJ, Viggiano RW, Midthun DE, et al. Lung

nodule enhancement at CT: multicenter study. Radi-

ology 2000;214:73–80.

[74] Lillington GA. The solitary pulmonary nodule–1974.

Am Rev Respir Dis 1974;110:699–707.

[75] Yankelevitz DF, Reeves AP, Kostis WJ, et al. Small

pulmonary nodules: volumetrically determined

growth rates based on CT evaluation. Radiology

2000;217:251–6.

[76] Yankelevitz DF, Henschke CI. Does 2-year stability

imply that pulmonary nodules are benign?AJR Am J

Roentgenol 1997;168:325–8.

[77] Therasse P, Arbuck SG, Eisenhauer EA, et al. New

guidelines to evaluate the response to treatment in

solid tumors. European Organization for Research

and Treatment of Cancer, National Cancer Institute

of the United States, National Cancer Institute of

Canada. J Natl Cancer Inst 2000;92:205–16.

[78] Schwartz LH, Ginsberg MS, DeCorato D, et al. Eval-

uation of tumor measurements in oncology: use of

film-based and electronic techniques. J Clin Oncol

2000;18:2179–84.

[79] Zhao B, Yankelevitz D, Reeves A, et al. Two-dimen-

sional multi-criterion segmentation of pulmonary

nodules on helical CT images. Med Phys 1999;26:

889–95.

[80] Zhao B, Reeves AP, Yankelevitz DF, et al. Three-

dimensional multicriterion automatic segmentation

of pulmonary nodules of helical computed tomogra-

phy images. Opt Eng 1999;38:1340–7.

[81] Ko JP, Rusinek H, Jacobs EL, et al. Volume quanti-

tation of small pulmonary nodules on low dose chest

CT: a phantom study. In: Radiological Society of

North America 87th Scientific Assembly and Annual

Meeting. Radiology 2001;221(P):312.

[82] Abe K, Doi K, MacMahon H, et al. Computer-aided

diagnosis in chest radiography: preliminary experi-

ence. Invest Radiol 1993;28:987–93.

[83] Lampeter WA, Wandtke JC. Computerized search of

chest radiographs for nodules. Invest Radiol 1986;21:

384–90.

[84] MacMahon H, Doi K, Chan HP, et al. Computer-

aided diagnosis in chest radiology. J Thorac Imaging

1990;5:67–76.

[85] Matsumoto T, Yoshimura H, Doi K, et al. Image fea-

ture analysis of false-positive diagnoses produced by

automated detection of lung nodules. Invest Radiol

1992;27:587–97.

[86] Matsumoto T, Yoshimura H, Giger ML, et al. Poten-

tial usefulness of computerized nodule detection in

screening programs for lung cancer. Invest Radiol

1992;27:471–5.

[87] Wu YC, Doi K, Giger ML, et al. Reduction of false

positives in computerized detection of lung nodules in

chest radiographs using artificial neural networks, dis-

criminant analysis, and a rule-based scheme. J Digit

Imaging 1994;7:196–207.

[88] Yoshimura H, Giger ML, Doi K, et al. Computerized

scheme for the detection of pulmonary nodules: a

nonlinear filtering technique. Invest Radiol 1992;27:

124–9.

[89] MacMahon H, Engelmann R, Behlen FM, Ishida T,

Roe C, Metz CE, et al. Computer-aided diagnosis of

pulmonary nodules: results of a large-scale observer

test. Radiology 1999;213:723–6.

[90] Armato III SG, Giger ML, MacMahon H. Automated

detection of lung nodules in CT scans: preliminary

results. Med Phys 2001;28:1552–61.

[91] Armato III SG, Giger ML, Moran CJ, et al. Compu-

terized detection of pulmonary nodules on CT scans.

Radiographics 1999;19:1303–11.

[92] Brown MS, McNitt-Gray MF, Goldin JG, et al. Pa-

tient-specific models for lung nodule detection and

surveillance in CT images. IEEE Trans Med Imaging

2001;20:1242–50.

[93] Brown MS, McNitt-Gray MF, Mankovich NJ, et al.

Method for segmenting chest CT image data using an

anatomical model: preliminary results. IEEE Trans

Med Imaging 1997;16:828–39.

[94] Kanazawa K, Kawata Y, Niki N, et al. Computer-

aided diagnosis for pulmonary nodules based on heli-

cal CT images. Comput Med Imaging Graph 1998;

22:157–67.

[95] Ko JP, Betke M. Chest CT: automated nodule detec-

tion and assessment of change over time-preliminary

experience. Radiology 2001;218:267–73.

[96] Reeves AP, Kostis WJ. Computer-aided diagnosis

for lung cancer. Radiol Clin North Am 2000;38:

497–509.

[97] Lee Y, Hara T, Fujita H, et al. Automated detection of

pulmonary nodules in helical CT images based on an

improved template-matching technique. IEEE Trans

Med Imaging 2001;20:595–604.

[98] Wormanns D, Fiebich M, Saidi M, et al. Automatic

detection of pulmonary nodules at spiral CT: clinical


application of a computer-aided diagnosis system.

Eur Radiol 2002;12:1052–7.

[99] Ankerst M, Kastenmuller G, Kriegel HP, et al. 3D

shape histograms for similarity search and classifica-

tion in spatial databases. Advances in Spatial Data-

bases 1999;1651:207–26.

[100] Kuo WJ, Chang RF, Chen DR, et al. Data mining

with decision trees for diagnosis of breast tumor in

medical ultrasonic images. Breast Cancer Res Treat

2001;66:51–7.

[101] Kusiak A, Kern JA, Kernstine KH, et al. Autonomous

decision-making: a data mining approach. IEEE Trans

Inf Technol Biomed 2000;4:274–84.

[102] Megalooikonomou V, Ford J, Shen L, et al. Data

mining in brain imaging. Stat Methods Med Res

2000;9:359–94.

[103] Tourassi GD. Journey toward computer-aided diagno-

sis: role of image texture analysis. Radiology 1999;

213:317–20.

[104] Grimson WEL, Ettinger GJ, White SJ, et al. An au-

tomatic registration method for frameless stereotaxy,

image guided surgery, and enhanced reality visualiza-

tion. IEEE Trans Med Imaging 1996;15:129–40.

[105] Malison RT, Miller EG, Greene R, et al. Computer-

assisted coregistration of multislice spect and MR

brain images by fixed external fiducials. J Comput

Assist Tomogr 1993;17:952–60.

[106] Pelizzari CA. Image processing in stereotactic plan-

ning: volume visualization and image registration.

Med Dosim 1998;23:137–45.

[107] Pelizzari CA, Chen GT, Spelbring DR, et al. Accurate

three-dimensional registration of CT, PET, and/or MR

images of the brain. J Comput Assist Tomogr 1989;

13:20–6.

[108] Fei BW, Wheaton A, Lee ZH, et al. Automatic MR

volume registration and its evaluation for the pelvis

and prostate. Phys Med Biol 2002;47:823–38.

[109] Kerrien E, Berger MO, Maurincomme E, et al. Fully

automatic 3D/2D subtracted angiography registration.

Medical Image Computing and Computer-Assisted

Intervention, MICCAI’99. Proceedings 1999;1679:

664–71.

[110] Shekhar R, Zagrodsky V. Mutual information-based

rigid and nonrigid registration of ultrasound volumes.

IEEE Trans Med Imaging 2002;21:9–22.

[111] Wilson DL, Carrillo A, Zheng L, et al. Evaluation of

3D image registration as applied to MR-guided ther-

mal treatment of liver cancer. J Magn Reson Imaging

1998;8:77–84.

[112] Aitken CL, Mahmoud F, McGuinness G, et al. Tumor

localization and image registration of F-18FDG coin-

cidence detection scans with computed tomographic

scans. Clin Nucl Med 2002;27:275–82.

[113] Aquino SL, Asmuth JC, Moore RH, et al. Improved

image interpretation with registered thoracic CT and

positron emission tomography data sets. AJR Am J

Roentgenol 2002;178:939–44.

[114] Ketai L, Hartshorne M. Potential uses of computed

tomography-SPECT and computed tomography-coin-

cidence fusion images of the chest. Clin Nucl Med

2001;26:433–41.

[115] Solomon SB, White P, Wiener CM, et al. Three-

dimensional CT-guided bronchoscopy with a real-

time electromagnetic position sensor: a comparison

of two image registration methods. Chest 2000;118:

1783–7.

[116] Weaver JB, Healy Jr. DM, Periaswamy S, et al. Elas-

tic image registration using correlations. J Digit Imag-

ing 1998;11:59–65.

[117] Stern EJ, Webb WR. Dynamic imaging of lung mor-

phology with ultrafast high-resolution computed to-

mography. J Thorac Imaging 1993;8:273–82.

[118] Verschakelen JA, Van Fraeyenhoven L, Laureys G,

et al. Differences in CT density between dependent

and nondependent portions of the lung: influence

of lung volume. AJR Am J Roentgenol 1993;161:

713–7.

[119] Webb WR, Stern EJ, Kanth N, et al. Dynamic pulmo-

nary CT: findings in healthy adult men. Radiology

1993;186:117–24.

[120] Napadow VJ, Mai V, Bankier A, et al. Determination

of regional pulmonary parenchymal strain during nor-

mal respiration using spin inversion tagged mag-

netization MRI. J Magn Reson Imaging 2001;13:

467–74.

[121] Haber I, Metaxas DN, Axel L. Three-dimensional

motion reconstruction and analysis of the right ven-

tricle using tagged MRI. Med Image Anal 2000;4:

335–55.

[122] Kaneko M, Eguchi K, Ohmatsu H, et al. Peripheral

lung cancer: screening and detection with low-dose

spiral CT versus radiography. Radiology 1996;201:

798–802.

[123] Kaneko M, Kusumoto M, Kobayashi T, et al. Com-

puted tomography screening for lung carcinoma in

Japan. Cancer 2000;89:2485–8.

[124] Sone S, Takashima S, Li F, et al. Mass screening for

lung cancer with mobile spiral computed tomography

scanner. Lancet 1998;351:1242–5.

[125] Patz Jr. EF, Black WC, Goodman PC. CT screening

for lung cancer: not ready for routine practice. Radi-

ology 2001;221:587–91.


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Lung Nodule Detection & Character is at Ion on Multi Slice CT