Running title: Key words Disclosure 30 · 20 Here we present a unique fiber-optic biopsy needle (FOBN) platform that uses Diffuse 21 Reflectance Spectroscopy (DRS) in conjunction

1

Title: Real-time in vivo tissue characterization with diffuse reflectance spectroscopy 1 during transthoracic lung biopsy: a clinical feasibility study 2

Authors: Jarich W. Spliethoffa, Warner Prevoob, Mark A. J. Meierb, Jeroen de Jongc, Daniel J. 3 Eversa, Hendricus J. C. M. Sterenborga,d, Gerald W. Lucassene, Benno H. W. Hendrikse, 4 Theodoor J. M. Ruersa,f 5

Affiliations: 6 a) Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX, 7 Amsterdam, Netherlands. 8 b) Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX, 9 Amsterdam, Netherlands. 10 c) Department of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066CX, 11 Amsterdam, Netherlands. 12 d) Department of Biomedical Engineering and Physics, Academic Medical Centre, 13 Meibergdreef 9, 1105AZ, Amsterdam, Netherlands. 14 e) Minimally Invasive Healthcare department, Philips Research, High Tech Campus 34, 15 5656AE, Eindhoven, Netherlands. 16 f) MIRA institute, University of Twente, Building Zuidhorst, Room ZH 116, Enschede, 17 Netherlands 18 19

Running title: Biopsy guidance by diffuse reflectance spectroscopy 20

Key words: Lung cancer, biopsy, real-time guidance, tissue diagnosis, optical spectroscopy 21 22

Disclosure: This study was sponsored in part by Philips Research, Eindhoven, the 23 Netherlands. The authors declare no competing financial interests. The prototype system 24 described in this article is currently only a research prototype and is not for commercial 25 use. J.W.S., B.H.W.H., and G.W.L., are inventors for an international patent 26 PCT/IB2013/059712, “System with photonic biopsy device for obtaining pathological 27 information” filed by Koninklijke Philips N.V. and Philips Deutschland Gmbh. 28

29

30

Research. on March 29, 2020. © 2015 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on August 31, 2015; DOI: 10.1158/1078-0432.CCR-15-0807

http://clincancerres.aacrjournals.org/

2

Word count manuscript: 3297 1

Number of figures: 6 2

Number of tables: 1 3

Number of supplementary figures (not included in this file): 0 4




3

Abstract 1 Purpose. This study presents the first in vivo real-time tissue characterization during 2 image-guided percutaneous lung biopsies using diffuse reflectance spectroscopy (DRS) 3 sensing at the tip of a biopsy needle with integrated optical fibers. 4 Experimental design. Tissues from 21 consented patients undergoing lung cancer surgery 5 were measured intraoperatively using the fiber-optic platform capable of assessing various 6 physical tissue properties highly correlated to tissue architecture and composition. 7 Additionally, the method was tested for clinical use by performing DRS tissue sensing 8 during 11 routine biopsy procedures in patients with suspected lung cancer. 9 Results. We found that water content and scattering amplitude are the primary 10 discriminators for the transition from healthy lung tissue to tumor tissue and that the 11 reliability of these parameters is not affected by the amount of blood at the needle tip. In 12 the 21 patients measured intraoperatively, the water-to-scattering ratio yielded a 56% to 13 81% contrast difference between tumor and surrounding tissue. Analysis of the 11 image-14 guided lung biopsy procedures showed that the tissue diagnosis derived from DRS was 15 diagnostically discriminant in each clinical case. 16 Conclusions. DRS tissue sensing integrated into a biopsy needle may be a powerful new 17 tool for biopsy guidance that can be readily used in routine diagnostic lung biopsy 18 procedures. This approach may not only help to increase the successful biopsy yield for 19 histopathological analysis, but may also allow specific sampling of vital tumor tissue for 20 genetic profiling. 21

22




4

Translational Relevance 1 Advances in molecular biology are improving the understanding of lung cancer and 2 directing clinical decision making. Consequently, highly representative tissue samples for 3 histologic characterization and mutation analysis are increasingly important. Although 4 diagnostic needle biopsy is widely used, this procedure suffers from significant non-5 diagnostic sampling rates, especially when small masses are targeted. Here we present an 6 innovative technology platform for spectral tissue sensing at the tip of a biopsy needle. By 7 providing the radiologist with real-time needle guidance, the diagnostic performance and 8 the quality of tumor biopsies could significantly be enhanced. 9

10

11

12




5

Introduction 1 Image guided transthoracic needle biopsy (TNB) of the lung is a well-established method 2 used for the diagnosis of lung cancer. TNB is particularly useful for peripheral pulmonary 3 lesions that are not readily accessible with bronchoscopy. With the introduction of lung 4 cancer screening programs (1, 2) lung cancers will be detected at earlier stages and at 5 smaller sizes. As sampling a small pulmonary lesion is often technically challenging (3), the 6 ability to successfully biopsy these lesions depends greatly on the skill of the physician. 7 During the procedure multiple needle insertions should be avoided to reduce the 8 possibility of complications, specifically pneumothorax or hemorrhage. To this end, TNB 9 procedures are generally performed under fluoroscopic or computed tomographic 10 guidance. Still, the pathologic area is often missed or undersampled, leading to diagnostic 11 failure rates of TNB in up to 23% (3-6) of the cases. A considerable number of patients 12 undergoing TNB will subsequently require an additional biopsy procedure, leading to extra 13 patient discomfort, prolonged psychological burden, and additional risks or complications. 14 Moreover, due to advances in genetic profiling and personalized medicine, obtaining 15 representative tissue samples that allow for both histologic characterization and 16 mutational analysis is becoming increasingly important (7, 8). Therefore, with advances in 17 detection of increasingly smaller pulmonary lesions and recent developments in molecular 18 profiling, there is a clear call for biopsy guidance to optimize tissue sample acquisition. 19 Here we present a unique fiber-optic biopsy needle (FOBN) platform that uses Diffuse 20 Reflectance Spectroscopy (DRS) in conjunction with a conventional biopsy needle. DRS is a 21 spectroscopic technique in which tissue is illuminated with a selected spectral band of 22 light. The light is either scattered or absorbed by the tissue, depending on the specific 23 composition of the tissue. Subsequent analysis of the tissue’s spectral response provides 24 specific quantitative morphologic, biochemical, and functional information, thereby 25 enabling tissue discrimination and potentially improving diagnostic capability. Several 26 preclinical and clinical studies have demonstrated the potential use of DRS for cancer 27 detection and diagnosis for a variety of tissue sites (9-18). Despite the proven potential of 28 DRS (19, 20), clinical translation involves a variety of major challenges, many of which are 29 unique to this stage of technology development. 30




6

To enable robust data acquisition under specific operating constraints in the clinic, 1 essential hurdles must be overcome. First, a major obstacle in the successful 2 implementation of DRS-based needle guidance is the inevitable presence of blood around 3 the needle tip which absorbs a significant amount of light and in that way decreases the 4 quality of the signal. This effect should be reduced to obtain reliable tissue characterization 5 during percutaneous interventions in well-perfused tissue. Second, real-time spectral 6 tissue sensing should be robust and small enough to be combined with biopsy 7 functionalities in the same clinical-grade instrument. Third, the developed technology 8 should fit seamlessly in the clinical workflow for obtaining a TNB. We report on the 9 development of the DRS-FOBN platform and its first application in patients undergoing 10 transthoracic needle biopsy for suspected lung cancer. 11

12

Materials and Methods 13

Clinical studies 14 Protocols for the human studies were reviewed and approved by the Medical Review Ethics 15 Committee of the Netherlands Cancer Institute/ Antoni van Leeuwenhoek hospital. The 16 clinical protocols were registered at the Dutch Trial Register (NTR2557; NTR3651) and the 17 U.S. National Institutes of Health Clinical Trial Database (NCT01730365). All patients gave 18 their written informed consent prior to the experimental procedures. All clinical 19 experiments were conducted in the NKI-AVL hospital. 20

Portable spectroscopy system 21 The advantage of our technique relative to those presented in most previous studies is that 22 the narrow wavelength range commonly used in DRS (typically between 400-900 nm) was 23 extended into the near-infrared region up to 1600 nm (21, 22) where blood has no 24 significant absorption features. The main benefit of this feature is that it helps to overcome 25 the effect of dominant absorption by excessive amounts of hemoglobin in the visible 26 wavelength region (400-700 nm).(22) Furthermore, it enables the quantification of water 27 content which is an important measure for lung tissue density.(19, 21, 22) The general 28 principles of DRS, the operating features of the spectroscopy system, and the calibration 29 procedure have been described elaborately by Nachabe et al. (21, 22). The system consists 30




7

of a Tungsten halogen broadband light source (360–2500 nm) with an embedded shutter, a 1 miniaturized optical probe and two spectrometers: one which resolves the light in the 2 visible wavelength range, i.e. 400 up to 1100 nm (Andor Technology, DU420ABRDD) and 3 one which resolves near infrared light from 900 up to 1700 nm (Andor Technology, 4 DU492A-1.7). The spectroscopy needle was connected to the light source and to both 5 spectrometers via low-OH optical fibers. The spectrometers are controlled by a custom 6 LabView software (National Instruments, Austin, TX) to acquire and calibrate the data. 7 8

Needles with sensing capabilities 9

Needle for intraoperative use. A disposable 15G spectroscopy needle (Invivo Germany, 10 Schwerin, Germany) was developed for practical use during surgery. The needle contained 11 three fibers, each with a core diameter of 200 μm. One fiber was connected to the light 12 source, while the other two fibers were connected to the spectrometers to capture the 13 diffusely reflected light from the tissue. The center-to-center distance between the emitting 14 and collecting fibers was 1.70 mm. 15 16

Fiber-optic biopsy needle. Adding DRS tissue sensing functionality to an automated biopsy 17 gun is challenging, as fast shooting mechanisms and the presence of a notch set strict 18 constraints for integrating the optical fibers within the device. For the measurements 19 during TNB procedures, a sterile single use fully-automated 16G FOBN needle was 20 developed (Invivo Germany, Schwerin, Germany) to take soft-tissue biopsies under image 21 guidance. An essential feature of this clinical-grade instrument is that it allows tissue 22 sampling from the exact location where the final tissue sensing took place without 23 restricting the usability of the biopsy gun (Fig. 1). A 100 μm diameter fiber was used for 24 light delivery, whereas two 200 μm diameter fibers were used for the collection of the 25 reflected light. The distance between the emitting and collecting fibers at the needle tip was 26 1.36 mm, resulting in a tissue probing depth of approximately 1 - 2 mm. 27 28

Study procedures 29

Intraoperative data acquisition. The portable DRS system was installed in a general 30 surgery operating room. DRS measurements were performed in 21 patients undergoing 31




8

partial lung resection. DRS measurements were performed after deflation of the lung, but 1 before any tissue dissection or ligation of major blood vessels. A sterile single use fiber-2 optic needle was inserted into the tissue that was planned for resection, using a 14G hollow 3 guidance cannula (Invivo Germany, Schwerin, Germany). For each patient two sets of 5 - 10 4 DRS spectra were recorded in healthy lung tissue and tumor tissue. Measurement sites 5 were marked with twist coil markers (OTM 3.0SA, Biomed.-Instrumente & Producte GmBh, 6 Tuerkenfeld, Germany) that were placed through the guidance cannula. Within 10 minutes 7 after resection, the tissue was measured ex vivo with the same setup to allow for the 8 comparison of in vivo and ex vivo spectra. The resected was processed by the pathologist 9 and tissue samples were taken from the measurement locations, as indicated by the twist 10 markers. The samples underwent detailed histopathological and findings were compared 11 to the results of the DRS spectral analysis. 12 13

Image-guided biopsy with fiber-optic biopsy needle. The FOBN was tested in a computed 14 tomography (CT) intervention room during 11 routine transthoracic needle biopsy 15 procedures for individuals with a suspicious pulmonary lesion. All patients underwent a 16 free-breathing CT-scan (16-slice Somatom Sensation Open, Siemens, Erlangen, Germany) as 17 part of the standard procedure planning. The FOBN was inserted at the planned entry point 18 and DRS measurements were performed along the needle tract, followed by DRS 19 measurements and biopsy of the target lesion using the same needle. CT-fluoroscopy 20 imaging was recorded and co-registered with the DRS spectra. For each patient, sets of 3-5 21 reflectance spectra were acquired from healthy lung tissue, tissue at the tumor border, and 22 tumor tissue. The radiologist was blinded to the DRS system output. Directly after 23 acquisition of the final DRS data, a tissue sample was taken from the target lesion using the 24 FOBN. The distal end of the tissue samples was marked with yellow tissue marking dye 25 (Polysciences Inc., Warrington, United Kingdom) for orientation purposes. The samples 26 were formalin-fixed and processed according to routine histopathology. Pathology results 27 were compared with the DRS data at the final measurement position. 28

29

30

31




9

Histology processing and analysis 1 Tissue samples were processed via standard histological procedures. After paraffin 2 embedding, the samples were sectioned and stained with standard hematoxylin and eosin 3 dye (Merck, Darmstadt, Germany) (HE). The resulting tissue slices were examined with a 4 light microscope by an experienced pathologist who was blinded to the spectroscopic 5 analysis. The glass slides were digitized by a histologic slide scanner (ScanScope - Aperio 6 Technologies Inc., Vista, California). 7 8

Spectral data analysis 9 DRS measurements were spectrally fitted with an analytical model by Farrell et al. (23) that 10 is derived from the diffusion theory using a Levenberg–Marquardt nonlinear inversion 11 algorithm to determine the absorption coefficient μa (λ) and the reduced scattering 12 coefficient μs (λ) expressed in cm−1. The validation of the model, including spectral 13 calibration procedures, and its application in various preclinical studies were described in 14 detail elsewhere. (12, 13, 19, 20) The model uses prior knowledge of light-tissue 15 interaction to translate the acquired spectra into estimates of various absorption and 16 scattering parameters, such as biological volume fractions (e.g. blood, water), oxygenation 17 level of blood and light scattering related to cell density, cell size, or air. Confidence 18 intervals of the estimated parameters derived from the covariance matrix were calculated 19 to investigate the reliability of measurements fits.(22) 20 To verify the performance of the FOBN per individual, we extracted the information 21 provided by the FOBN along each needle path and used each patient’s healthy tissue as an 22 internal reference. This was done by calculating an optical contrast index (OCI). The OCI 23 was defined as the relative difference in the water-to-scattering ratio between tumor and 24 lung tissue within the same individual. Similar composite optical parameters have been 25 applied by other research groups (9, 24, 25).The OCI was calculated using the simple 26 formula: 27 28

Normals

Tumors

waterwaterOCI

)]800([)]800([

'

'

μμ

= , 29




10

1 where Tumorswater )]800([ 'μ and Normalswater )]800([ 'μ correspond to the average water-to-2 scattering ratio measured in tumor tissue and surrounding normal lung tissue, 3 respectively. 4 5

Statistics 6 Tissue parameters determined from DRS spectral measurements (blood, stO2, water, 7 µs’(800)) were compared between tumor tissue and normal tissue using a generalized 8 estimating equations (GEEs) approach with controlling for repeated measurements within 9 the same subject. These DRS parameters were assumed to be normally distributed. Within-10 patient dependencies were represented by the correlation matrix where all pairwise 11 correlations were assumed to be equal (equicorrelated). The analyses were performed 12 using the GEEQBOX toolbox in Matlab 8.4 (MathWorks Inc., Natick, Massachusetts) and P-13 values < 0.01 were considered statistically significant. 14

Results 15

Robust tissue discrimination in vivo 16 The tissue sensing performance of the system was investigated in vivo during lung cancer 17 surgery, where the presence of blood plays a substantial role. Twenty-one patients were 18 included, median age was 62.8 years (range 38.6 – 78.8 years). A total of 407 and 341 DRS 19 spectra were acquired in vivo and ex vivo, respectively. 20 Measurements in tumor tissue and surrounding lung tissue revealed clear differences in 21 absorption (Fig. 2, A and C) and scattering coefficients (Fig. 2, B and D), indicating inherent 22 differences in tissue structure and composition. The most noticeable differences in the 23 absorption spectrum were observed for the oxyhemoglobin and deoxyhemoglobin 24 absorption bands in the 540-580 nm wavelength region and the water absorption peak 25 near 1450 nm (Fig. 2C). 26 The spectral fitting model was used to derive various tissue parameters from each DRS 27 spectrum, including biological volume fractions (e.g. blood content, water content), 28 oxygenation level of blood (stO2), and the reduced scattering coefficient at 800 nm 29




11

(µs’(800)). For each of these parameters, confidence intervals were computed. We found 1 that extending the wavelength range up to 1600 nm effectively narrowed the confidence 2 intervals for all four parameters, thereby improving the reliability of the obtained values 3 (Fig. 3A). When comparing the data acquired intraoperatively and postoperatively, our 4 system detected consistent differences (P < 0.01) in water content (Fig. 3B) and µs’(800) 5 (Fig. 3C) between tumor tissue and surrounding healthy lung tissue. The average estimated 6 blood content (Fig. 3D) that was encountered during surgery (on average >16%) was much 7 higher than typical physiological values for intact alveolar lung tissue (26), indicating a 8 considerable blood contamination effect. Consequently, estimates for blood content and 9 associated oxygenation levels (Fig. 2E) do not necessarily reflect the true physiological 10 composition of the measured tissue, thus rendering them less suitable for tissue 11 discrimination in the current clinical setting. 12 As stated earlier, the difference in our DRS system and those presented in previous 13 studies is that it extracts diagnostic information from the near-infrared spectral range, 14 where blood has no significant absorption features. When comparing the data acquired 15 during surgery with the data obtained directly after surgery, we note that there are marked 16 differences in blood content (Fig. 3D). However, calculated average values for water 17 content and µs’(800) are comparable for both data sets (Fig. 3, B and C). This supports the 18 notion that estimates for water content and µs’(800) are not compromised by blood 19 absorption. Further evidence of the limited influence of blood contamination comes from 20 the fact that increasing amounts of blood do not lead to an increase in errors in water 21 content (Fig. 4A) and µs’(800) (Fig. 4B), meaning that the reliability of these parameters is 22 essentially unaffected by blood content. 23 The higher water content that was measured in tumor tissue is attributed to the rather 24 high density of tumor tissue compared to aerated healthy lung tissue. Similarly, the 25 relatively high µs’(800) values measured in healthy lung tissue may be the result of 26 scattering due to the large difference in the refractive index between air and lung tissue. 27 Having shown that water content and µs’(800) contain diagnostically useful information, 28 we combined both parameters by calculating the water-to-scattering ratio (water/ 29 µs’(800)). This yielded an average 81% and 56% contrast difference between tumor and 30 surrounding tissue for the ex vivo and in vivo data, respectively (Fig. 4C). 31




12

1

Clinical performance of fiber-optic biopsy needle 2 Added real-time spectral measurements did not interfere with the standard biopsy 3 procedure. The median procedure time including spectral measurements and tissue 4 sampling was less than 5 minutes (range: 1.5-16.4; Table 1). Figure 5 illustrates how real-5 time DRS tissue characterization was performed using the FOBN. A total of 134 DRS spectra 6 were acquired during 11 TNB procedures (patient median age: 67.2 years, range 47.3 - 7 80.5). Histopathological examination of the targeted tissue revealed a total of 10 8 malignancies, of which 9 were classified as non-small cell lung carcinoma (NSCLC) and one 9 as colorectal metastasis. One tissue sample was non-diagnostic. 10 Two observations in particular are important with regards to the clinical relevance of 11 our approach. First, comparison of the DRS OCI values with the biopsy reports showed that 12 in 10 out of 11 patients, tumor tissue could be correctly identified based on an increase in 13 the OCI. Second, one of the subjects (subject 3 in Table 1 and Fig. 6) underwent a biopsy of 14 an 18 mm lesion located close to the diaphragm. Positioning of the FOBN was challenging 15 due to respiratory movement, but fluoroscopic imaging suggested that the tumor was 16 sampled correctly. Pathological analysis of the tissue biopsy taken at that specific location 17 revealed non-diagnostic material. Subsequent repeated biopsy demonstrated moderately 18 differentiated adenocarcinoma. When evaluating the DRS measurements of the first TNB 19 procedure, the OCI suggested that at the moment of the biopsy the FOBN was not in contact 20 with the tumor tissue. If this feedback would have been used, a corrective manipulation of 21 the needle could have increased the chance of an adequate biopsy during the first 22 procedure. These results show that the OCI matched with final pathology in all 11 clinical 23 cases. 24

25

Discussion 26 The DRS-FOBN is an advancement for several reasons: (i) it adds tissue sensing 27 functionality to a biopsy needle tip while retaining all biopsy capabilities; (ii) it operates in 28 real-time and provides diagnostic information to the physician, thereby enabling more 29 accurate needle positioning; (iii) it can be used in conjunction with conventional imaging 30




13

modalities, such as (CT) fluoroscopy or ultrasound, exploiting the complementary 1 strengths of each method; (iv) it can easily be translated into routine clinical use of biopsy 2 procedures. With these attributes, DRS-FOBN offers an integrated solution for 3 spectroscopic biopsy guidance. Tissue sensing and biopsy capabilities are conveniently 4 linked in a manner that provides optimal conditions for both reliable tissue identification 5 (DRS data-acquisition in a wide spectral range) and adequate tissue sampling (the full 6 volume of the notch is available for securing the sample). 7 A key feature of our system is its ability to perform reliable estimation of diagnostically 8 relevant tissue parameters, regardless of the amount of blood that is encountered. Our 9 system provides quantitative information that corresponds well with differences in tissue 10 structure and composition. 11 We found that water content and scattering are the primary discriminators for the 12 transition from healthy lung tissue to tumor tissue. These findings are consistent with 13 previous preclinical studies (19, 20). Using the outlined technology, we demonstrated the 14 clinical feasibility in 11 routine lung biopsy procedures. In this limited series, the identified 15 DRS optical contrast, when matched against histology, could be used to assess needle 16 positioning in each clinical case. 17 Primary lung tumors frequently occur in patients with chronic obstructive pulmonary 18 diseases (COPD). The relationship between these diseases is based upon smoking as 19 mutual risk factor. In COPD like emphysema normal lung tissue architecture is affected, 20 accompanied by the destruction of alveolar walls. In the current study 9 out of the 10 21 patients with a primary lung tumor were smokers. As healthy lung tissue mainly consists of 22 air-filled alveoli and optical contrast between tumor and surrounding tissue is basically 23 based on tissue density, we do not anticipate any problems in applying the method to non-24 COPD patients. However, additional research is needed for evaluating effects of (severe) 25 emphysema on optical parameters. 26 During transthoracic biopsy, breath holding instructions are important, especially 27 during biopsy of lung lesions closer to diaphragm due to respiratory motion. One of the 28 main advantages of the proposed methodology lies in its speed of tissue characterization 29 by spectroscopic analysis. To enable the radiologist to take a tissue sample at the right 30 moment, based on the changes in the derived DRS parameter, spectral data acquisition and 31




14

processing should be performed (near) real-time. The current prototype system and 1 software settings were optimized for acquisition of high quality DRS research data and 2 spectra were acquired with subsecond acquisition times (300-700 ms). Simple adjustments 3 to the system configuration would enable almost immediate feedback on the tissue at the 4 needle tip. 5 Standard-of-care procedures, even when carefully performed by highly skilled and 6 experienced radiologists, can miss small pulmonary lesions due to location, size, and 7 respiratory motion during the biopsy procedure. Accurate real-time tissue identification 8 during biopsy procedures, as described in this study, can shift the paradigm of diagnostic 9 biopsies by enabling accurate tissue sampling of lesions that are difficult or impossible to 10 sample by fluoroscopic imaging alone. This may not only help to increase the biopsy yield 11 for histopathological analysis, but may also allow specific sampling of vital tumor tissue 12 when needed, such as for genetic profiling for tailored treatment (personalized medicine) 13 (7, 8). 14 We anticipate that DRS-based biopsy guidance, as described in this study, is not limited 15 to lung cancer, but may also be used for breast (9-12), liver (13), and other cancer types. 16 Larger multicenter studies are needed to confirm our data and further elucidate the 17 diagnostic value (sensitivity and specificity) of the reported method for lung cancer and 18 other tumors. 19 We conclude that real-time spectroscopic guidance is an important new step to optimize 20 the diagnostic performance and the quality of biopsy procedures in clinical practice. The 21 presented technology creates a basis for the design and clinical implementation of 22 integrated fiber-optic tools for a variety of minimal invasive applications. 23 24

Acknowledgements 25 We thank J.J.J. de Vries, H.M. Klomp, M.W. Wouters, and J.W. van Sandick, T.M. Bydlon, M. 26 Müller, V.V. Pully, C. Reich and M. van der Voort for their assistance in conducting the 27 clinical experiments; L. de Boer and N. Langhout for helpful discussion on the experimental 28 results; and W. Verkruijsse, M. van der Voort and T.M. Bydlon for attentive reading of the 29 manuscript. 30




15

1

References 2

1. Aberle DR, Berg CD, Black WC, Church TR, Fagerstrom RM, Galen B, et al. The National 3 Lung Screening Trial: overview and study design. Radiology. 2011;258:243-53. 4 2. Oudkerk M, Heuvelmans MA. Screening for lung cancer by imaging: the Nelson study. 5 Jbr-btr. 2013;96:163-6. 6 3. Kothary N, Lock L, Sze DY, Hofmann LV. Computed tomography-guided percutaneous 7 needle biopsy of pulmonary nodules: impact of nodule size on diagnostic accuracy. Clin 8 Lung Cancer. 2009;10:360-3. 9 4. Gong Y, Sneige N, Guo M, Hicks ME, Moran CA. Transthoracic fine-needle aspiration vs 10 concurrent core needle biopsy in diagnosis of intrathoracic lesions: a retrospective 11 comparison of diagnostic accuracy. Am J Clin Pathol. 2006;125:438-44. 12 5. Rivera MP, Detterbeck F, Mehta AC. Diagnosis of lung cancer: the guidelines. Chest. 13 2003;123:129s-36s. 14 6. Priola AM, Priola SM, Cataldi A, Ferrero B, Garofalo G, Errico L, et al. CT-guided 15 percutaneous transthoracic biopsy in the diagnosis of mediastinal masses: evaluation of 16 73 procedures. Radiol Med. 2008;113:3-15. 17 7. Moreira AL, Thornton RH. Personalized medicine for non-small-cell lung cancer: 18 implications of recent advances in tissue acquisition for molecular and histologic 19 testing. Clin Lung Cancer. 2012;13:334-9. 20 8. Ausborn NL, Le QT, Bradley JD, Choy H, Dicker AP, Saha D, et al. Molecular profiling to 21 optimize treatment in non-small cell lung cancer: a review of potential molecular 22 targets for radiation therapy by the translational research program of the radiation 23 therapy oncology group. Int J Radiat Oncol Biol Phys. 2012;83:e453-64. 24 9. Cerussi A, Shah N, Hsiang D, Durkin A, Butler J, Tromberg BJ. In vivo absorption, 25 scattering, and physiologic properties of 58 malignant breast tumors determined by 26 broadband diffuse optical spectroscopy. J Biomed Opt. 2006;11:044005. 27




16

10. Brown JQ, Wilke LG, Geradts J, Kennedy SA, Palmer GM, Ramanujam N. Quantitative 1 optical spectroscopy: a robust tool for direct measurement of breast cancer vascular 2 oxygenation and total hemoglobin content in vivo. Cancer Res. 2009;69:2919-26. 3 11. Volynskaya Z, Haka AS, Bechtel KL, Fitzmaurice M, Shenk R, Wang N, et al. Diagnosing 4 breast cancer using diffuse reflectance spectroscopy and intrinsic fluorescence 5 spectroscopy. J Biomed Opt. 2008;13:024012. 6 12. Evers DJ, Nachabe R, Vranken Peeters MJ, van der Hage JA, Oldenburg HS, Rutgers EJ, et 7 al. Diffuse reflectance spectroscopy: towards clinical application in breast cancer. 8 Breast Cancer Res Treat. 2013;137:155-65. 9 13. Evers DJ, Nachabe R, Hompes D, van Coevorden F, Lucassen GW, Hendriks BH, et al. 10 Optical sensing for tumor detection in the liver. Eur J Surg Oncol. 2013;39:68-75. 11 14. Zonios G, Dimou A, Carrara M, Marchesini R. In vivo optical properties of melanocytic 12 skin lesions: common nevi, dysplastic nevi and malignant melanoma. Photochem 13 Photobiol. 2010;86:236-40. 14 15. Rajaram N, Reichenberg JS, Migden MR, Nguyen TH, Tunnell JW. Pilot clinical study for 15 quantitative spectral diagnosis of non-melanoma skin cancer. Lasers Surg Med. 16 2010;42:716-27. 17 16. A'Amar OM, Liou L, Rodriguez-Diaz E, De las Morenas A, Bigio IJ. Comparison of elastic 18 scattering spectroscopy with histology in ex vivo prostate glands: potential application 19 for optically guided biopsy and directed treatment. Lasers Med Sci. 2013;28:1323-9. 20 17. Chang VT, Bean SM, Cartwright PS, Ramanujam N. Visible light optical spectroscopy is 21 sensitive to neovascularization in the dysplastic cervix. J Biomed Opt. 2010;15:057006. 22 18. Bard MP, Amelink A, Skurichina M, Noordhoek Hegt V, Duin RP, Sterenborg HJ, et al. 23 Optical spectroscopy for the classification of malignant lesions of the bronchial tree. 24 Chest. 2006;129:995-1001. 25 19. Spliethoff JW, Evers DJ, Klomp HM, van Sandick JW, Wouters MW, Nachabe R, et al. 26 Improved identification of peripheral lung tumors by using diffuse reflectance and 27 fluorescence spectroscopy. Lung Cancer. 2013;80:165-71. 28 20. Evers DJ, Nachabe R, Klomp HM, van Sandick JW, Wouters MW, Lucassen GW, et al. 29 Diffuse reflectance spectroscopy: a new guidance tool for improvement of biopsy 30 procedures in lung malignancies. Clin Lung Cancer. 2012;13:424-31. 31




17

21. Nachabe R, Hendriks BH, Desjardins AE, van der Voort M, van der Mark MB, Sterenborg 1 HJ. Estimation of lipid and water concentrations in scattering media with diffuse optical 2 spectroscopy from 900 to 1,600 nm. J Biomed Opt. 2010;15:037015. 3 22. Nachabe R, Hendriks BH, van der Voort M, Desjardins AE, Sterenborg HJ. Estimation of 4 biological chromophores using diffuse optical spectroscopy: benefit of extending the 5 UV-VIS wavelength range to include 1000 to 1600 nm. Biomed Opt Express. 6 2010;1:1432-42. 7 23. Farrell TJ, Patterson MS, Wilson B. A diffusion theory model of spatially resolved, 8 steady-state diffuse reflectance for the noninvasive determination of tissue optical 9 properties in vivo. Med Phys. 1992;19:879-88. 10 24. Shah N, Cerussi AE, Jakubowski D, Hsiang D, Butler J, Tromberg BJ. The Role of Diffuse 11 Optical Spectroscopy in the Clinical Management of Breast Cancer. Dis Markers. 12 2004;19:95-105. 13 25. Nagdyman N, Fleck T, Schubert S, Ewert P, Peters B, Lange PE, et al. Comparison 14 between cerebral tissue oxygenation index measured by near-infrared spectroscopy 15 and venous jugular bulb saturation in children. Intensive care med. 2005;31:846-50. 16 26. Weibel ER. In: Gil J, editor. Models of Lung Disease: Microscopy and Structural Methods. 17 New York: Marcel Dekker; 1990. p.239. 18

19




18

Figure Legends 1

2

Figure 1. Real-time tissue sensing with fiber-optic biopsy needle (FOBN). During 3 transthoracal biopsy, the loaded FOBN device is inserted at the planned entry point and 4 advanced towards the lesion (A). Real-time spectral characterization of the tissue at the 5 needle tip is performed along the needle path and at the biopsy site as displayed on the 6 monitor. Specific design of the FOBN and the subsequent steps in handling (B; steps 1-3). 7 Optical fibers at the tip of the biopsy needle allow spectral characterization of the tissue 8 directly in front of the needle tip (step 1). After firing the biopsy gun, the inner stylet 9 rapidly penetrates the target tissue (step 2) and is followed by a split-second automatic 10 firing of the outer cannula, cutting and capturing tissue in the notch from the site where the 11 final tissue sensing took place (step 3). The FOBN instrument is handled by a radiologist 12 like a regular biopsy needle (C). 13

14

Figure 2. Intraoperative tissue characterization. Average absorption coefficients (A) and 15 reduced scattering coefficients (B) acquired in vivo in healthy lung tissue (Normal) and 16 tumor tissue (Tumor) during surgery (n = 21 patients). Errors bars indicate 95% 17 confidence intervals and are shown every 50 nm. Absolute differences in average 18 absorption coefficient (C) and scattering coefficients (D) indicate clear intrinsic tissue 19 contrast between healthy lung tissue and tumor. 20

21

Figure 3. DRS parameter quantification. Comparison of confidence intervals obtained for 22 blood, stO2, water and µs’(800) when model fit is applied from 450 to 1100 nm and from 23 450 to 1600 nm (A). Note that extending the wavelength range up to 1600 nm narrows the 24 confidence intervals of each parameter. Bar graphs showing the values for water (B), 25 µs’(800) (c), blood (D), stO2 (E), as measured during (in vivo; n = 407 spectra) and after (ex 26

vivo; n = 341 spectra) surgery. N: healthy lung tissue; T: tumor tissue. Values are given as 27 the mean ± standard error, adjusted for repeated measurements. *P < 0.01. 28

29




19

Figure 4. Robust tissue characterization using water and µs’(800). Confidence intervals for 1 water (A) and µs’(800) (B) remain stable with increasing blood content. Quantification of 2 the water-to-scattering ratio (water/ µs’(800)) showing a significant differences between 3 healthy lung tissue (N) and tumor tissue (T), both during (in vivo) and after (ex vivo) 4 surgery (C). Values are given as the mean ± standard error, adjusted for repeated 5 measurements. *P < 0.01. 6 7

Figure 5. Added quantitative spectral functionality during routine lung biopsy. Positioning 8 of the FOBN based on CT fluoroscopy imaging in lung tissue (A) and near the target lesion 9 (D). Co-registered DRS measurements (blue dotted line) and corresponding fit curves (red 10 lines) (B and E). Optical contrast index (OCI) values were determined based on the 11 spectroscopically derived values for water and µs’(800) (C and F). Data for water and 12 µs’(800) represent mean values ± standard error of the mean. Note that the OCI measured 13 near the target tissue (F) represents the relative water-to-scattering ratio, using healthy 14 lung tissue as a reference. 15

16

Figure 6. DRS tissue characterization during TNB procedures in two subjects. Although CT 17 fluoroscopic imaging in subject 3 (A) suggests that the tissue biopsy was taken from the 18 target nodule, the tissue sample (B) proved to be non-diagnostic. The sample contained 19 only normal lung tissue, indicating that the targeted tissue was missed. No substantial 20 change in Optical Contrast Index (OCI) was seen (C). This example underlines the 21 importance of real-time measurements and data analysis in order to identify the transition 22 of needle placement in a tumor based on the changes in the derived parameters. In subject 23 11(D-F) the OCI (F) nearly doubled once the FOBN was inserted into the target nodule (E). 24 The tumor was histologically diagnosed as a non-small-cell lung carcinoma (NSCLC). OCI 25 data represent mean values ± standard error of the mean. 26

27




1

3

2

B

A

C

Figure 1




C

A B

D

Figure 2




Blo

od [%

]

StO

2 [%

]

B

Confidence intervals

(fitted 450-1100 nm)

Confid

ence in

terv

als

(fitted 4

50-1

600 n

m)

C

D E

A

µs’(800)

[cm

-1]

Wate

r [%

]

Figure 3




Confid

ence in

terv

al

Water [%]

µs’(800) [cm-1]

Confid

ence in

terv

al

A

B

C

Figure 4




A B C

OCI: 3.38

Norm

al

Tum

or

5 cm

D E F 5 cm

Figure 5




A C B Position 1 Position 2 Position 3

Subje

ct 11

S

ubje

ct 3

OCI Histopathology

5 cm

5 cm

200 μm

200 μm

NSCLC

Non-diagnostic

D F E

Figure 6




Study

No.

Age

(years)

Smoker Tumor

diameter

(mm)

Procedure

planning time

(min)

Procedure

time (min)

OCI Histopathology

1 61 Yes 52 13.7 5.7 3.38 Non-small-cell lung carcinoma 2 75 Yes 63 N/A 1.9 1.12 Non-small-cell lung carcinoma 3 71 Yes 18 4.5 6.6 0.72 Non-diagnostic; only lung 4 67 No 47 3.6 4.3 9.65 Metastasis colon carcinoma 5 69 Yes 17 6.5 4.8 >10 Non-small-cell lung carcinoma 6 76 Yes 17 4.3 3.0 1.37 Non-small-cell lung carcinoma 7 47 Yes 29 4.5 5.0 1.73 Non-small-cell lung carcinoma 8 54 Yes 60 7.0 4.2 3.56 Non-small-cell lung carcinoma 9 80 No 18 5.8 16.6 2.97 Non-small-cell lung carcinoma 10 67 Yes 40 7.6 3.3 1.43 Non-small-cell lung carcinoma 11 65 Yes 27 4.1 6.1 1.95 Non-small-cell lung carcinoma

Table 1. Demographics and individual data for patients (n = 11). The procedure planning time (min) is the time between the CT imaging made for procedure planning and injection of the local anesthetic. The procedure time was defined as time between insertion of the FOBN and acquisition of a tissue sample. OCI: optical contrast index.




Published OnlineFirst August 31, 2015.Clin Cancer Res Jarich W Spliethoff, Warner Prevoo, Mark AJ Meier, et al. feasibility studyspectroscopy during transthoracic lung biopsy: a clinical Real-time in vivo tissue characterization with diffuse reflectance

Updated version

10.1158/1078-0432.CCR-15-0807doi:

Access the most recent version of this article at:

Manuscript

Authoredited. Author manuscripts have been peer reviewed and accepted for publication but have not yet been

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected] at

To order reprints of this article or to subscribe to the journal, contact the AACR Publications

Permissions

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://clincancerres.aacrjournals.org/content/early/2015/08/29/1078-0432.CCR-15-0807To request permission to re-use all or part of this article, use this link



http://clincancerres.aacrjournals.org/lookup/doi/10.1158/1078-0432.CCR-15-0807

http://clincancerres.aacrjournals.org/cgi/alerts

mailto:[email protected]

http://clincancerres.aacrjournals.org/content/early/2015/08/29/1078-0432.CCR-15-0807


Documents

Running title: Key words Disclosure 30 · 20 Here we present a unique fiber-optic biopsy needle (FOBN) platform that uses Diffuse 21 Reflectance Spectroscopy (DRS) in conjunction