Evaluating Lung Nodules in an Endemic Region for

CoccidioidomycosisLung Nodule Conference

Michael W. Peterson, M.D.Valley Medical Foundation

Professor and Chief of MedicineUCSF Fresno

Overview of the Talk

Overview for evaluating lung nodules Challenges applying National

Guidelines in Fresno Evolving tools in the Central Valley

Cancer Death Rates for Men

Cancer Death Rates for Women

Cancer Survival by Stage

The Challenge

56 year old male current smoker with 40 pack years.He has an unintended 10 pound weight loss without other constitutional symptoms.

67 year old woman lifetime nonsmoker who had symptoms of a respiratory infection 3 months agopresents with this chest CT scan. Currently asymptomatic.

Overview of the Talk

Overview for evaluating lung nodulesOverview for evaluating lung nodules Challenges applying National

Guidelines in Fresno Evolving tools in the Central Valley

Clinical Issues Related to Risk

Clinical risk factors (Pretest Probability)– Underlying risk:

• Exposure (tobacco, radon, asbestos)• Age• Gender (male>female)• Presence of chronic lung disease• Personal history of malignancy• First degree relative with lung cancer

Approach to Evaluating Lung Nodules

Clinical risk factors (Pretest Probability) Radiological characteristicsRadiological characteristics Special characteristics

Radiological Criteria: Size

Radiological characteristics– Size: one of the

most important factors in your evaluation

Size Risk

<3 mm 0.2%4-7 mm 0.9%8-20 mm 18%

>20 mm 50%

Radiological Criteria: Border, Calcification and Growth

Spiculated border/corona radiata Stippled or eccentric calcification Growth rate

Nodule Demonstrating “Corona Radiata”

Dense Central Calcification = Benign Disease

Clinical Issues Related to Risk

Radiological characteristics– Growth rate: usual doubling time between

20 and 400 days• Three dimensional growth (30% increase in

diameter = doubling volume; volume = r3)• Screening and review has questioned the

“two-year rule”

Nodule Growth Rate

Average doubling times for lung nodules

Radiographic Characteristic Doubling Time

Ground glass 813 days

Ground glass with solid component

457 days

Solid 149 days

Problem: accurate measurements of nodulesDoubling times shorter in smokers

Hasegawa, BMJ, 2000

Solid Nodule

Ground Glass Nodule

Clinical Issues Related to Risk

Clinical risk factors (Pretest Probability) Radiological characteristics Special characteristics Risk stratification

Proposed Guideline: Solid Lung Nodules

Chest. 2013;143(3):840-846

Lung Nodules and Fresno’s “Friend”

Proposed Guideline: Solid Lung Nodules

Chest. 2013;143(3):840-846

Representative Cases: On Line Calculators

56 year old male current smoker with 40 pack years.He has an unintended 10 pound weight loss without other constitutional symptoms.

67 year old woman lifetime nonsmoker who had symptoms of a respiratory infection 3 months agopresents with this chest CT scan. Currently asymptomatic.

Coccidioidomycosis Adenocarcinoma of the lungCoccidioidomycosis Adenocarcinoma of the lung

Calculated Risk 33-75% Calculated Risk 2.5-7.2%Calculated Risk 33-75% Calculated Risk 2.5-7.2%

Analysis of Previous Calculators

 Probability of Cancer Coccidioidomycosis CancerAverage 60.8 ± 38.1 59.2 ± 30.7<5 % N: 10 (9%) N: 4 (2%)5-60% N: 38 (35%) N: 92 (48%)>60% N: 62 (56%) N: 96 (50%)

  Coccidiodomycosis CancerAverage 25.9 ± 21 52.8 ± 23<5 % N: 18 (16%) N: 3 (2%)5-60% N: 82 (75%) N: 93 (48%)>60% N: 10 (9%) N: 96 (50%)

  Coccidiodomycosis CancerAverage  69.5 ± 38 76.6 ± 22.4 <5 % N: 6 (5%) N: 3 (2%)5-60% N: 50 (45%) N: 112 (58%)>60% N: 54 (50%) N: 77 (40%)

Mayo Clinic

Brock Univ.

BayesianModel

Effectiveness of Serology to Differentiate Lung Cancer

from Cocci

Nicola et al., ATS

  Sensitivity(95% CI)

Specificity(95% CI)

Positive predictive value

(95% CI)

Negative predictive value

(95% CI)

Coccidioides serology by immunodiffusion

77%(68-84)

93%(89-96)

86%(77-91)

89%(84-92)

Coccidioides serology by complement fixation

51%(42-61)

98%(96-99)

92%(82-96)

79%(74-84)

Differentiation Based on the Radiographic Appearance of the

Lung Nodules Two chest radiologists reviewed chest

CT scans from 302 patients in a blinded fashion. All patients had a biopsy-proven diagnosis of Cocci or Lung cancer

Ronaghi, ACCP 2015

Radiographic Appearance of Nodules

Radiographic Charateristic Cocci Lung Cancer

P-Value

Diameter (cm) 2.9 ± 1.6 4.2 ± 2.5 .0001

Satellite lesions present 59% 14% .001

Chronic Lung Disease present 19% 66% .0001

Solid Density 80% 82% NS

Cavitary Nodule 5% 6% NS

Cavity wall thickness (mm) 4.8 ±2.7 4.4 ± 2.9 NS

Mediastinal adenopathy 57% 62% .035

Ronaghi, ACCP 2015

Project Goal

To develop a calculator that better differentiates nodules due to Cocci

Methods Developed using

302 patients – 192 Lung Cancer and 110 Cocci

Using backward regression – we identified 9 clinical and radiographic variables from 20

Calculated odds ratio for each of the variables for cancer

Odds ratio was used to calculate a numerical value weighted for lung cancer

UCSF – Fresno CalculatorVariable 0 Points 1 Point 2 Points 3 Points 4 Points TotalAge Dx < 50 50-55 55-59 60-64 65+  Gender Male Female  Smoking Hx Never Past Current  

Occup. Other Construct. Field Work Mechanic Military  

Chronic Lung Disease Hx

None Asthma Bronchitis COPD COPD+Asthma

 

Lung Disease on CT

None/Other Emphysema/Reticular

Nodule Location

RML LLL RLL RUL LUL  

Nodule Border-

Smooth Lobulated Spiculated  

Family Hx None Asthma/COPD Lung ca  

Nodule Size < 2cm >2 cm  Total            

Results Learning Set (238

patients):– Cocci patients (N =

41): mean score 8.9 (95%CI: 5.1-12.7)

– Lung Cancer (N = 192): mean score 19.6 (95% CI: 14.6-24.6)

Results

We next applied the scoring system to 143 patients who were not included in the learning set

117 patients had lung cancer and 26 patients had Cocci

Results Testing Set (117

patients):– Cocci patients (N =

26): mean score 9.1 (95%CI: 1.7-16.5)

– Lung Cancer (N = 117): mean score 25.2 (95% CI: 15.4-30.0)

Proposed Guideline: Solid Lung Nodules

Chest. 2013;143(3):840-846

Use of FDG-PET Scanning Principle that malignant lesions have

higher metabolic rates Limitations:

– At least 8 mm in size– Diabetic control– Cost– Best utilized in the moderate risk group– Poor anatomic localization

Combined PET-CT

SUV 4.5

PET CT to Evaluate Lung Nodules

SUV 3.75

PET Activity in Lung Cancer versus Cocci Nodules

SUV 2.5

Lung; published on-line May 7, 2014

FDG-PET for Lung cancer Risk Calculation

Reviewed 70 published studies between Oct 2000 and April 2014 that evaluated nodules by PET

Compared test performance between sites with endemic fungal disease and those in non-endemic regions

SA Deppen et al, JAMA, 2014

Results of the Meta Analysis Overall (70 STUDIES):

– Sensitivity: 89%– Specificity: 75%

Nonendemic regions (60):– Sensitivity: 89%– Specificity: 77%

Endemic regions (10):– Sensitivity: 94%– Specificity: 61%

SA Deppen et al, JAMA, 2014

Conclusions Evaluating lung nodules remains a

challenging exercise for clinicians Guidelines must be interpreted and utilized

within the context of local conditions We have limited tools for differentiating

nodules due to Cocci from lung cancer A multidisciplinary clinic provides us the

opportunity to develop our local guidelines

Future and Ongoing Projects

Refining and testing the nodule prediction tool prospectively

Evaluating the performance of recently developed PCR for Cocci

Development of a tissue and clinical database to share for clinical research

Evaluating the impact of the program on patient quality of life

Acknowledgments CRMC for supporting

the Lung Nodule Clinic Kathy Norkunas, Nurse

Navigator Paul Mills, PhD, MPH Kathy Bilello, MD Karl Van Gundy, MD

Daya Upadhyay, MD Gurpreet Bambra, MD Ali Rashidian, MD Mickey Sachdeva, MD Reza Ronaghi, MD Summer Biomedical

Intern Program