Evaluating Lung Nodules in an Endemic Region for Coccidioidomycosis

1. Evaluating Lung Nodules
in an Endemic Region for
Coccidioidomycosis
Lung Nodule Conference
Michael W. Peterson, M.D.
Valley Medical Foundation
Professor and Chief of Medicine
UCSF Fresno

2. Overview of the Talk
 Overview for evaluating lung nodules
 Challenges applying National
Guidelines in Fresno
 Evolving tools in the Central Valley

3. Cancer Death Rates for Men

4. Cancer Death Rates for
Women

5. Cancer Survival by Stage

6. 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 ago
presents with this chest CT scan. Currently asymptomatic.

7. 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

8. 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

9. Approach to Evaluating Lung
Nodules
 Clinical risk factors (Pretest Probability)
 Radiological characteristicsRadiological characteristics
 Special characteristics

10. 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%

11. Radiological Criteria: Border,
Calcification and Growth
 Spiculated border/corona radiata
 Stippled or eccentric calcification
 Growth rate

12. Nodule Demonstrating
“Corona Radiata”

13. Dense Central Calcification =
Benign Disease

14. 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 = πr3
)
• Screening and review has questioned the
“two-year rule”

15. 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 nodules
Doubling times shorter in smokers
Hasegawa, BMJ, 2000

16. Solid Nodule

17. Ground Glass Nodule

18. Clinical Issues Related to Risk
 Clinical risk factors (Pretest Probability)
 Radiological characteristics
 Special characteristics
 Risk stratification

19. Proposed Guideline: Solid Lung
Nodules
Chest. 2013;143(3):
840-846

20. Lung Nodules and Fresno’s
“Friend”

21. Proposed Guideline: Solid Lung
Nodules
Chest. 2013;143(3):
840-846

22. 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 ago
presents 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%

23. Analysis of Previous
Calculators
Probability of Cancer Coccidioidomycosis Cancer
Average 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 Cancer
Average 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 Cancer
Average 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.
Bayesian
Model

24. 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)

25. 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

26. 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

27. Project Goal
 To develop a calculator that better
differentiates nodules due to Cocci

28. 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

29. UCSF – Fresno Calculator
Variable 0 Points 1 Point 2 Points 3 Points 4 Points Total
Age 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

30. 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)

31. 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

32. 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)

33. Proposed Guideline: Solid Lung
Nodules
Chest. 2013;143(3):
840-846

34. 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

35. Combined PET-CT
SUV 4.5

36. PET CT to Evaluate Lung
Nodules
SUV 3.75

37. PET Activity in Lung Cancer
versus Cocci Nodules
SUV 2.5
Lung; published on-line May 7, 2014

38. 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

39. 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

40. 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

41. 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

42. 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