1. Bayes Network: Lung Cancer Diagnostics
Group 7: Hoang-Dung Nguyen, Lily Tran
Dr. Vidakovic

2. Introduction
Objective: With given characteristics of an individual’s gender, age, smoking status, carcinogen exposure, CT scan results, and presence of a bloody sputum, a Bayesian Network will be used as a tool to find the probability of an individual being diagnosed with lung cancer.
Bayesian Networks
Graphical model
Probability of random variables and its conditional dependencies
Uses
Diagnoses of disease, treatment alternatives, treatment outcomes1
Robotics, finance
Hoang(Intro and Objective) - In 2013, it is estimated that lung cancer causes more cancer-related deaths than prostate, breast, and colon cancer combined
inadequate screening methods to identify patients at earlier stages of the disease. Therefore, we aim to develop a diagnostic screening system using bayesian networks that identify individuals at the highest risk for lung cancer
& Lily (Bayesian Network & Uses)

3. Directed Acyclic Graph (DAG)
Independent Variables
Predispositions/Causes
Condition
Hoang - To utilize Bayesian Networks as a diagnostic tool, we developed a Directed Acrylic Graph, which is a causation model
Symptom/Effects

4. Probabilities Legend Age (>60): A Gender (Male): G Smoker: S
Carcinogen Exposure: C
Lung Cancer: L
CT Scan: CT
Bloody Sputum: B AC A
.816
.1843 GC G
.50 CC C
Condition
.820
.1804
A G
.890
.110
A GC
.224
.7765
AC G
.333
.667
AC GC SC S
Condition
.916
.0846,7
A G
.914
.086
A GC
.742
.258
AC G
.802
.198
AC GC
Lily

5. Probabilities Legend Age (>60): A Gender (Male): G Smoker: S
Carcinogen Exposure: C
Lung Cancer: L
CT Scan: CT
Bloody Sputum: B LC L
Condition
.910
.0908
C S
.931
.069
C Sc
.920
.080
Cc S
.983
.0179
Cc Sc
CTC CT
Condition
.30
.7010 L
.70 LC BC B
Condition
.43
.5711 L
.57 LC
Lily

6. Example 1
Sally is an 80 year old women who smokes and is exposed to carcinogens. She recently got a positive CT scan and has a bloody sputum. What is the probability of her having lung cancer?
Hard Evidence: Yes = 1 No = 0
>60 = 1; male = 0; smoker = 1, carcinogen = 1,
CT scan = 1, bloody sputum = 1
Probability: ?
Lily (state lung cancer symptoms are prominent→ high value )

7. Example 2 Hard Evidence: Yes = 1 No = 0
John is a 54-year old man who has been experiencing shortness of breath and fatigue. His father has smoked cigarettes throughout his childhood, so he was likely exposed to secondhand smoke for several years. John has a history of smoking but is currently not a smoker. What is the probability that he has lung cancer?
Hard Evidence: Yes = 1 No = 0
>60 = 0; male = 1; smoker = 0, carcinogen = 1
Probability: ?
Hoang

8. Example 3
Cara is a 30-year old woman who has led a very healthy lifestyle. She is a nonsmoker who has minimal to no exposure to secondhand smoke. From her recent check up, she had a healthy CT scan and no bloody sputum. What is the probability of Cara being diagnosed with lung cancer?
Hard Evidence: Yes = 1 No = 0
>60 = 0; male = 0; smoker = 0, carcinogen = 0,
CT scan = 0, bloody sputum = 0
Probability: ?
Lily

9. Conclusion Limitations
Values do not take other variables into account (i.e. family history, smoking history)
Non-Uniform probabilities based on conditionals in given literature
Nonrepresentative Found Probabilities
Prevalence of lung cancer is ~0.01% within U.S. 12
Results are estimates of risk, not concrete probabilities
Findings
Bayes Network has the potential to be a valuable tool for medical diagnostics.
Measures a person’s risk for lung cancer, but should not be used alone.
Future Improvements
Expand our model (DAG) to include all variables that contribute to risk
Improving upon the limitations previously stated.
Lily (Limitations) → nonuniform example, only depends on age and gender and not region Hoang (Conclusions)

10. References
1. Institute NC. SEER Stat Fact Sheets: Lung and Bronchus Cancer. 2013; Accessed November 19, 2013.
2. Peter JF Lucus et al. Bayesian networks in biomedicine and health-care. Artificial Intelligence in Medicine 30 (2004) 201–214
3. Siegel J. Projected Future Growth of the Older Population. In: Services HaH, ed2013.
4. Jaakkola MS. Environmental tobacco smoke and health in the elderly. European Respiratory Journal. January 1, ;19(1):
5. Association AL. Trends in Tobacco Use. 2011; Accessed November 23, 2013.
6.Pleis JR, Ward BW, Lucas JW. Summary health statistics for U.S. adults: National Health Interview Survey, Vital And Health Statistics. Series 10, Data From The National Health Survey. 2010(249):1-207.
7. NCHS C. Early Release of Selected Estimates on Data From the 2009 National Health Interview Survey. 2010; Accessed November 20, 2013.
8. Couraud, S. et al. Lung cancer in never smokers – A review. European Journal of Cancer Mar 28. (Epub ahead of print).
9. Hemminki L, Li X. Familial risk for lung cancer by histology and age of onset: evidence for recessive inheritance. Division of Molecular Genetic Epidemiology, German Cancer Research Center, Heidelberg, Germany Mar;31(2):
10. Koji O et al. Low-dose CT scan screening for lung cancer: comparison of images and radiation doses between low-dose CT and follow-up standard diagnostic CT. SpringerPlus 2013, 2:393 doi: / l.
11. Lorraine Johnston. Symptoms of Lung Cancer. Onconurse. Updated Acessed Nov 18, 2013.
12. Projected Future Growth of the Older Population. Administration on Aging. Updated Dec 31, Accessed Nov 20, 2013.