Diagnosis of Ovarian Cancer Based on Mass Spectrum of Blood Samples

1. Diagnosisof Ovarian CancerBased on Mass Spectrum of Blood Samples Hong Tang Committee: Eugene Fink Lihua Li Dmitry B. Goldgof

2. Outline • Introduction • Previous work • Feature selection • Experiments

3. Motivation Early cancer detection is criticalfor successful treatment. • Five year survival for ovarian cancer: • Early stage: 90% • Late stage: 35% 80% are diagnosed at a late stage.

4. Motivation • Desired features ofcancer detection: • Early detection • High accuracy • Low cost

5. 102 100 intensity 10–2 10–4 0 5,000 10,000 15,000 20,000 ratio of molecular weight to electrical charge Mass spectrum We can detect some early-stage cancersby analyzing the blood mass spectrum.

6. Blood Mass spectrum Data mining Results Mass spectrum

7. Outline • Introduction • Previous work • Feature selection • Experiments

8. Initial work • Vlahou et al. (2001): Manual diagnosis of bladder cancer based on mass spectra • Petricoin et al.(2002): Application of clustering to mass spectra for the ovarian-cancer diagnosis

9. Later work Decision trees Adam et al. (2002): 96% accuracy for prostate cancer Qu et al. (2002): 98% accuracy for prostate cancer Clustering Petricoin et al. (2002): 80% accuracy for prostate cancer Neural networks Poon et al. (2003): 91% accuracy for liver cancer

10. Outline • Introduction • Previous work • Feature selection • Experiments

11. Cancer Healthy Statistical difference: Feature selection intensity 200400600 ratio of molecular weight to electrical charge

12. Cancer Healthy Feature selection intensity 200400600 ratio of molecular weight to electrical charge Window size: minimal distance between selected points

13. Outline • Introduction • Previous work • Feature selection • Experiments

14. Data sets

15. Learning algorithms • Decision trees (C4.5) • Support vector machines (SVMFu) • Neural networks (Cascor 1.2)

16. Control variables • Number of features, 1–64 • Window size, 1–1024

17. Best control valuesDecision trees

18. Best control valuesSupport vector machines

19. Best control valuesNeural networks

20. Decision trees , SVM , Neural networks Learning curveData set 1 100 90 accuracy (%) 80 70 60 150 50 250 200 100 training size

21. Learning curveData set 2 100 90 accuracy (%) 80 70 60 150 50 250 0 200 100 training size Decision trees , SVM , Neural networks

22. Learning curveData set 3 100 90 accuracy (%) 80 70 60 150 50 250 0 200 100 training size Decision trees , SVM , Neural networks

23. Main results • Automated detection of ovarian cancer by • analyzing the mass spectrum of the blood • Identification of the most informative points of the mass-spectrum curves • Experimental comparison of decision trees, SVM and neural networks

24. Future work • Experiments with other data sets • Other methods for feature selection • Combining with genetic algorithm