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

2. Outline Introduction Previous work Feature selection Experiments

3. Motivation Early cancer detection is critical for 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 of cancer detection: Early detection High accuracy Low cost

5. Mass spectrum We can detect some early-stage cancers by analyzing the blood mass spectrum. ratio of molecular weight to electrical charge intensity 20,000 05,000 10,00015,000 10 –4 10 –2 10 0 10 2

6. Mass spectrum Data mining Results Blood

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

10. Outline Introduction Previous work Feature selection Experiments

11. Feature selection ratio of molecular weight to electrical charge intensity 200 400 600 Cancer Healthy Statistical difference:

12. Feature selection ratio of molecular weight to electrical charge intensity 200 400 600 Window size: minimal distance between selected points Cancer Healthy

13. Outline Introduction Previous work Feature selection Experiments

14. Data sets Data set Number of cases Cancer Healthy 123123 100 162 116 91

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

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

17. Best control values Decision trees Data set Number of features Window size Accuracy 1 4 182% 2 8 494% 3 86499%

18. Best control values Support vector machines Data set Number of features Window size Accuracy 1 321683% 2 4 294% 3 16 899%

19. Best control values Neural networks Data set Number of features Window size Accuracy 1 3225682% 2 32 196% 3 16 299%

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

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

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

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

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