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DESIGN OF CONSISTENT SYSTEM FOR RADIOLOGISTS TO SUPPORT BREAST CANCER DIAGNOSIS

DESIGN OF CONSISTENT SYSTEM FOR RADIOLOGISTS TO SUPPORT BREAST CANCER DIAGNOSIS. Authors. Boris Kovalerchuk Department of Computer Science, Central Washington University, Ellensburg, WA 98926-7520 borisk@cwu.edu Evgenii Vityaev

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DESIGN OF CONSISTENT SYSTEM FOR RADIOLOGISTS TO SUPPORT BREAST CANCER DIAGNOSIS

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  1. DESIGN OF CONSISTENT SYSTEM FOR RADIOLOGISTS TO SUPPORT BREAST CANCER DIAGNOSIS CWU B.Kovalerchuk

  2. Authors Boris Kovalerchuk Department of Computer Science, Central Washington University, Ellensburg, WA 98926-7520 borisk@cwu.edu Evgenii Vityaev Institute of Mathematics, Russian Academy of Science vityaev@mat.nsk.ru James Ruiz Department of Radiology, Woman’s Hospital, Baton Rouge, LA jrmd@womans.com CWU B.Kovalerchuk

  3. Overall purpose • to develop a prototype radiological consultation system. • retrieve the seconddiagnostic opinion(probable diagnosis) for a given case CWU B.Kovalerchuk

  4. Agenda • BACKGROUND • COMPONENTS OF SYSTEM • RESULTS • METHOD CWU B.Kovalerchuk

  5. 1. BACKGROUND • In the US breast cancer is #1 cancer in women • 182,000 cases in 1995 [Wingo, et al., 1995] • Screening mammography is the most effective tool against breast cancer • However, intra- and inter- observer variability in mammographic interpretation is significant (about 25%) CWU B.Kovalerchuk

  6. Computer-Aided Diagnostic (CAD) systems in Breast Cancer • neural networks, • nearest neighbor methods, • discriminant analysis, • cluster analysis, • linear programming • genetic algorithm • decision trees • ([F. Shtern, 1996], [SCAR, 1996], [TIWDM, 1996] and [CAR, 1996]. CWU B.Kovalerchuk

  7. Consistency of diagnosis • Linear discriminant analysis gives an equation, which separates benign and malignant classes. • Example 0.0670x1-0.9653x2+... • represents a case. • How would one interpret the weighted number of calcifications/cm2 (x1) plus weighted volume (cm3)(x2)? There is no direct medical sense in this formula. CWU B.Kovalerchuk

  8. Basis • similar cases • experience of many radiologists • radiologists’ diagnostic rules • data base of previous cases • Description of a particular case • BI-RADS lexicon of American College of Radiology • BI-RADS- breast imaging reporting and data system CWU B.Kovalerchuk

  9. Advantages • System allows a radiologist to get more important information in comparison with known Computer-Aided Diagnostic (CAD) systems. • These advances are based on a new computational intelligence technique • Logical Data Analysis (LAD) CWU B.Kovalerchuk

  10. Implementation • A rule-based prototype diagnostic system. • The diagnosis is based on combination of • the opinions of radiologists and • the statistically significant diagnostic rules extracted from the available data base. CWU B.Kovalerchuk

  11. 2. COMPONENTS OF SYSTEM • Component 1. Data/Image Base: • Component 2. Diagnostic Rule Base: • Component 3. Diagnosis simulator: • Component 4. Consultant: CWU B.Kovalerchuk

  12. Components of Consultation System data/Image base Rule base simulator of diagnosis “Consultant” CWU B.Kovalerchuk

  13. Component 1. Data/Image Base: • Supports extracted features of mammograms, • Supports patient clinical records, • Supports digital images of mammograms • Component 2. Diagnostic Rule Base: • Support rules extracted from Data Base • Support rules obtained by interviewing radiologists • Component 3. Diagnosis simulator: • Generates diagnosis for a particular case based on the Diagnostic Rule Base. CWU B.Kovalerchuk

  14. Component 2. Diagnostic Rule Base: Rules obtained by interviewing radiologists Rules extracted from Data Base IIf…..Then... If….. Then... radiologist #1 radiologist #2 radiologist #3 If…..Then… If….. Then... CWU B.Kovalerchuk

  15. Component 4. Consultant:“Consultant” Simulated diagnosis for case x Similar cases {yi} Simulated diagnosis of other radiologists for {yi} Digital reproduction of the mammograms x and {yi} DB rules applicable for case x Statistical significance of rules Comparisonuser’s (radiologist’s) diagnosis with simulated diagnosis of other radiologists Comparison of user’s diagnosis with DB diagnosis Rationale of applicable diagnostic rules by experienced radiologists from the RB Consultant CWU B.Kovalerchuk

  16. 3. RESULTSDiagnostic Rules Acquisition. • Expert diagnostic rules were extracted from specially organized interviews of a radiologist (J. Ruiz, MD). • Method of minimized interview • theory of Monotone Boolean Functions • hierarchical approach. • [Kovalerchuk, et al, 1996 a,b]. CWU B.Kovalerchuk

  17. Example of extracted rule • RULE 1: IFNUMber of calcifications per cm2(w1) is large AND TOTal number of calcifications (w3) is large AND irregularity in SHAPE of individual calcifications is marked (y1)THEN highly suspicious for malignancy. • Mathematical expression • w1&w&3y1=>"highly suspicious for malignancy". CWU B.Kovalerchuk

  18. Used features • calcification features: • 1) the number of calcifications/cm2 ; • 2) the volume (in cm3) ; • 3) total number of calcifications ; • 4) irregularity in shape of individual calcifications; • 5) variation in shape of calcifications ; • 6) variation in size of calcifications; • 7) variation in density of calcifications ; • 8) density of calcifications; • 9) ductal orientation; • 10) comparison with previous exam ; • 11) associated findings . CWU B.Kovalerchuk

  19. Questioning procedure for rule extraction • To restore all diagnostic rules thousands of questions might be needed if questions are not specially organized. • For 11 diagnostic features of clustered calcifications there are (2 =2,048) feature combinations, representing cases. 11 CWU B.Kovalerchuk

  20. Results of questioning • Only about 40 questions, i.e. 50 times fewer questions than the full set of feature combinations [Kovalerchuk et al, 1996 a,b]. • Practically all studies in CAD systems derive diagnostic rules using significantly less than 1,000 cases [Gurney, 1994]. • This is the first attempt to work with large number of cases (2,000). CWU B.Kovalerchuk

  21. Data used to extract rules • 156 cases (77 malignant, 79 benign) • 11 features of clustered calcification listed above • two extra features: Le Gal type and density of parenchyma • the diagnostic classes: "malignant" and "benign". CWU B.Kovalerchuk

  22. Diagnostic Rules extracted from Data Base • 44 statistically significant diagnostic rules • conditional probability greater than 0.7 • 30 rules with the conditional probability greater than 0.85 • 18 rules with conditional probability more than 0.95. CWU B.Kovalerchuk

  23. Accuracy • 44 rules. The total accuracy of diagnosis-- 82%. • The False/negative rate -- 6.5% • (9 malignant cases were diagnosed as benign) • The false/positive rate -- 11.9% • (16 benign case were diagnosed as malignant). • For the 30 more reliable rules we obtained 90% total accuracy, • For the 18 most reliable rules we obtained 96.6% accuracy with only 3 false positive cases (3.4%). CWU B.Kovalerchuk

  24. Accuracy • Neural Network ("Brainmaker") software • 100% accuracy on training data • Round-Robin test the total accuracy fell to 66%. • The main reason for this low accuracy is that NN do not have a mechanism to evaluate statistical significance /reliability of the performance on training data. CWU B.Kovalerchuk

  25. Accuracy • Linear Discriminant Analysis ("SIGAMD" software). Poor results (76% on training data test) • Decision Tree approach ("SIPINA" software) accuracy of 76%-82% on training data. • This is worse than what we obtained for the LAD method with the much more difficult Round-Robin test. CWU B.Kovalerchuk

  26. Decision trees x1 < 3 x2 <4 t f x2>5 x2<5 x1 t f t f = 8 > 8 8 < restricted different structures no loops Statistical significance? CWU B.Kovalerchuk

  27. Accuracy • The extremely important false-negative rate • 3-8 cases (LAD), • 8-9 cases (Decision Tree), • 19 cases (Linear Discriminant Analysis) • 26 cases (Neural Network). CWU B.Kovalerchuk

  28. Consistency of diagnosis • Only LAD and decision trees produce diagnostic rules. • These rules make a CAD decision process visible to radiologists. • With these methods radiologists can control the decision making process. CWU B.Kovalerchuk

  29. CONCLUSION • Our study has shown that used Logical Data analysis approach is appropriate for designing a consultation diagnostic system under presented requirements. • This approach can be used for development of a full-size consultation system. CWU B.Kovalerchuk

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