Program Information

Mammogram Surveillance Using Texture Analysis for Breast Cancer Patients After Radiation Therapy

H Kuo

H Kuo¹*, W Tome'¹ , J FOX¹ , L Hong¹ , R Yaparpalvi¹ , K Mehta¹ , Y Huang² , W Bodner¹ , S Kalnicki¹ , (1) Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York, (2) Memorial Sloan-Kettering Cancer Center, Great Neck, NY

Presentations

TU-F-18C-9 Tuesday 4:30PM - 6:00PM Room: 18C

Purpose: To study the feasibility of applying cancer risk model established from treated patients to predict the risk of recurrence on follow-up mammography after radiation therapy for both ipsilateral and contralateral breast.

Methods: An extensive set of textural feature functions was applied to a set of 196 Mammograms from 50 patients. 56 Mammograms from 28 patients were used as training set, 44 mammograms from 22 patients were used as test set and the rest were used for prediction. Feature functions include Histogram, Gradient, Co-Occurrence Matrix, Run-Length Matrix and Wavelet Energy. An optimum subset of the feature functions was selected by Fisher Coefficient (FO) or Mutual Information (MI) (up to top 10 features) or a method combined FO, MI and Principal Component (FMP) (up to top 30 features). One-Nearest Neighbor (1-NN), Linear Discriminant Analysis (LDA) and Nonlinear Discriminant Analysis (NDA) were utilized to build a risk model of breast cancer from the training set of mammograms at the time of diagnosis. The risk model was then used to predict the risk of recurrence from mammogram taken one year and three years after RT.

Results: FPM with NDA has the best classification power in classifying the training set of the mammogram with lesions versus those without lesions. The model of FPM with NDA achieved a true positive (TP) rate of 82% compared to 45.5% of using FO with 1-NN. The best false positive (FP) rates were 0% and 3.6% in contra-lateral breast of 1-year and 3-years after RT, and 10.9% in ipsi-lateral breast of 3-years after RT.

Conclusion: Texture analysis offers high dimension to differentiate breast tissue in mammogram. Using NDA to classify mammogram with lesion from mammogram without lesion, it can achieve rather high TP and low FP in the surveillance of mammogram for patient with conservative surgery combined RT.


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