ROC analysis of lesion descriptors in breast ultrasound images

Michael P. Andre; Michael Galperin; Peter Phan M.D.; Peter Chiu

doi:10.1117/12.480333

22 May 2003 ROC analysis of lesion descriptors in breast ultrasound images

Michael P. Andre, Michael Galperin, Peter Phan M.D., Peter Chiu

Proceedings Volume 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment; (2003) https://doi.org/10.1117/12.480333
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

Breast biopsy serves as the key diagnostic tool in the evaluation of breast masses for malignancy, yet the procedure affects patients physically and emotionally and may obscure results of future mammograms. Studies show that high quality ultrasound can distinguish a benign from malignant lesions with accuracy, however, it has proven difficult to teach and clinical results are highly variable. The purpose of this study is to develop a means to optimize an automated Computer Aided Imaging System (CAIS) to assess Level of Suspicion (LOS) of a breast mass. We examine the contribution of 15 object features to lesion classification by calculating the Wilcoxon area under the ROC curve, A_W, for all combinations in a set of 146 masses with known findings. For each interval A, the frequency of appearance of each feature and its combinations with others was computed as a means to find an “optimum” feature vector. The original set of 15 was reduced to 6 (area, perimeter, diameter ferret Y, relief, homogeneity, average energy) with an improvement from A_w=0.82∓0.04 for the original 15 to A_w=0.93∓0.02 for the subset of 6, p=0.03. For comparison, two sub-specialty mammography radiologists also scored the images for LOS resulting in A_z of 0.90 and 0.87. The CAIS performed significantly higher, p=0.02.

Citation Download Citation

Michael P. Andre, Michael Galperin, Peter Phan M.D., and Peter Chiu "ROC analysis of lesion descriptors in breast ultrasound images", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); https://doi.org/10.1117/12.480333

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