Preliminary Feature Extraction for Small Lesion Classification in Sonomammographic Images

In breast cancer diagnosis, early detection of tumors and accurate differentiation of malignant and benign breast lesions are key demands. Tumor size, as a measure of tumor progression, is related to recurrence rate and patient survival. This study aims to determine which sonographic features allow the differentiation of small breast lesions into benign and malignant. Inclusion criteria for the analysis were tumors with the longest diameter of less than or equal to 10 mm and tumors with confirmed classification by follow-up care or core needle biopsy result. Following the criteria, 1515 cases were analyzed, including 365 carcinomas and 1150 benign lesions. To quantitatively evaluate the images, 383 ultrasound parameters (BI-RADS features, morphological features, fractal features, histogram features, and texture parameters) were used. Univariate and multiple logistic regression analyses were used to assess the significance of various diagnostic features and their combinations. The combined classifier (based on 19 quantitative features) yields an area under the ROC curve of 0.91.