No abstract

A series of 151 women underwent 156 preoperative localizations of nonpalpable, mammographically detected breast lesions. Indications for biopsy were (1) a cluster of more than five fine microcalcifications; (2) a solid lump found by ultrasound investigation; and (3) a radiologic abnormality of the breast parenchyma. The lesions were localized preoperatively using the hook-wire method (Frank needle), and all biopsies were performed under general anesthesia. Carcinoma was discovered in 34 (21.8%) cases; in 22 (64.7%) it was a noninvasive cancer (9 with microinvasions) and in 12 (35.3%) an invasive carcinoma with a mean tumor diameter of 0.8 cm. The highest malignancy rate was found among those with microcalcifications (21 of 81 cancers, or 25.9%). Lymph node involvement was seen in 25% of patients with invasive carcinomas. In conclusion, the needle localization of nonpalpable breast lesions is a simple, accurate method for early detection of small cancers with favorable prognosis.

One of the most common cancer types among women is breast cancer. Regular mammographic examinations increase the possibility for early diagnosis and treatment and significantly improve the chance of survival for patients with breast cancer. Clustered microcalcifications have been considered as important indicators of the presence of breast cancer. We present "Hippocrates-mst", a prototype system for computer-aided risk assessment of breast cancer. Our research has been focused in developing software to locate microcalcifications on X-ray mammography images, quantify their critical features and classify them according to their probability of being cancerous. A total of 260 cases (187 benign and 73 malignant) have been examined and the performance of the prototype is presented through receiver operating characteristic (ROC) analysis. The system is showing high levels of sensitivity identifying correctly 98.63% of malignant cases.

Our study showed that computer analysis achieved statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications.

Our study showed that computer analysis achieved statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications.