Downgrading BIRADS 3 to BIRADS 2 category using a computer-aided microcalcification analysis and risk assessment system for early breast cancer.

This paper explores the potential of a computer-aided diagnosis system to discriminate the real benign microcalcifications among a specific subset of 109 patients with BIRADS 3 mammograms who had undergone biopsy, thus making it possible to downgrade them to BIRADS 2 category. The system detected and quantified critical features of microcalcifications and classified them on a risk percentage scale for malignancy. The system successfully detected all cancers. Nevertheless, it suggested biopsy for 11/15 atypical lesions. Finally, the system characterized as definitely benign (BIRADS 2) 29/88 benign lesions, previously assigned to BIRADS 3, and thus achieved a reduction of 33% in unnecessary biopsies.

Design of a 'smart' patient record system for mammography patients.

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. Keeping an informed and complete patient record is of great importance as the doctor needs this information for every patient examination. The proposed implementation is a patient record system that includes 'smart' algorithms in order to automatically use data from the patient's record to calculate well established epidemiological breast cancer models. A computer-aided diagnosis system is also used in order to analyze each mammogram and obtain a certain risk percentage concerning whether the patient has to undergo biopsy or not. The aforementioned system has been implemented to support a large set of patient data (1,178 patients) which included detailed personal patient data, medical history and examination details per date and is used in the daily clinical practice of a breast cancer diagnostic center facilitating patient record retrieval, storage and supporting the doctor's decisions.

Evaluation of a breast cancer computer aided diagnosis system.

Mammography is accepted as the most effective method to detect breast cancer. However, interpreting a mammogram is not easy for not experienced radiologists. The aim of computer aided detection techniques in breast cancer is to improve the chance that a malignant region is detected and appropriately evaluated. Breast microcalcifications have been considered as a very useful index of malignancy, which helps in the early detection of breast cancer. A system of computer aided diagnosis has been developed that is based on detailed analysis and evaluation of related features of individual microcalcifications and of formed clusters helping the doctor to make risk estimation for each microcalcification cluster as well as for isolated microcalcifications. This information is considered to be very useful to radiologists, giving them extra input before making their estimation of each case. The aforementioned system has been thoroughly tested using a number of real life cases provided from collaborating doctors. Each case, apart from the mammograms, was accompanied by a biopsy test result, the patient's demographic data and medical history. A total of 200 cases (147 benign and 53 malignant) have been examined and the results are presented as the Receiver Operating Characteristic (ROC) performance and are quantified using the ROC curve. The system is showing high levels of sensitivity identifying correctly all malignant cases.

"Hippocrates-mst": a prototype for computer-aided microcalcification analysis and risk assessment for breast cancer.

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.