Panos A. Ligomenides

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.

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.

e-Prolipsis: A web based risk estimation platform to support and register breast cancer diagnosis in Greece

Breast cancer diagnosis requires specific expertise from the Medical Doctors. Furthermore, prognosis, monitoring and early detection of malignant findings can be successfully realized through synergies between physicians, researchers and general population in concert with the health policy program of each country. Information technology and computational intelligence play a crucial role in the production of digital repositories and cancer registries as well as in the development of systems to support diagnosis. In this paper we present the concept and the architecture of an approved grant under the National Strategic Reference Framework 2007-2013 (NSRF), called e-Prolipsis. Through this project, we will design and implement a web based risk estimation platform and a Central Breast Cancer Registry (CBCR) that will co-operate to provide medical doctors and patients with services such as multiplicity in diagnostic opinions, synergy, computational risk estimation, access to statistical/epidemiological data for trend estimation. Such a system, will serve as a reference tool and will help the clinician, the radiologist, the rural doctor or trainee doctor in the final assessment on the existence or likelihood of breast cancer. The system will assist in the successful diagnosis of breast cancer, giving each patient access to a large pool of doctors and at the same time, the data stored in the CBCR will be used for statistical analysis, providing useful results for improving both the cancer detection application and for making a national policy for combating breast cancer. This system will be accessible through a Web Portal, with different access levels for patients, doctors and general public and different web services available to each user group, eliminating the geographical distances that would be imposed otherwise.

Generation of clustered microcalcifications' atlases for benign and malignant cases

Breast microcalcifications are one of the most important mammographic findings related to the existence of the breast cancer. Radiologists usually characterize microcalcifications based on their morphologies, the distribution within the cluster they form, the shape of the cluster and its relative location inside the breast. In this study, we focus on the latter factor and we study its effect on the probability of malignancy. The main purpose of our study is to generate probabilistic breast cancer atlases for clusters of microcalcifications in order to visualize the influence of cluster location on cancer probability. We propose a framework for the generation of such atlases, including segmentation of important breast landmarks and projection of different clusters of microcalcifications on a reference breast shape. The generation of the atlases is implemented using mammograms from the Digital Database of Screening Mammography. The obtained probabilistic atlases reveal specific areas in the breast of higher occurrence of clusters and higher risk of malignancy.

Performance evaluation of clustering algorithms on microcalcifications as mammography findings

Breast cancer can be prevented with regular mammography screening. Yet, the incorporation of Computational Intelligence relies on training classifiers on a set of predefined Regions of Interest (ROIs). Data Clustering has been applied to address the problem of ROI detection, yet no extensive research has been carried out on which algorithm to utilize. This contribution focuses on microcalcification clustering as a Data Clustering application, giving insights concerning the performance of three main clustering algorithms.