DATA-DRIVEN DIAGNOSTIC TOOLS FOR BREAST CANCER DETECTION IN CLINICAL DECISION SUPPORT SYSTEMS

Abstract

Computer-aided diagnosis (CADx) technology has demonstrated enhanced efficacy in breast cancer (BC) detection, which is particularly crucial during primary care physicians' initial evaluation of patients. The work aims to develop novel, user-friendly auxiliary computer aids accessible directly to frontline medical practitioners, eliminating the need for costly computer systems. The scientific novelty lies in the fact that we have devised a nonrelational database (DB) of factual data designed to house the results of patient studies, which can be harnessed for machine learning in computer-aided BC diagnosis systems. The DB encompasses a heterogeneous vector of primary measurements (metadata, DICOM standard files, alongside other images and data) for each patient, facilitating the construction of a neural network for tumor recognition and preliminary classification. We have populated the database with new, region-specific data pertinent to women in Ukraine amidst severe stress induced by the ongoing war. Additionally, we have developed a new system for concurrent monitoring of ultrasound, computed tomography, and mammography results, complemented by a decision support system for simultaneous cross-verification of neoplasm diagnoses based on density and spatial correlation.