【Successful Development of Pathological AI】Now Possible to Classify Invasive/Non-invasive Breast Ductal Carcinoma - Published in Springer Nature -
Medmain Inc., a provider of PidPort digital pathology support solutions, has successfully developed a pathological AI that enables to distinguish invasive/non-invasive ductal carcinoma of the breast using deep learning. We are also pleased to announce that a paper on this development has been submitted to Virchows Archiv, issued by Springer Nature, and published on January 25, 2022.
The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pathologists using microscopes is limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately diagnose large numbers of histopathological specimens. Computational pathology tools which can assist pathologists in detecting and classifying DCIS and IDC from whole slide images (WSIs) would be of great benefit for routine pathological diagnosis. In this paper, we trained deep learning models capable of classifying biopsy and surgical histopathological WSIs into DCIS, IDC, and benign. We evaluated the models on two independent test sets (n= 1382, n= 548), achieving ROC areas under the curves (AUCs) up to 0.960 and 0.977 for DCIS and IDC, respectively.