AI Article Synopsis
- - The distinction between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is crucial for effective cancer treatment and outcomes, highlighting the need for accurate diagnoses.
- - Traditional diagnosis methods are limited by human resource constraints, necessitating new techniques to handle large volumes of histopathological samples rapidly and accurately.
- - The study developed deep learning models that classify whole slide images of biopsies and surgical samples as DCIS, IDC, or benign, achieving high diagnostic performance with ROC areas under the curve reaching up to 0.977 for IDC.
Article Abstract
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.
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| http://dx.doi.org/10.1007/s00428-021-03241-z | DOI Listing |
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