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25.01.2022 | Original Article

A deep learning model for breast ductal carcinoma in situ classification in whole slide images

verfasst von: Fahdi Kanavati, Shin Ichihara, Masayuki Tsuneki

Erschienen in: Virchows Archiv | Ausgabe 5/2022

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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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Titel: A deep learning model for breast ductal carcinoma in situ classification in whole slide images
verfasst von: Fahdi Kanavati
Shin Ichihara
Masayuki Tsuneki
Publikationsdatum: 25.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Virchows Archiv / Ausgabe 5/2022
Print ISSN: 0945-6317
Elektronische ISSN: 1432-2307
DOI: https://doi.org/10.1007/s00428-021-03241-z

Neu im Fachgebiet Pathologie

Open Access 15.04.2024 | Biomarker | Schwerpunkt: Next Generation Pathology

Springer Medizin

A deep learning model for breast ductal carcinoma in situ classification in whole slide images

Abstract

Neu im Fachgebiet Pathologie

Molekularpathologische Untersuchungen im Wandel der Zeit

Vergleichende Pathologie in der onkologischen Forschung

Gastrointestinale Stromatumoren

Personalisierte Medizin in der Onkologie

Springer Medizin

Abstract

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