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

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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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Acknowledgements

We are grateful for the support provided by Professor Takayuki Shiomi at Department of Pathology, Faculty of Medicine, International University of Health and Welfare; Dr. Ryosuke Matsuoka at Diagnostic Pathology Center, International University of Health and Welfare, Mita Hospital. We thank pathologists and oncologists who have been engaged in reviewing cases and clinicopathological discussion for this study.

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    Authors and Affiliations

    1. Medmain Research, Medmain Inc., 2-4-5-104, Akasaka, Chuo-ku, Fukuoka, 810-0042, Fukuoka, Japan

      Fahdi Kanavati & Masayuki Tsuneki

    2. Department of Surgical Pathology, Sapporo Kosei General Hospital, 8-5 Kita-3-jo Higashi, Chuo-ku, Sapporo, 060-0033, Hokkaido, Japan

      Shin Ichihara

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    1. Fahdi Kanavati
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    Correspondence to Masayuki Tsuneki.

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    The experimental protocol was approved by the ethical board of the Sapporo-Kosei General Hospital (No. 580) and International University of Health and Welfare (No. 19-Im-007). All research activities complied with all relevant ethical regulations and were performed in accordance with relevant guidelines and regulations in the all hospitals mentioned above. Informed consent to use histopathological samples and pathological diagnostic reports for research purposes had previously been obtained from all patients prior to the surgical procedures at all hospitals, and the opportunity for refusal to participate in research had been guaranteed by an opt-out manner.

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    F.K. and M.T. are employees of Medmain Inc. All authors declare no competing interests.

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    F.K., S.I., and M.T. contributed equally to this study; F.K. and M.T. designed the studies; F.K., S.I., and M.T. performed experiments and analyzed the data; S.I. performed pathological diagnoses and reviewed cases; F.K. and M.T. performed computational studies; F.K., S.I., and M.T. wrote the manuscript; M.T. supervised the project. All authors reviewed and approved the final manuscript.

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    Fahdi Kanavati, Shin Ichihara and Masayuki Tsuneki contributed equally to this work.

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    Kanavati, F., Ichihara, S. & Tsuneki, M. A deep learning model for breast ductal carcinoma in situ classification in whole slide images. Virchows Arch 480, 1009–1022 (2022). https://doi.org/10.1007/s00428-021-03241-z

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