Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

Eun Kyung Kim, Hyo Eun Kim, Kyunghwa Han, Bong Joo Kang, Yu Mee Sohn, Ok Hee Woo, Chan Wha Lee

    Research output: Contribution to journalArticlepeer-review

    35 Citations (Scopus)

    Abstract

    We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer.

    Original languageEnglish
    Article number2762
    JournalScientific reports
    Volume8
    Issue number1
    DOIs
    Publication statusPublished - 2018 Dec 1

    ASJC Scopus subject areas

    • General

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