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Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings

Author

Listed:
  • Shih-Chiang Huang

    (Chang Gung University, College of Medicine
    Chang Gung University)

  • Chi-Chung Chen

    (aetherAI Co., Ltd.)

  • Jui Lan

    (Chang Gung University, College of Medicine)

  • Tsan-Yu Hsieh

    (Chang Gung University, College of Medicine)

  • Huei-Chieh Chuang

    (Chang Gung University, College of Medicine)

  • Meng-Yao Chien

    (aetherAI Co., Ltd.)

  • Tao-Sheng Ou

    (aetherAI Co., Ltd.)

  • Kuang-Hua Chen

    (Chang Gung University, College of Medicine)

  • Ren-Chin Wu

    (Chang Gung University, College of Medicine)

  • Yu-Jen Liu

    (Chang Gung University, College of Medicine)

  • Chi-Tung Cheng

    (Chang Gung University, College of Medicine)

  • Yu-Jen Huang

    (National Taiwan University)

  • Liang-Wei Tao

    (National Taiwan University)

  • An-Fong Hwu

    (National Taiwan University)

  • I-Chieh Lin

    (Chang Gung University, College of Medicine)

  • Shih-Hao Hung

    (National Taiwan University)

  • Chao-Yuan Yeh

    (aetherAI Co., Ltd.)

  • Tse-Ching Chen

    (Chang Gung University, College of Medicine)

Abstract

The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the development of a clinically applicable solution. We design an artificial-intelligence-assisted LN assessment workflow to facilitate the routine counting of metastatic LNs. Unlike previous patch-based approaches, our proposed method trains CNNs by using 5-gigapixel images, obviating the need for lesion-level annotations. Trained on 5907 LN images, our algorithm identifies metastatic LNs in gastric cancer with a slide-level area under the receiver operating characteristic curve (AUC) of 0.9936. Clinical experiments reveal that the workflow significantly improves the sensitivity of micrometastasis identification (81.94% to 95.83%, P

Suggested Citation

  • Shih-Chiang Huang & Chi-Chung Chen & Jui Lan & Tsan-Yu Hsieh & Huei-Chieh Chuang & Meng-Yao Chien & Tao-Sheng Ou & Kuang-Hua Chen & Ren-Chin Wu & Yu-Jen Liu & Chi-Tung Cheng & Yu-Jen Huang & Liang-Wei, 2022. "Deep neural network trained on gigapixel images improves lymph node metastasis detection in clinical settings," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30746-1
    DOI: 10.1038/s41467-022-30746-1
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    References listed on IDEAS

    as
    1. Chi-Long Chen & Chi-Chung Chen & Wei-Hsiang Yu & Szu-Hua Chen & Yu-Chan Chang & Tai-I Hsu & Michael Hsiao & Chao-Yuan Yeh & Cheng-Yu Chen, 2021. "An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Xiaodong Wang & Ying Chen & Yunshu Gao & Huiqing Zhang & Zehui Guan & Zhou Dong & Yuxuan Zheng & Jiarui Jiang & Haoqing Yang & Liming Wang & Xianming Huang & Lirong Ai & Wenlong Yu & Hongwei Li & Chan, 2021. "Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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