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PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer

Author

Listed:
  • Yifan Zhong

    (Tongji University School of Medicine)

  • Chuang Cai

    (Jiangsu University)

  • Tao Chen

    (Tongji University School of Medicine)

  • Hao Gui

    (Tsinghua University)

  • Jiajun Deng

    (Tongji University School of Medicine)

  • Minglei Yang

    (Chinese Academy of Sciences)

  • Bentong Yu

    (The First Affiliated Hospital of Nanchang University)

  • Yongxiang Song

    (Affiliated Hospital of Zunyi Medical University)

  • Tingting Wang

    (Fudan University)

  • Xiwen Sun

    (Tongji University School of Medicine)

  • Jingyun Shi

    (Tongji University School of Medicine)

  • Yangchun Chen

    (Tongji University School of Medicine)

  • Dong Xie

    (Tongji University School of Medicine)

  • Chang Chen

    (Tongji University School of Medicine)

  • Yunlang She

    (Tongji University School of Medicine)

Abstract

Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.

Suggested Citation

  • Yifan Zhong & Chuang Cai & Tao Chen & Hao Gui & Jiajun Deng & Minglei Yang & Bentong Yu & Yongxiang Song & Tingting Wang & Xiwen Sun & Jingyun Shi & Yangchun Chen & Dong Xie & Chang Chen & Yunlang She, 2023. "PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42811-4
    DOI: 10.1038/s41467-023-42811-4
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Zhongkai Liu & Howard D. Bondell, 2019. "Binormal Precision–Recall Curves for Optimal Classification of Imbalanced Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 141-161, April.
    3. Cheng Jin & Heng Yu & Jia Ke & Peirong Ding & Yongju Yi & Xiaofeng Jiang & Xin Duan & Jinghua Tang & Daniel T. Chang & Xiaojian Wu & Feng Gao & Ruijiang Li, 2021. "Predicting treatment response from longitudinal images using multi-task deep learning," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
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