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

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 () and Yunlang She ()
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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

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-42811-4

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