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Prognostic and predictive value of a pathomics signature in gastric cancer

Author

Listed:
  • Dexin Chen

    (Southern Medical University
    Jimei University)

  • Meiting Fu

    (Southern Medical University)

  • Liangjie Chi

    (Southern Medical University
    Teaching Hospital of Fujian Medical University)

  • Liyan Lin

    (Fujian Cancer Hospital)

  • Jiaxin Cheng

    (Southern Medical University)

  • Weisong Xue

    (Southern Medical University)

  • Chenyan Long

    (Southern Medical University)

  • Wei Jiang

    (Southern Medical University)

  • Xiaoyu Dong

    (Southern Medical University)

  • Jian Sui

    (Southern Medical University
    Teaching Hospital of Fujian Medical University)

  • Dajia Lin

    (Southern Medical University
    Teaching Hospital of Fujian Medical University)

  • Jianping Lu

    (Fujian Cancer Hospital)

  • Shuangmu Zhuo

    (Jimei University)

  • Side Liu

    (Southern Medical University)

  • Guoxin Li

    (Southern Medical University)

  • Gang Chen

    (Fujian Cancer Hospital)

  • Jun Yan

    (Southern Medical University)

Abstract

The current tumour-node-metastasis (TNM) staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer (GC). Pathomics, which is based on the development of digital pathology, is an emerging field that might improve clinical management. Herein, we propose a pathomics signature (PSGC) that is derived from multiple pathomics features of haematoxylin and eosin-stained slides. We find that the PSGC is an independent predictor of prognosis. A nomogram incorporating the PSGC and TNM staging system shows significantly improved accuracy in predicting the prognosis compared to the TNM staging system alone. Moreover, in stage II and III GC patients with a low PSGC (but not in those with a high PSGC), satisfactory chemotherapy benefits are observed. Therefore, the PSGC could serve as a prognostic predictor in patients with GC and might be a potential predictive indicator for decision-making regarding adjuvant chemotherapy.

Suggested Citation

  • Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34703-w
    DOI: 10.1038/s41467-022-34703-w
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    References listed on IDEAS

    as
    1. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    2. Kun-Hsing Yu & Ce Zhang & Gerald J. Berry & Russ B. Altman & Christopher Ré & Daniel L. Rubin & Michael Snyder, 2016. "Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
    3. Vickers, Andrew J, 2008. "Decision Analysis for the Evaluation of Diagnostic Tests, Prediction Models, and Molecular Markers," The American Statistician, American Statistical Association, vol. 62(4), pages 314-320.
    4. Torsten Hothorn & Achim Zeileis, 2008. "Generalized Maximally Selected Statistics," Biometrics, The International Biometric Society, vol. 64(4), pages 1263-1269, December.
    5. Chi Hyun Lee & Jing Ning & Yu Shen, 2019. "Model diagnostics for the proportional hazards model with length-biased data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 79-96, January.
    6. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
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    Cited by:

    1. Bao Feng & Jiangfeng Shi & Liebin Huang & Zhiqi Yang & Shi-Ting Feng & Jianpeng Li & Qinxian Chen & Huimin Xue & Xiangguang Chen & Cuixia Wan & Qinghui Hu & Enming Cui & Yehang Chen & Wansheng Long, 2024. "Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Yangzi Chen & Bohong Wang & Yizi Zhao & Xinxin Shao & Mingshuo Wang & Fuhai Ma & Laishou Yang & Meng Nie & Peng Jin & Ke Yao & Haibin Song & Shenghan Lou & Hang Wang & Tianshu Yang & Yantao Tian & Pen, 2024. "Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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