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A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma

Author

Listed:
  • Kang-Bo Huang

    (Sun Yat-sen University
    Sun Yat-sen University Cancer center
    Sun Yat-sen University Cancer center)

  • Cheng-Peng Gui

    (Sun Yat-sen University)

  • Yun-Ze Xu

    (Shanghai Jiao Tong University)

  • Xue-Song Li

    (Peking University, National Urological Cancer Center)

  • Hong-Wei Zhao

    (Qingdao University)

  • Jia-Zheng Cao

    (Sun Yat-sen University)

  • Yu-Hang Chen

    (Sun Yat-sen University)

  • Yi-Hui Pan

    (The Third Affiliated Hospital of Soochow University)

  • Bing Liao

    (Sun Yat-sen University)

  • Yun Cao

    (Sun Yat-sen University Cancer center
    Sun Yat-sen University Cancer center)

  • Xin-Ke Zhang

    (Sun Yat-sen University Cancer center
    Sun Yat-sen University Cancer center)

  • Hui Han

    (Sun Yat-sen University Cancer center
    Sun Yat-sen University Cancer center)

  • Fang-Jian Zhou

    (Sun Yat-sen University Cancer center
    Sun Yat-sen University Cancer center)

  • Ran-Yi Liu

    (Sun Yat-sen University Cancer center)

  • Wen-Fang Chen

    (Sun Yat-sen University)

  • Ze-Ying Jiang

    (Sun Yat-sen University)

  • Zi-Hao Feng

    (Sun Yat-sen University)

  • Fu-Neng Jiang

    (South China University of Technology)

  • Yan-Fei Yu

    (Peking University, National Urological Cancer Center)

  • Sheng-Wei Xiong

    (Peking University, National Urological Cancer Center)

  • Guan-Peng Han

    (Peking University, National Urological Cancer Center)

  • Qi Tang

    (Peking University, National Urological Cancer Center)

  • Kui Ouyang

    (Qingdao University)

  • Gui-Mei Qu

    (Qingdao University)

  • Ji-Tao Wu

    (Qingdao University)

  • Ming Cao

    (Shanghai Jiao Tong University)

  • Bai-Jun Dong

    (Shanghai Jiao Tong University)

  • Yi-Ran Huang

    (Shanghai Jiao Tong University)

  • Jin Zhang

    (Shanghai Jiao Tong University)

  • Cai-Xia Li

    (Sun Yat-sen University)

  • Pei-Xing Li

    (Sun Yat-sen University)

  • Wei Chen

    (Sun Yat-sen University)

  • Wei-De Zhong

    (South China University of Technology)

  • Jian-Ping Guo

    (Sun Yat-sen University)

  • Zhi-Ping Liu

    (University of Texas Southwestern Medical Center at Dallas)

  • Jer-Tsong Hsieh

    (University of Texas Southwestern Medical Center at Dallas)

  • Dan Xie

    (Sun Yat-sen University Cancer center
    Sun Yat-sen University Cancer center)

  • Mu-Yan Cai

    (Sun Yat-sen University Cancer center
    Sun Yat-sen University Cancer center)

  • Wei Xue

    (Shanghai Jiao Tong University)

  • Jin-Huan Wei

    (Sun Yat-sen University)

  • Jun-Hang Luo

    (Sun Yat-sen University
    Sun Yat-sen University)

Abstract

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I–III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p

Suggested Citation

  • Kang-Bo Huang & Cheng-Peng Gui & Yun-Ze Xu & Xue-Song Li & Hong-Wei Zhao & Jia-Zheng Cao & Yu-Hang Chen & Yi-Hui Pan & Bing Liao & Yun Cao & Xin-Ke Zhang & Hui Han & Fang-Jian Zhou & Ran-Yi Liu & Wen-, 2024. "A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50369-y
    DOI: 10.1038/s41467-024-50369-y
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    References listed on IDEAS

    as
    1. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Ming Y. Lu & Tiffany Y. Chen & Drew F. K. Williamson & Melissa Zhao & Maha Shady & Jana Lipkova & Faisal Mahmood, 2021. "AI-based pathology predicts origins for cancers of unknown primary," Nature, Nature, vol. 594(7861), pages 106-110, June.
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