Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study

Cheng Peng Gui, Yu Hang Chen, Hong Wei Zhao, Jia Zheng Cao, Tian Jie Liu, Sheng Wei Xiong, Yan Fei Yu, Bing Liao, Yun Cao, Jia Ying Li, Kang Bo Huang, Hui Han, Zhi Ling Zhang, Wen Fang Chen, Ze Ying Jiang, Ye Gao, Guan Peng Han, Qi Tang, Kui Ouyang, Gui Mei QuJi Tao Wu, Jian Ping Guo, Cai Xia Li, Pei Xing Li, Zhi Ping Liu, Jer Tsong Hsieh, Mu Yan Cai, Xue Song Li, Jin Huan Wei, Jun Hang Luo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Improved markers for predicting recurrence are needed to stratify patients with localised (stage I–III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities—clinical, genomic, and histopathological—to improve the predictive accuracy for localised renal cell carcinoma recurrence. Methods: In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI). Findings: The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825–0·876 vs 0·608–0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49–8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19–6·59; p<0·0001). Interpretation: Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy. Funding: National Natural Science Foundation of China, and National Key Research and Development Program of China.

Original languageEnglish (US)
Pages (from-to)e515-e524
JournalThe Lancet Digital Health
Volume5
Issue number8
DOIs
StatePublished - Aug 2023

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Decision Sciences (miscellaneous)
  • Health Information Management

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