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.
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).
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).
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.
National Natural Science Foundation of China, and National Key Research and Development Program of China.
The Lancet. Digital health. 2023 Jun 29 [Epub ahead of print]
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 Qu, Ji-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
Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China., Department of Urology, Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, China., Department of Urology, Jiangmen Central Hospital, Jiangmen, China., Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China., Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center, Beijing, China., Department of Pathology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China., Department of Pathology, Cancer Center, Sun Yat-sen University, Guangzhou, China., Department of Urology, Cancer Center, Sun Yat-sen University, Guangzhou, China., Department of Pathology, Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, China., Institute of Precision Medicine, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China., School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, China., Department of Internal Medicine and Department of Molecular Biology, University of Texas Southwestern Medical Center at Dallas, Dallas TX, USA., Department of Urology, University of Texas Southwestern Medical Center at Dallas, Dallas TX, USA., Department of Urology, Peking University First Hospital, Institute of Urology, Peking University, National Urological Cancer Center, Beijing, China. Electronic address: ., Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address: ., Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; Institute of Precision Medicine, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address: .