1. A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma
- Author
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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-Fang Chen, Ze-Ying Jiang, Zi-Hao Feng, Fu-Neng Jiang, Yan-Fei Yu, Sheng-Wei Xiong, Guan-Peng Han, Qi Tang, Kui Ouyang, Gui-Mei Qu, Ji-Tao Wu, Ming Cao, Bai-Jun Dong, Yi-Ran Huang, Jin Zhang, Cai-Xia Li, Pei-Xing Li, Wei Chen, Wei-De Zhong, Jian-Ping Guo, Zhi-Ping Liu, Jer-Tsong Hsieh, Dan Xie, Mu-Yan Cai, Wei Xue, Jin-Huan Wei, and Jun-Hang Luo
- Subjects
Science - Abstract
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
- Published
- 2024
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