{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Huang KB"],"funding":["National Key Research and Development Program of China"],"pubmed_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 < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-cl"],"journal":["Nature communications"],"pagination":["6215"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11266571"],"repository":["biostudies-literature"],"pubmed_title":["A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma."],"pmcid":["PMC11266571"],"pubmed_authors":["Li XS","Zhao HW","Cao JZ","Liu RY","Cao M","Zhang J","Li CX","Liao B","Ouyang K","Xu YZ","Zhou FJ","Zhong WD","Dong BJ","Xiong SW","Xie D","Xue W","Han GP","Wu JT","Tang Q","Chen W","Cai MY","Huang KB","Gui CP","Huang YR","Pan YH","Hsieh JT","Jiang FN","Qu GM","Yu YF","Jiang ZY","Luo JH","Guo JP","Zhang XK","Liu ZP","Li PX","Wei JH","Cao Y","Han H","Chen YH","Chen WF","Feng ZH"],"additional_accession":[]},"is_claimable":false,"name":"A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.","description":"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 < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-cl","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-06-01T10:01:02.476Z","creation":"2025-04-19T13:11:24.304Z"},"accession":"S-EPMC11266571","cross_references":{"pubmed":["39043664"],"doi":["10.1038/s41467-024-50369-y"]}}