1. A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.
- Author
-
Huang KB, Gui CP, Xu YZ, Li XS, Zhao HW, Cao JZ, Chen YH, Pan YH, Liao B, Cao Y, Zhang XK, Han H, Zhou FJ, Liu RY, Chen WF, Jiang ZY, Feng ZH, Jiang FN, Yu YF, Xiong SW, Han GP, Tang Q, Ouyang K, Qu GM, Wu JT, Cao M, Dong BJ, Huang YR, Zhang J, Li CX, Li PX, Chen W, Zhong WD, Guo JP, Liu ZP, Hsieh JT, Xie D, Cai MY, Xue W, Wei JH, and Luo JH
- Subjects
- Humans, Male, Female, Middle Aged, Aged, Prognosis, Genomics methods, Adult, Neoplasm Staging, Deep Learning, Disease-Free Survival, Carcinoma, Renal Cell genetics, Carcinoma, Renal Cell pathology, Kidney Neoplasms genetics, Kidney Neoplasms pathology, Kidney Neoplasms surgery, Neoplasm Recurrence, Local genetics
- 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-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy., (© 2024. The Author(s).)
- Published
- 2024
- Full Text
- View/download PDF