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A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.
- Source :
-
Nature communications [Nat Commun] 2024 Jul 23; Vol. 15 (1), pp. 6215. Date of Electronic Publication: 2024 Jul 23. - Publication Year :
- 2024
-
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, pā<ā0.05). 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 (pā<ā0.05). 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.<br /> (© 2024. The Author(s).)
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 2041-1723
- Volume :
- 15
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Nature communications
- Publication Type :
- Academic Journal
- Accession number :
- 39043664
- Full Text :
- https://doi.org/10.1038/s41467-024-50369-y