Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma.
Aged
Biomarkers, Tumor
Carcinoma, Renal Cell
/ diagnosis
Computational Biology
/ methods
Female
Humans
Image Processing, Computer-Assisted
Immunohistochemistry
Kidney Neoplasms
/ diagnosis
Machine Learning
Male
Middle Aged
Mutation
Neoplasm Grading
/ methods
Neoplasm Staging
/ methods
Neural Networks, Computer
Prognosis
ROC Curve
Journal
Clinical cancer research : an official journal of the American Association for Cancer Research
ISSN: 1557-3265
Titre abrégé: Clin Cancer Res
Pays: United States
ID NLM: 9502500
Informations de publication
Date de publication:
15 05 2021
15 05 2021
Historique:
received:
19
10
2020
revised:
25
01
2021
accepted:
10
03
2021
pubmed:
17
3
2021
medline:
17
3
2022
entrez:
16
3
2021
Statut:
ppublish
Résumé
Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles. To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women's Hospital (Boston, MA) to develop computational methods for integrating data analyses. Leveraging these large and diverse datasets, we developed fully automated convolutional neural networks to diagnose renal cancers and connect quantitative pathology patterns with patients' genomic profiles and prognoses. Our deep convolutional neural networks successfully detected malignancy (AUC in the independent validation cohort: 0.964-0.985), diagnosed RCC histologic subtypes (independent validation AUCs of the best models: 0.953-0.993), and predicted stage I ccRCC patients' survival outcomes (log-rank test Our results suggest that convolutional neural networks can extract histologic signals predictive of patients' diagnoses, prognoses, and genomic variations of clinical importance. Our approaches can systematically identify previously unknown relations among diverse data modalities.
Identifiants
pubmed: 33722896
pii: 1078-0432.CCR-20-4119
doi: 10.1158/1078-0432.CCR-20-4119
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2868-2878Informations de copyright
©2021 American Association for Cancer Research.
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