期刊
CLINICAL CANCER RESEARCH
卷 27, 期 10, 页码 2868-2878出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-20-4119
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资金
- Microsoft Azure
- NVIDIA GPU Grant Program
- Partners' Innovation Discovery Grant
- Schlager Family Award for Early Stage Digital Health Innovations
- Blavatnik Center for Computational Biomedicine Award
- Digital Imaging Facility, Department of Pathology, Brigham and Women's Hospital, Boston, MA
- Brigham and Women's Hospital
The study demonstrates that convolutional neural networks can accurately diagnose renal cancers, predict patients' genomic profiles and prognosis, and identify genomic variations, showcasing the potential for integrating diverse data modalities in clinical research.
Purpose: 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. Experimental Design: 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. Results: 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 P = 0.02). Our machine learning approaches further identified histopathology image features indicative of copy-number alterations (AUC > 0.7 in multiple genes in patients with ccRCC) and tumor mutation burden. Conclusions: 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.
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