Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma.
Eliana MarosticaRebecca BarberThomas DenizeIsaac S KohaneSabina SignorettiJeffrey A GoldenKun-Hsing YuPublished in: Clinical cancer research : an official journal of the American Association for Cancer Research (2021)
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.
Keyphrases
- convolutional neural network
- deep learning
- end stage renal disease
- renal cell carcinoma
- chronic kidney disease
- newly diagnosed
- ejection fraction
- electronic health record
- machine learning
- big data
- prognostic factors
- peritoneal dialysis
- copy number
- patient reported outcomes
- gene expression
- dna methylation
- artificial intelligence
- patient reported
- anti inflammatory