Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction

Rhabdomyosarcoma (RMS), the most common malignant soft tissue tumor in children, has several histologic subtypes that influence treatment and predict patient outcomes. Assistance with histologic classification for pathologists as well as discovery of optimized predictive biomarkers is needed. A convolutional neural network for RMS histology subtype classification was developed using digitized pathology images from 80 patients collected at time of diagnosis. A subsequent embryonal rhabdomyosarcoma (eRMS) prognostic model was also developed in a cohort of 60 eRMS patients. The RMS classification model reached a performance of an area under the receiver operating curve of 0.94 for alveolar rhabdomyosarcoma and an area under the receiver operating curve of 0.92 for eRMS at slide level in the test data set (n = 192). The eRMS prognosis model separated the patients into predicted high-and low-risk groups with significantly different event-free survival outcome (likelihood ratio test; P = 0.02) in the test data set (n = 136). The predicted risk group is significantly associated with patient event-free survival outcome after adjusting for patient age and sex (predicted high-versus low risk group hazard ratio, 4.64; 95% CI, 1.05-20.57; P = 0.04). This is the first comprehensive study to develop computational algorithms for subtype classification and prognosis prediction for RMS histopathology images. Such models can aid pathology evaluation and provide additional parameters for risk stratification.

Automated summary

This study developed a computational algorithm for subtype classification and prognosis prediction of rhabdomyosarcoma (RMS) using pathology images. The algorithm achieved good performance in classification and prediction on the test dataset, providing assistance in pathology evaluation and risk stratification.