Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

PURPOSE: Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. MATERIALS AND METHODS: Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. RESULTS: The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. CONCLUSION: The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

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.