biostudies-literatureMilewski DNational Cancer Institute (NCI)Intramural NIH HHSNational Cancer InstituteNCI NIH HHS364-378https://www.ebi.ac.uk/biostudies/studies/S-EPMC9843436biostudies-literatureUnknown29(2)<h4>Purpose</h4>Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS, which strongly correlated with survival; however, predicting these mutations or high-risk disease at diagnosis remains a significant challenge. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS.<h4>Experimental design</h4>Digital whole slide H&E images were collected from clinically annotated diagnostic tumor samples from 321 patients with RMS enrolled in Children's Oncology Group (COG) trials (1998-2017). Patches were extracted and fed into deep learning CNNs to learn fClinical cancer research : an official journal of the American Association for Cancer ResearchPredicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group.PMC9843436U10CA180886U54CA231630U10CA098413P30CA008748U10CA098543U10 CA180899ZIA BC011002U10CA180899Barkauskas DAHawkins DSChoo-Wosoba HMilewski DLadanyi MBrown GTShern JFSteinberg SMLiu YZaki GBode PKHall DChou HCLisle CJung HRudzinski ERSomerville BVenkatramani RLinardic CMSkapek SXLo TKhan JWei JSKuznetsov IBCollins JfalsePredicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group.<h4>Purpose</h4>Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS, which strongly correlated with survival; however, predicting these mutations or high-risk disease at diagnosis remains a significant challenge. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS.<h4>Experimental design</h4>Digital whole slide H&E images were collected from clinically annotated diagnostic tumor samples from 321 patients with RMS enrolled in Children's Oncology Group (COG) trials (1998-2017). Patches were extracted and fed into deep learning CNNs to learn f2023-01-01T00:00:00Z2023 Jan2025-04-18T15:44:57.439Z2025-02-19T04:25:12.796ZS-EPMC98434363634668810.1158/1078-0432.CCR-22-1663