{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Milewski D"],"funding":["National Cancer Institute (NCI)","Intramural NIH HHS","National Cancer Institute","NCI NIH HHS"],"pagination":["364-378"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9843436"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["29(2)"],"pubmed_abstract":["Purpose
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.Experimental design
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 f"],"journal":["Clinical cancer research : an official journal of the American Association for Cancer Research"],"pubmed_title":["Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group."],"pmcid":["PMC9843436"],"funding_grant_id":["U10CA180886","U54CA231630","U10CA098413","P30CA008748","U10CA098543","U10 CA180899","ZIA BC011002","U10CA180899"],"pubmed_authors":["Barkauskas DA","Hawkins DS","Choo-Wosoba H","Milewski D","Ladanyi M","Brown GT","Shern JF","Steinberg SM","Liu Y","Zaki G","Bode PK","Hall D","Chou HC","Lisle C","Jung H","Rudzinski ER","Somerville B","Venkatramani R","Linardic CM","Skapek SX","Lo T","Khan J","Wei JS","Kuznetsov IB","Collins J"],"additional_accession":[]},"is_claimable":false,"name":"Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group.","description":"Purpose
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.Experimental design
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 f","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-18T15:44:57.439Z","creation":"2025-02-19T04:25:12.796Z"},"accession":"S-EPMC9843436","cross_references":{"pubmed":["36346688"],"doi":["10.1158/1078-0432.CCR-22-1663"]}}