{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Blackledge MD"],"funding":["Cancer Research UK","Institute of Cancer Research, Royal Cancer Hospital","National Institute for Health Research (NIHR)"],"pagination":["941"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6795696"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9"],"pubmed_abstract":["<b>Background:</b> Multi-parametric MRI provides non-invasive methods for response assessment of soft-tissue sarcoma (STS) from non-surgical treatments. However, evaluation of MRI parameters over the whole tumor volume may not reveal the full extent of post-treatment changes as STS tumors are often highly heterogeneous, including cellular tumor, fat, necrosis, and cystic tissue compartments. In this pilot study, we investigate the use of machine-learning approaches to automatically delineate tissue compartments in STS, and use this approach to monitor post-radiotherapy changes. <b>Methods:</b> Eighteen patients with retroperitoneal sarcoma were imaged using multi-parametric MRI; 8/18 received a follow-up imaging study 2-4 weeks after pre-operative radiotherapy. Eight commonly-used supervis"],"journal":["Frontiers in oncology"],"pubmed_title":["Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric MRI of Soft-Tissue Sarcoma."],"pmcid":["PMC6795696"],"funding_grant_id":["16464","II-LA-0216-20007"],"pubmed_authors":["Collins DJ","Strauss D","Koh DM","Leach MO","Blackledge MD","Thway K","Winfield JM","Messiou C","Morgan VA","Miah A"],"additional_accession":[]},"is_claimable":false,"name":"Supervised Machine-Learning Enables Segmentation and Evaluation of Heterogeneous Post-treatment Changes in Multi-Parametric MRI of Soft-Tissue Sarcoma.","description":"<b>Background:</b> Multi-parametric MRI provides non-invasive methods for response assessment of soft-tissue sarcoma (STS) from non-surgical treatments. However, evaluation of MRI parameters over the whole tumor volume may not reveal the full extent of post-treatment changes as STS tumors are often highly heterogeneous, including cellular tumor, fat, necrosis, and cystic tissue compartments. In this pilot study, we investigate the use of machine-learning approaches to automatically delineate tissue compartments in STS, and use this approach to monitor post-radiotherapy changes. <b>Methods:</b> Eighteen patients with retroperitoneal sarcoma were imaged using multi-parametric MRI; 8/18 received a follow-up imaging study 2-4 weeks after pre-operative radiotherapy. Eight commonly-used supervis","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019","modification":"2026-05-06T21:31:54.271Z","creation":"2019-11-05T08:06:44Z"},"accession":"S-EPMC6795696","cross_references":{"pubmed":["31649872"],"doi":["10.3389/fonc.2019.00941"]}}