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Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach

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dc.contributor.author Stocker, Daniel
dc.contributor.author Hectors, Stefanie
dc.contributor.author Marinelli, Brett
dc.contributor.author Carbonell-López-del-Castillo, Guillermo
dc.contributor.author Bane, Octavia
dc.contributor.author Hulkower, Miriam
dc.contributor.author Kennedy, Paul
dc.contributor.author Ma, Weiping
dc.contributor.author Lewis, Sara
dc.contributor.author Kim, Edward
dc.contributor.author Wang, Pei
dc.contributor.author Taouli, Bachir
dc.date.accessioned 2026-03-06T14:05:01Z
dc.date.available 2026-03-06T14:05:01Z
dc.date.issued 2024-10-26
dc.identifier.citation Stocker D, Hectors S, Marinelli B, Carbonell G, Bane O, Hulkower M, et al. Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach. Abdom Radiol. 26 de octubre de 2024;50(5):2000-11. doi:10.1007/s00261-024-04606-z
dc.identifier.issn 2366-004X
dc.identifier.uri https://sms.carm.es/ricsmur/handle/123456789/24615
dc.description.abstract PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy. METHODS: This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6 months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6 months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6 months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests. RESULTS: A total 168 HCCs (mean size 2.9 ± 1.7 cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001). CONCLUSION: The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.
dc.language.iso eng
dc.publisher SPRINGER
dc.rights Atribución/Reconocimiento 4.0 Internacional
dc.rights.uri https://creativecommons.org/licenses/by/4.0/deed.es
dc.subject.mesh Humans
dc.subject.mesh Female
dc.subject.mesh Carcinoma, Hepatocellular/radiotherapy/diagnostic imaging/surgery
dc.subject.mesh Liver Neoplasms/radiotherapy/diagnostic imaging/surgery
dc.subject.mesh Aged
dc.subject.mesh Magnetic Resonance Imaging/methods
dc.subject.mesh Male
dc.subject.mesh Retrospective Studies
dc.subject.mesh Machine Learning
dc.subject.mesh Contrast Media
dc.subject.mesh Treatment Outcome
dc.subject.mesh Middle Aged
dc.subject.mesh Yttrium Radioisotopes/therapeutic use
dc.subject.mesh Predictive Value of Tests
dc.title Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach
dc.type info:eu-repo/semantics/article
dc.identifier.pmid 39460801
dc.relation.publisherversion https://link.springer.com/10.1007/s00261-024-04606-z
dc.type.version info:eu-repo/semantics/publishedVersion
dc.identifier.doi 10.1007/s00261-024-04606-z
dc.journal.title Abdominal Radiology
dc.identifier.essn 2366-0058


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