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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 |