Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.
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-en...
| Publicado en: | Abdominal Radiology Vol. 50; no. 5; pp. 2000 - 2012 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184452395&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184452395 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: May2025 vid: 50 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184452395 180453900 10.1007/s00261-024-04606-z 184452395 ppf: 2000 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach. aug: au: Stocker, Daniel Hectors, Stefanie Marinelli, Brett Carbonell, Guillermo Bane, Octavia Hulkower, Miriam Kennedy, Paul Ma, Weiping Lewis, Sara Kim, Edward Wang, Pei Taouli, Bachir affil: https://ror.org/04a9tmd77 BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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