ArticleAbdominal radiology (New York)2025
Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.
Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Knowledge mapping of ⁹⁰Y transarterial radioembolization combined with immunotherapy in hepatocellular carcinoma: a bibliometric analysis.Discover oncology · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.Journal of magnetic resonance imaging : JMRI · 2026Review
- A Multimodal MRI-Based Model for Colorectal Liver Metastasis Prediction: Integrating Radiomics, Deep Learning, and Clinical Features with SHAP Interpretation.Current oncology (Toronto, Ont.) · 2025Article
- A magnetic resonance imaging radiomics approach predicts hepatocellular carcinoma response to stereotactic body radiation therapy.Physics and imaging in radiation oncology · 2025Article
- Photon counting CT clinical adoption, integration, and workflow.Abdominal radiology (New York) · 2024Review
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Abstract
purposeTo 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.
methodsThis 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.
resultsA 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).
conclusionThe 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.
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