AI Summary & Key Takeaways
AI InsightRESULTS The radiomics signature stratified patients into low-risk and high-risk, which showed significant difference in recurrence-free survival and overall survival (P ≤ 0.01).
📌 3 Key Takeaways
Özet
PURPOSE The aim of this study was to develop and validate a radiomics nomogram based on radiomics features and clinical data for the noninvasive preoperative prediction of early recurrence (≤2 years) in patients with hepatocellular carcinoma (HCC). METHODS We enrolled 262 HCC patients who underwent preoperative contrast-enhanced computed tomography (CT) and curative resection (training cohort, n=214; validation cohort, n=48). We applied propensity score matching (PSM) to eliminate redundancy between clinical characteristics and image features, and the least absolute shrinkage and selection operator (LASSO) was used to prevent overfitting. Next, a radiomics signature, clinical nomogram, and combined clinical-radiomics nomogram were built to predict early recurrence, and we compared the performance and generalization of these models. RESULTS The radiomics signature stratified patients into low-risk and high-risk, which showed significant difference in recurrence-free survival and overall survival (P ≤ 0.01). Multivariable analysis identified dichotomized radiomics signature, alpha-fetoprotein, and tumor number and size as key early recurrence indicators, which were incorporated into clinical and radiomics nomograms. The radiomics nomogram showed the highest area under the receiver operating characteristic curve (AUC), with significantly superior predictive performance over the clinical nomogram in the training cohort (0.800 vs. 0.716, respectively; P = 0.001) and the validation cohort (0.785 vs. 0.654, respectively; P = 0.039). CONCLUSION The radiomics nomogram is a noninvasive preoperative biomarker for predicting early recurrence in patients with HCC. This model may be of clinical utility for guiding surveillance follow-ups and identifying optimal interventional strategies.