Fractal analysis based on Gd-EOB-DTPA-enhanced MRI for prediction of vessels that encapsulate tumor clusters in patients with hepatocellular carcinoma

作者：Feng Che, Feifei Gao, Qian Li, Wei Ren, Hehan Tang, Guli Zaina, Xin Zhang, Shan Yao, Ning Zhang, Shaocheng Zhu, Bin Song, Yi Wei · 发表于：International Journal of Surgery · 年份：2025 · DOI：10.1097/js9.0000000000002547 · 被引用次数：9 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、MRI in cancer diagnosis、Radiomics and Machine Learning in Medical Imaging

OBJECTIVE: The aim of this study was to assess the potential role of fractal analysis derived from Gd-EOB-DTPA-enhanced MRI in predicting vessels that encapsulate tumor clusters (VETC) in patients with hepatocellular carcinoma (HCC). METHODS: This retrospective study included 505 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI before surgical resection at two medical centers (training set: 253 patients, internal test set: 108 patients, external test set: 144 patients). The fractal dimension (FD) and lacunarity were extracted from the hepatobiliary phase of the tumor using box-counting algorithms. Additionally, conventional imaging features were evaluated. Univariate and multivariate logistic regression analyses were conducted in the training set to identify independent predictors for VETC, and a nomogram was created to visualize the final predictive model. The performance of these models was tested in the internal and external test sets. Recurrence-free survival (RFS) and overall survival (OS) were analyzed using the Kaplan-Meier method along with the log-rank test. RESULTS: VETC-positive HCC exhibited higher FD and lacunarity than VETC-negative HCC ( P < 0 .001). The FD-lacunarity model achieved an area under receiver operating characteristics curve (AUC) of 0.78 (95% confidence interval [CI]: 0.70-0.87) in the internal test set and 0.79 (95%CI: 0.70-0.86) in the external test set. Multivariate logistic regression analysis identified serum alpha-fetoprotein, tumor s...