基于脂肪酸增强MRI的放射素模型，用于术前风险预测和预后评估肝细胞癌
Gadoxetic Acid-Enhanced MRI-Based Radiomic Models for Preoperative Risk Prediction and Prognostic Assessment of Proliferative Hepatocellular Carcinoma
影响因子:3.90000
分区:医学2区 / 核医学2区
发表日期:2025 Jan
作者:
Zuyi Yan, Zixin Liu, Guodong Zhu, Mengtian Lu, Jiyun Zhang, Maotong Liu, Jifeng Jiang, Chunyan Gu, Xiaomeng Wu, Tao Zhang, Xueqin Zhang
摘要
增殖性肝细胞癌（HCC）与高侵入性和预后不良有关。这项研究旨在调查不同放射素学模型的术前风险预测和预后价值，并将其增殖性HCC的命名摄影患者随机分为训练队列（n = 156）和验证队列（n = 66）（n = 66），以7：3的比率为7：3。原始和三角洲（从两个不同阶段提取的成像特征之间的不同值）是从T1加权成像（T1WI），动脉和肝胆管相中提取的放射线特征，以使用不同的机器学习算法来构建模型。逻辑回归用于选择临床独立的风险因素。通过将最佳放射线模型分数与独立的风险因素整合在一起来构建命名图。评估了模型的诊断功效和临床实用性。随后，根据放射线模型的得分和列诺图评分将患者分为高风险和低风险亚组，并评估了无复发生存率（RFS）和总体存活率（OS）（OS）。多数逻辑回归分析表明，BCLC阶段和组合的RADSC阶段是独立的RADSC预测指标。在训练和验证队列中，拟合这些因素的曲线图（AUC）下的面积分别为0.838和0.801，具有良好的预测性能。 Multivariate Cox regression analysis shows that the delta radiomics model (DR)-predicted proliferative HCC can independently predict RFS and OS, with scores from the delta radiomics model performing best in prognostic risk stratification.The nomogram can effectively predict proliferative HCC, while different radiomics models and the nomogram can offer varying prognostic stratification values.
Abstract
Proliferative hepatocellular carcinoma (HCC) is associated with high invasiveness and poor prognosis. This study aimed to investigate the preoperative risk prediction and prognostic value of different radiomics models and a nomogram for proliferative HCC.Patients were randomly divided into a training cohort (n = 156) and a validation cohort (n = 66) in a 7:3 ratio. Original and delta (the different value between imaging features extracted from two different phases) radiomics features were extracted from T1-weighted imaging (T1WI), arterial, and hepatobiliary phases to construct models using different machine learning algorithms. Logistic regression was used to select clinical independent risk factors. A nomogram was constructed by integrating the optimal radiomics model score with independent risk factors. The diagnostic efficacy and clinical utility of the models were assessed. Subsequently, patients were stratified into high-risk and low-risk subgroups based on radiomics model scores and nomogram scores, and both recurrence-free survival (RFS) and overall survival (OS) were evaluated.Multivariate logistic regression analysis showed that BCLC stage and combined radscore were independent predictors of proliferative HCC. The area under the curve (AUC) of the nomogram incorporating these factors was 0.838 and 0.801 in the training and validation cohorts, respectively, with good predictive performance. Multivariate Cox regression analysis shows that the delta radiomics model (DR)-predicted proliferative HCC can independently predict RFS and OS, with scores from the delta radiomics model performing best in prognostic risk stratification.The nomogram can effectively predict proliferative HCC, while different radiomics models and the nomogram can offer varying prognostic stratification values.