■这项研究的目的是开发一种综合模型,该模型结合了基于gadoxetic酸增强MRI的临床放射学和影像组学特征,用于术前评估肝细胞癌(HCC)中血管包裹肿瘤簇(VETC)模式。
■这项回顾性研究涵盖了234例接受手术切除的患者。其中,101例患者表现为VETC阳性HCC,而133例患者显示VETC阴性HCC。在动脉期(AP)中手动描绘整个肿瘤区域的感兴趣体积,入口阶段(PP),和肝胆相(HBP)图像。通过最小绝对收缩和选择算子(LASSO)回归和多变量逻辑回归分析确定VETC的独立预测因子。利用影像组学-AP,PP,HBP,连同24个影像学特征和19个临床特征。随后,临床放射学模型,影像组学模型,并建立了集成模型,使用列线图可视化集成模型。使用接收器工作特性曲线评估VETC预测的性能。
■集成模型,由3个选定的传统影像学特征(坏死或严重缺血[OR=2.457],外围冲洗[OR=1.678],LLR_AP(病变与肝脏比率_AP)[OR=0.433]和影像组学-AP[OR=2.870],影像组学-HBP[OR=2.023],影像组学-PP[OR=1.546]),在训练(AUC=0.873,95%置信区间[CI]:0.821-0.925)和验证(AUC=0.869,95%CI:0.789-0.950)队列中都显示出预测VETC模式的良好准确性。
■这项研究建立了一个集成模型,该模型结合了传统的成像特征和来自gadoxetic酸增强MRI的放射学特征,在预测VETC模式方面表现良好。
UNASSIGNED: The aim of this study was to develop an integrated model that combines clinical-radiologic and radiomics features based on gadoxetic acid-enhanced MRI for preoperative evaluating of vessels encapsulating tumour clusters (VETC) patterns in hepatocellular carcinoma (HCC).
UNASSIGNED: This retrospective study encompassed 234 patients who underwent surgical resection. Among them, 101 patients exhibited VETC-positive HCC, while 133 patients displayed VETC-negative HCC. Volumes of interest were manually delineated for entire tumour regions in the arterial phase (AP), portal phase (PP), and hepatobiliary phase (HBP) images. Independent predictors for VETC were identified through least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis, utilising radiomics-AP, PP, HBP, along with 24 imaging features and 19 clinical characteristics. Subsequently, the clinico-radiologic model, radiomics model, and integrated model were established, with a nomogram visualising the integrated model. The performance for VETC prediction was evaluated using a receiver operating characteristic curve.
UNASSIGNED: The integrated model, composed of 3 selected traditional imaging features (necrosis or severe ischemia [OR=2.457], peripheral washout [OR=1.678], LLR_AP (Lesion to liver ratio_AP) [OR=0.433] and radiomics-AP [OR=2.870], radiomics-HBP [OR=2.023], radiomics-PP [OR=1.546]), showcased good accuracy in predicting VETC patterns in both the training (AUC=0.873, 95% confidence interval [CI]: 0.821-0.925)) and validation (AUC=0.869, 95% CI:0.789-0.950) cohorts.
UNASSIGNED: This study established an integrated model that combines traditional imaging features and radiomic features from gadoxetic acid-enhanced MRI, demonstrating good performance in predicting VETC patterns.