Abstract：
BACKGROUND: Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment.
OBJECTIVE: To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis.
METHODS: Retrospective.
METHODS: Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94).
UNASSIGNED: 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence.
RESULTS: Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews.
METHODS: Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance.
RESULTS: Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival.
CONCLUSIONS: The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk.
METHODS: 3 TECHNICAL EFFICACY: Stage 2.
OBJECTIVE: To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis.
METHODS: Retrospective.
METHODS: Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94).
UNASSIGNED: 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence.
RESULTS: Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews.
METHODS: Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance.
RESULTS: Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival.
CONCLUSIONS: The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk.
METHODS: 3 TECHNICAL EFFICACY: Stage 2.
摘要：
背景：肝细胞癌（HCC）预后不良，通常以微血管侵犯（MVI）为特征。影像组学和栖息地成像为术前MVI评估提供了潜力。
目的：通过栖息地成像识别HCC中的MVI，肿瘤放射组学分析，和基于肿瘤生境的放射学分析。
方法：回顾性。
方法：三百十八例（53±11.42岁；男性=276）病理证实为HCC（训练：测试=224：94）。
■1.5T,T2WI（自旋回波），预对比和动态T1WI使用三维梯度回波序列。
结果：临床模型，栖息地模型,单序列放射学模型，基于栖息地的放射学模型，并构建了用于评估MVI的组合模型。通过回顾病历或电话访谈获得随访临床数据。
方法：单变量和多变量逻辑回归，接收机工作特性(ROC)曲线,校准,决策曲线,德隆测试,K-M曲线,对数秩检验。P值小于0.05(两侧)被认为指示统计学显著性。
结果：生境成像显示亚区域数量与MVI概率呈正相关。Radiomic-Pre模型显示，在训练和测试队列中检测MVI的AUC为0.815（95％CI：0.752-0.878）和0.708（95％CI：0.599-0.817），分别。同样，使用Radiomic-HBP进行MVI检测的AUC对于训练队列为0.790(95%CI:0.724-0.855),对于测试队列为0.712(95%CI:0.604-0.820).组合模型表现出改进的性能，影像组学+生境+扩张+生境2+临床模型(模型7)实现比模型1-4和6更高的AUC(0.825vs.在测试队列中分别为0.688、0.726、0.785、0.757、0.804，P=0.013、0.048、0.035、0.041、0.039）。通过该模型鉴定的高风险患者(截止值>0.11)显示出较短的无复发生存期。
结论：组合模型包括肿瘤大小，栖息地成像,影像组学分析在预测MVI方面表现最佳，同时还评估预后风险。
方法：3技术效果：阶段2。
目的：通过栖息地成像识别HCC中的MVI，肿瘤放射组学分析，和基于肿瘤生境的放射学分析。
方法：回顾性。
方法：三百十八例（53±11.42岁；男性=276）病理证实为HCC（训练：测试=224：94）。
■1.5T,T2WI（自旋回波），预对比和动态T1WI使用三维梯度回波序列。
结果：临床模型，栖息地模型,单序列放射学模型，基于栖息地的放射学模型，并构建了用于评估MVI的组合模型。通过回顾病历或电话访谈获得随访临床数据。
方法：单变量和多变量逻辑回归，接收机工作特性(ROC)曲线,校准,决策曲线,德隆测试,K-M曲线,对数秩检验。P值小于0.05(两侧)被认为指示统计学显著性。
结果：生境成像显示亚区域数量与MVI概率呈正相关。Radiomic-Pre模型显示，在训练和测试队列中检测MVI的AUC为0.815（95％CI：0.752-0.878）和0.708（95％CI：0.599-0.817），分别。同样，使用Radiomic-HBP进行MVI检测的AUC对于训练队列为0.790(95%CI:0.724-0.855),对于测试队列为0.712(95%CI:0.604-0.820).组合模型表现出改进的性能，影像组学+生境+扩张+生境2+临床模型(模型7)实现比模型1-4和6更高的AUC(0.825vs.在测试队列中分别为0.688、0.726、0.785、0.757、0.804，P=0.013、0.048、0.035、0.041、0.039）。通过该模型鉴定的高风险患者(截止值>0.11)显示出较短的无复发生存期。
结论：组合模型包括肿瘤大小，栖息地成像,影像组学分析在预测MVI方面表现最佳，同时还评估预后风险。
方法：3技术效果：阶段2。
