基于CT的栖息地成像与放射学和临床病理学相结合，用于无创预测肝细胞癌的微血管侵袭
CT-based habitat imaging integrated with radiomics and clinicopathology for noninvasive prediction of microvascular invasion in hepatocellular carcinoma.
摘要(中文译文)
开发并验证一种基于CT的栖息地影像模型，该模型整合了瘤内微环境异质性分析，用于肝细胞癌（HCC）微血管侵犯（MVI）的无创术前预测，以解决传统影像组学在表征瘤内异质性方面的局限性。本回顾性研究纳入216例经病理确诊并接受切除术的HCC患者。收集术前门静脉期增强CT及相应的术后组织病理学MVI状态。提取栖息地（功能异质性亚区）特征和传统影像组学特征，计算栖息地风险评分和影像组学评分。通过单因素/多因素分析筛选出的临床-病理因素（Edmondson分级、p53/CD10表达、肿瘤直径）被整合至预测模型，并以列线图形式可视化。采用AUC、校准曲线和决策曲线分析（DCA）评估性能。DeLong检验用于比较ROC曲线。在训练集中，联合模型的AUC为0.862（95% CI：0.797-0.926）；在验证集中，AUC为0.814（95% CI：0.710-0.918），均显著优于各单一模型（均P < 0.05）。校准曲线显示良好的一致性（Hosmer-Lemeshow P = 0.60）。DCA表明在15%-65%的阈值范围内具有净获益。DeLong检验证实联合模型的AUC高于临床模型（Z = -3.21，P < .05）和纯影像组学模型（Z = -2.05，P < .05）。基于CT的栖息地影像模型可量化瘤内异质性，并结合临床病理数据，为HCC术前MVI风险分层提供了可靠的无创工具，具有较强的临床潜力。
Abstract
To develop and validate a CT-based habitat imaging model incorporating intratumoral microenvironment heterogeneity analysis for noninvasive preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC), addressing limitations of conventional radiomics in characterizing intra-tumor heterogeneity. This retrospective study included 216 patients with pathologically confirmed HCC undergoing resection. Preoperative portal venous phase contrast-enhanced CT and corresponding postoperative histopathologic MVI status were collected. Habitat (functional heterogeneous subregion) and conventional radiomic features were extracted. A habitat risk score and radiomic score were calculated. Clinical-pathologic factors (Edmondson grade, p53/CD10 expression, tumor diameter) identified via univariate/multivariate analyzes were integrated into a predictive model visualized as a nomogram. Performance was assessed by AUC, calibration curves, and decision curve analysis (DCA). DeLong's test compared ROC curves. In the training set, the combined model achieved an AUC of 0.862 (95% CI: 0.797-0.926); in the validation set, AUC was 0.814 (95% CI: 0.710-0.918), both significantly outperforming individual models (all P < 0.05). Calibration curves showed good agreement (Hosmer-Lemeshow P = 0.60). DCA indicated net benefit at thresholds of 15%-65%. DeLong's test confirmed the combined model had higher AUC than the clinical model (Z = -3.21, P < .05) and radiomics-only model (Z = -2.05, P < .05). The CT-based habitat imaging model quantified intratumoral heterogeneity and, combined with clinicopathologic data, provided a reliable noninvasive tool for preoperative MVI risk stratification in HCC, with strong clinical potential.
如何引用
AMA
Shuangxi Chen, Xushuang Qin, Shanni Dong, Xiaoshu Zhu, Yang Liu, Jun Chen, et al. CT-based habitat imaging integrated with radiomics and clinicopathology for noninvasive prediction of microvascular invasion in hepatocellular carcinoma.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1832615.
APA
Shuangxi Chen, Xushuang Qin, Shanni Dong, Xiaoshu Zhu, Yang Liu, Jun Chen, et al (2026). CT-based habitat imaging integrated with radiomics and clinicopathology for noninvasive prediction of microvascular invasion in hepatocellular carcinoma.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1832615
GB/T 7714
Shuangxi Chen, Xushuang Qin, Shanni Dong, Xiaoshu Zhu, Yang Liu, Jun Chen, et al. CT-based habitat imaging integrated with radiomics and clinicopathology for noninvasive prediction of microvascular invasion in hepatocellular carcinoma.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1832615.
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