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基于CE-CT的VETC预测模型可预测孤立性肝癌的预后并辅助治疗方案制定:与放射组学联合使用效果更佳。

VETC predicting model based on CE-CT can predict prognosis and assisting treatment plan for solitary HCC: better together with radiomics.

作者信息

Zhao Yu-Meng, Xie Shuang-Shuang, Wang Jian, Hu Zhan-Dong, Yao Sheng-Juan, Wang Rui-Hang, Qin Jia-Ming, Zhang Cheng, Zhang Ya-Min, Ye Zhao-Xiang, Yan Jian-Hua, Shen Wen

机构信息

Medical School of Nankai University, No. 94, Weijin Road, Nankai District, Tianjin, China.

Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, Nankai University, Nankai District, No. 24 Fukang Road, Tianjin, China.

出版信息

BMC Cancer. 2025 Jul 1;25(1):1033. doi: 10.1186/s12885-025-14408-1.

DOI:10.1186/s12885-025-14408-1
PMID:40596967
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12211255/
Abstract

OBJECTIVES

Noninvasive evaluation and treatment of vessels encapsulating tumor cluster (VETC) HCCs remain challenging. Herein, a new Clinic-Radiologic-Intratumor Radiomics (CRIR) model was investigated for the preoperative prediction of VETC-HCCs and prognosis based on CE-CT, then compared therapeutic outcomes between predicted VETC and nonVETC-HCCs after different treatment methods.

METHODS

Total 456 HCC patients who underwent radical resection (RR), liver transplantation (LT) or TACE were retrospectively included in this multicenter (Center 1–4) study between January 2014 and November 2022. The intratumor and 1 cm peritumor VOI were segmented in the three phases of CE-CT imaging. Radiomics features were selected using LASSO and multivariable logistic regression (LR) to filtered the useful features. Clinical, radiological qualitative and quantitative features, intratumor, peritumor and combined radiomics, were established using LR into Clinic-radiological (CR), intratumor radiomics (IR), peritumor radiomics (PR), CRIR, Clinic-radiological- peritumor radiomics (CRPR)and CR-intra and peritumor radiomics (CRIPR) models. Diagnostic performance was calculated and compared for the models. Kaplan–Meier survival analysis was used to assess progression state in model-predicted VETC-HCCs and non-VETC-HCCs in TACE, or early recurrence in both pathologic and model-predicted in RR or LT groups. Additionally, outcomes between the RR and LT groups were compared to determine the optimal treatment approach.

RESULTS

Neutrophil-to-lymphocyte ratio (NLR) ( = 0.031), gamma-glutamyl transferase (GGT) ( = 0.043), intratumor necrosis ( = 0.026), Arterial enhancement fraction (AEF) (  0.038), and intra-tumoral artery ( = 0.035) were independent predictors of VETC-HCC. CRIR model showed best area under the ROC curve value (0.85-080 across training, internal test, and external test), statistically significant improvement over the clinico-radiologic model, but not the CRIPR model. In patients with pathologic VETC-HCC, those treated with RR exhibited higher early recurrence rate compared to those treated with LT ( = 0.029). On the contrast, the early recurrence rates in patients with pathologic non-VETC-HCC, were similar between the RR and LT groups ( > 0.05). Similarly, the early recurrence rates in patients with CRIR model predicted VETC-HCC or non-VETC-HCC presented same trend as pathologic group. In application (TACE) group, CRIR model predicted VETC-HCC had lower tumor response rate (50.00% vs. 75.56%,  < 0.001) and worse PFS (17 months vs. 30 months;  = 0 0.039) than those with non-VETC HCCs.

CONCLUSION

The CRIR model provides accurate preoperative identification of VETC-HCC and offers prognostic value for early recurrence following RR or LT, tumor response after TACE and surgical approach selection between RR and LT in solitary HCC.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12885-025-14408-1.

摘要

目的

对包裹肿瘤簇的血管(VETC)型肝癌进行无创评估和治疗仍具有挑战性。在此,研究了一种新的临床-放射学-肿瘤内放射组学(CRIR)模型,用于基于增强CT对VETC型肝癌进行术前预测和预后评估,然后比较不同治疗方法后预测的VETC型和非VETC型肝癌的治疗效果。

方法

回顾性纳入2014年1月至2022年11月期间在该多中心(中心1-4)研究中接受根治性切除术(RR)、肝移植(LT)或经动脉化疗栓塞术(TACE)的456例肝癌患者。在增强CT成像的三个阶段对肿瘤内和肿瘤周围1 cm的感兴趣区(VOI)进行分割。使用套索回归和多变量逻辑回归(LR)选择放射组学特征以筛选有用特征。利用LR将临床、放射学定性和定量特征、肿瘤内、肿瘤周围和联合放射组学特征建立为临床-放射学(CR)、肿瘤内放射组学(IR)、肿瘤周围放射组学(PR)、CRIR、临床-放射学-肿瘤周围放射组学(CRPR)和CR-肿瘤内和肿瘤周围放射组学(CRIPR)模型。计算并比较各模型的诊断性能。采用Kaplan-Meier生存分析评估TACE治疗中模型预测的VETC型肝癌和非VETC型肝癌的进展状态,或RR或LT组病理和模型预测的早期复发情况。此外,比较RR组和LT组的治疗效果以确定最佳治疗方法。

结果

中性粒细胞与淋巴细胞比值(NLR)(=0.031)、γ-谷氨酰转移酶(GGT)(=0.043)、肿瘤内坏死(=0.026)、动脉强化分数(AEF)(=0.038)和肿瘤内动脉(=0.035)是VETC型肝癌的独立预测因素。CRIR模型在ROC曲线下面积值方面表现最佳(训练集、内部测试集和外部测试集分别为0.85-0.80),与临床-放射学模型相比有统计学显著改善,但CRIPR模型无此改善。在病理诊断为VETC型肝癌的患者中,接受RR治疗的患者早期复发率高于接受LT治疗的患者(=0.029)。相反,病理诊断为非VETC型肝癌的患者中,RR组和LT组的早期复发率相似(>0.05)。同样,CRIR模型预测的VETC型肝癌或非VETC型肝癌患者的早期复发率与病理组呈现相同趋势。在应用(TACE)组中,CRIR模型预测的VETC型肝癌的肿瘤反应率较低(50.00%对75.56%,<0.001),无进展生存期较差(17个月对30个月;=0.039),低于非VETC型肝癌患者。

结论

CRIR模型为VETC型肝癌提供了准确的术前识别,并为RR或LT术后的早期复发、TACE后的肿瘤反应以及孤立性肝癌RR和LT之间的手术方式选择提供了预后价值。

补充信息

在线版本包含可在10.1186/s12885-025-14408-1获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/7d5c34fba389/12885_2025_14408_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/712fc9bbd394/12885_2025_14408_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/8918df91dfae/12885_2025_14408_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/b7afd2e63f8d/12885_2025_14408_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/0955b673a639/12885_2025_14408_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/7d5c34fba389/12885_2025_14408_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/712fc9bbd394/12885_2025_14408_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/8918df91dfae/12885_2025_14408_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/b7afd2e63f8d/12885_2025_14408_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/0955b673a639/12885_2025_14408_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c68/12211255/7d5c34fba389/12885_2025_14408_Fig5_HTML.jpg

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