Suppr超能文献

多机构研究使用影像学-临床列线图预测不可切除肝细胞癌患者 DEB-TACE 与分子靶向药物序贯治疗的获益。

A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram.

机构信息

Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.

Department of Interventional Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.

出版信息

Radiol Med. 2024 Jan;129(1):14-28. doi: 10.1007/s11547-023-01736-0. Epub 2023 Oct 20.

Abstract

OBJECTIVE

Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment.

METHODS

In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression. A Rad-Clinical model for postoperative PFS was further constructed according to the Rad-signature and clinical variables by Cox regression analysis. It was presented as a nomogram and evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis. And further evaluate the application value of Rad-Clinical model in clinical stages and targeted sequential therapy of HCC.

RESULTS

Tumor size, Barcelona Clinic Liver Cancer (BCLC) stage, and radiomics score (Rad-score) were found to be independent risk factors for PFS after DEB-TACE treatment for unresectable HCC, with the Rad-Clinical model being the greatest predictor of PFS in these patients (hazard ratio: 2.08; 95% confidence interval: 1.56-2.78; P < 0.001) along with high 6 months, 12 months, 18 months, and 24 months area under the curves of 0.857, 0.810, 0.843, and 0.838, respectively. In addition, compared to the radiomics and clinical nomograms, the Radiological-Clinical nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (45.2%, 95% CI 0.260-0.632, p < 0.05) and integrated discrimination improvement (14.9%, 95% CI 0.064-0.281, p < 0.05). Based on this model, low-risk patients had higher PFS than high-risk patients in BCLC-B and C stages (P = 0.021). Targeted sequential therapy for patients with high and low-risk HCC in BCLC-B stage exhibited significant benefits (P = 0.018, P = 0.012), but patients with high-risk HCC in BCLC-C stage did not benefit much (P = 0.052).

CONCLUSION

The Rad-Clinical model may be favorable for predicting PFS in patients with unresectable HCC treated with DEB-TACE and for identifying patients who may benefit from targeted sequential therapy.

摘要

目的

探讨放射临床(Rad-Clinical)模型在预测不可切除肝细胞癌(HCC)患者经载药微球动脉化疗栓塞(DEB-TACE)后预后中的疗效,以优化靶向序贯治疗。

方法

本回顾性分析纳入了 202 例 2018 年 6 月至 2022 年 12 月在 17 家机构接受 DEB-TACE 治疗的不可切除 HCC 患者。使用最小绝对值收缩和选择算子回归(least absolute shrinkage and selection operator regression)从 HCC 患者中计算与无进展生存期(PFS)相关的放射组学特征,以构建放射组学特征模型(Rad-signature)。进一步根据 Rad-signature 和临床变量通过 Cox 回归分析构建术后 PFS 的 Rad-Clinical 模型。通过接受者操作特征曲线、校准曲线和决策曲线分析对其进行评估。进一步评估 Rad-Clinical 模型在 HCC 临床分期和靶向序贯治疗中的应用价值。

结果

肿瘤大小、巴塞罗那临床肝癌(BCLC)分期和放射组学评分(Rad-score)是不可切除 HCC 患者 DEB-TACE 治疗后 PFS 的独立危险因素,Rad-Clinical 模型是这些患者 PFS 的最大预测因素(风险比:2.08;95%置信区间:1.56-2.78;P<0.001),具有较高的 6 个月、12 个月、18 个月和 24 个月曲线下面积,分别为 0.857、0.810、0.843 和 0.838。此外,与放射组学和临床列线图相比,放射临床列线图也显著提高了 PFS 结果的分类准确性,基于净重新分类改善(45.2%,95%置信区间 0.260-0.632,p<0.05)和综合判别改善(14.9%,95%置信区间 0.064-0.281,p<0.05)。基于该模型,BCLC-B 和 C 期的低危患者 PFS 高于高危患者(P=0.021)。BCLC-B 期的高、低危 HCC 患者进行靶向序贯治疗有显著获益(P=0.018,P=0.012),但 BCLC-C 期的高危 HCC 患者获益不大(P=0.052)。

结论

Rad-Clinical 模型可能有利于预测接受 DEB-TACE 治疗的不可切除 HCC 患者的 PFS,并识别可能受益于靶向序贯治疗的患者。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验