基于多期增强 MRI 的 Delta 放射组学分析预测经皮热消融治疗后肝细胞癌早期复发。

Delta-radiomics Analysis Based on Multi-phase Contrast-enhanced MRI to Predict Early Recurrence in Hepatocellular Carcinoma After Percutaneous Thermal Ablation.

机构信息

Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China.

Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350005, China; Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China.

出版信息

Acad Radiol. 2024 Dec;31(12):4934-4945. doi: 10.1016/j.acra.2024.06.002. Epub 2024 Jun 19.

DOI:10.1016/j.acra.2024.06.002

PMID:38902111

Abstract

RATIONALE AND OBJECTIVES

It is critical to predict early recurrence (ER) after percutaneous thermal ablation (PTA) for hepatocellular carcinoma (HCC). We aimed to develop and validate a delta-radiomics nomogram based on multi-phase contrast-enhanced magnetic resonance imaging (MRI) to preoperatively predict ER of HCC after PTA.

MATERIALS AND METHODS

We retrospectively enrolled 164 patients with HCC and divided them into training, temporal validation, and other-scanner validation cohorts (n = 110, 29, and 25, respectively). The volumes of interest of the intratumoral and/or peritumoral regions were delineated on preoperative multi-phase MR images. Original radiomics features were extracted from each phase, and delta-radiomics features were calculated. Logistic regression was used to train the corresponding radiomics models. The clinical and radiological characteristics were evaluated and combined to establish a clinical-radiological model. A fusion model comprising the best radiomics scores and clinical-radiological risk factors was constructed and presented as a nomogram. The performance of each model was evaluated and recurrence-free survival (RFS) was assessed.

RESULTS

Child-Pugh grade B, high-risk tumor location, and an incomplete/absent tumor capsule were independent predictors of ER. The optimal radiomics model comprised 12 delta-radiomics features with areas under the curve (AUCs) of 0.834, 0.795, and 0.769 in the training, temporal validation, and other-scanner validation cohorts, respectively. The nomogram showed the best predictive performance with AUCs as 0.893, 0.854, and 0.827 in the three datasets. There was a statistically significant difference in RFS between the risk groups calculated using the delta-radiomics model and nomogram.

CONCLUSIONS

The nomogram combined with the delta-radiomic score and clinical-radiological risk factors could non-invasively predict ER of HCC after PTA.

摘要

背景与目的

预测经皮热消融（PTA）治疗肝细胞癌（HCC）后的早期复发（ER）至关重要。我们旨在开发和验证一种基于多期对比增强磁共振成像（MRI）的 delta 放射组学列线图，以术前预测 HCC 经 PTA 治疗后的 ER。

材料与方法

我们回顾性纳入了 164 例 HCC 患者，并将其分为训练、时间验证和其他扫描仪验证队列（n=110、29 和 25）。在术前多期 MRI 图像上勾画肿瘤内和/或肿瘤周围感兴趣区域的容积。从每个相位提取原始放射组学特征，并计算 delta 放射组学特征。使用逻辑回归训练相应的放射组学模型。评估临床和影像学特征，并将其组合以建立临床-放射学模型。构建包含最佳放射组学评分和临床-放射学危险因素的融合模型，并以列线图表示。评估每个模型的性能，并评估无复发生存（RFS）。

结果

Child-Pugh 分级 B、高危肿瘤位置和不完全/无肿瘤包膜是 ER 的独立预测因子。最佳放射组学模型包含 12 个 delta 放射组学特征，在训练、时间验证和其他扫描仪验证队列中的 AUC 分别为 0.834、0.795 和 0.769。列线图在三个数据集的 AUC 分别为 0.893、0.854 和 0.827，具有最佳的预测性能。使用 delta 放射组学模型和列线图计算的风险组之间的 RFS 存在统计学差异。