文章：

放射组学在预测局部晚期肝细胞癌肝定向联合放疗肿瘤学疗效中的效能

Efficacy of Radiomics in Predicting Oncologic Outcome of Liver-Directed Combined Radiotherapy in Locally Advanced Hepatocellular Carcinoma

原文发布日期：14 November 2023

DOI: 10.3390/cancers15225405

类型: Article

开放获取: 是

英文摘要：

Purpose: We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in patients with hepatocellular carcinoma (HCC) who received liver-directed combined radiotherapy (LD-CRT). Methods: We included 409 patients, and they were randomly divided into training (n = 307) and validation (n = 102) cohorts. For radiomics models, we extracted 116 radiomic features from the region of interest on the CECT images. Significant clinical prognostic factors are identified to predict the OR and IFFR in the clinical models. We developed clinical models, radiomics models, and a combination of both features (CCR model). Results: Among the radiomic models evaluated for OR, the OR-PVP-Peri-1cm model showed favorable predictive performance with an area under the curve (AUC) of 0.647. The clinical model showed an AUC of 0.729, whereas the CCR model showed better performance (AUC 0.759). For the IFFR, the IFFR-PVP-Peri-1cm model showed an AUC of 0.673, clinical model showed 0.687, and the CCR model showed 0.736. We also developed and validated a prognostic nomogram based on CCR models. Conclusion: In predicting the OR and IFFR in patients with HCC undergoing LD-CRT, CCR models performed better than clinical and radiomics models. Moreover, the constructed nomograms based on these models may provide valuable information on the prognosis of these patients.

摘要翻译： 

目的：本研究旨在探讨从三期动态增强计算机断层扫描（CECT）中提取的影像组学特征，能否用于预测接受肝脏定向联合放疗（LD-CRT）的肝细胞癌（HCC）患者的临床结局，包括客观治疗反应（OR）和场内无失败生存率（IFFR）。方法：共纳入409例患者，随机分为训练队列（n = 307）和验证队列（n = 102）。在影像组学模型中，我们从CECT图像的兴趣区域提取了116个影像组学特征。临床模型中则识别出显著影响OR和IFFR的临床预后因素。我们分别构建了临床模型、影像组学模型以及结合两者特征的联合模型（CCR模型）。结果：在评估OR的影像组学模型中，OR-PVP-Peri-1cm模型显示出较好的预测性能，曲线下面积（AUC）为0.647。临床模型的AUC为0.729，而CCR模型表现更优（AUC 0.759）。对于IFFR，IFFR-PVP-Peri-1cm模型的AUC为0.673，临床模型为0.687，CCR模型为0.736。我们还基于CCR模型开发并验证了预后列线图。结论：在预测接受LD-CRT的HCC患者的OR和IFFR方面，CCR模型的表现优于临床模型和影像组学模型。此外，基于这些模型构建的列线图可能为这些患者的预后评估提供有价值的信息。

原文链接：

Efficacy of Radiomics in Predicting Oncologic Outcome of Liver-Directed Combined Radiotherapy in Locally Advanced Hepatocellular Carcinoma