Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients

IF 8.9 1区医学

Journal of Cachexia, Sarcopenia and Muscle Pub Date : 2023-08-17 DOI:10.1002/jcsm.13315

Sylvia Saalfeld, Robert Kreher, Georg Hille, Uli Niemann, Mattes Hinnerichs, Osman Öcal, Kerstin Schütte, Christoph J. Zech, Christian Loewe, Otto van Delden, Vincent Vandecaveye, Chris Verslype, Bernhard Gebauer, Christian Sengel, Irene Bargellini, Roberto Iezzi, Thomas Berg, Heinz J. Klümpen, Julia Benckert, Antonio Gasbarrini, Holger Amthauer, Bruno Sangro, Peter Malfertheiner, Bernhard Preim, Jens Ricke, Max Seidensticker, Maciej Pech, Alexey Surov

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引用次数: 1

Abstract

Background

Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC).

Methods

Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups.

Results

We used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376–0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930–0.9134).

Conclusions

Parameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.

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基于放射组学的身体成分分析对肝细胞癌患者1年生存率的预后作用。

背景：肿瘤疾病患者的身体组成参数具有潜在的预后潜力。本研究的目的是分析基于放射组学的骨骼肌组织和脂肪组织参数对晚期肝细胞癌（HCC）患者的预后潜力。方法：从297名HCC患者的队列中提取放射组学特征，作为SORAMIC随机对照试验的特设子研究。患者接受选择性内放射治疗（SIRT）联合索拉非尼或单独索拉非尼治疗，分为两组：（1）索拉非尼单药治疗（n=147）和（2）索拉非尼和SIRT治疗（n=150）。主要结果是1年生存率。使用肌肉组织和脂肪组织的分割来检索881个特征。相关性分析和特征清理为每个患者组和每个组织类型产生292个特征。我们将9种特征选择方法与10种特征集组成相结合，构建了90个特征集。我们使用11个分类器构建了990个模型。我们将患者组细分为训练和验证队列和测试队列，即三分之一的患者组。结果：我们使用训练和验证集来确定最佳特征选择和分类模型，并将其应用于每个患者组的测试集。接受索拉非尼单药治疗的患者的分类准确率为75.51%，曲线下面积（AUC）为0.7576（95%置信区间[CI]:0.6376-0.8876），结果的准确率为78.00%，AUC=0.8032（95%可信区间：0.6930-0.9134）。结论：基于骨骼肌组织和脂肪组织放射组学分析的参数可以预测晚期HCC患者的1年生存率。在接受SIRT和索拉非尼治疗的患者中，基于放射组学的参数的预后价值更高。

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来源期刊

Journal of Cachexia, Sarcopenia and Muscle Medicine-Orthopedics and Sports Medicine

自引率

12.40%

发文量

期刊介绍： The Journal of Cachexia, Sarcopenia, and Muscle is a prestigious, peer-reviewed international publication committed to disseminating research and clinical insights pertaining to cachexia, sarcopenia, body composition, and the physiological and pathophysiological alterations occurring throughout the lifespan and in various illnesses across the spectrum of life sciences. This journal serves as a valuable resource for physicians, biochemists, biologists, dieticians, pharmacologists, and students alike.