18F-FDG PET/CT-based intratumoral and peritumoral radiomics combining ensemble learning for prognosis prediction in hepatocellular carcinoma: a multi-center study.

IF 3.4 2区 医学 Q2 ONCOLOGY BMC Cancer Pub Date : 2025-02-19 DOI:10.1186/s12885-025-13649-4
Chunxiao Sui, Kun Chen, Enci Ding, Rui Tan, Yue Li, Jie Shen, Wengui Xu, Xiaofeng Li
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Abstract

Background: Radiomic models combining intratumoral with peritumoral features are potentially beneficial to enhance the predictive performance. This study aimed to identify the optimal 18F-FDG PET/CT-derived radiomic models for prediction of prognosis in hepatocellular carcinoma (HCC).

Methods: A total of 135 HCC patients from two institutions were retrospectively included. Four peritumoral regions were defined by dilating tumor region with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm, respectively. Based on segmentation of intratumoral, peritumoral and integrated volume of interest (VOI), corresponding radiomic features were extracted respectively. After feature selection, a total of 15 intratumoral radiomic models were constructed based on five ensemble learning algorithms and radiomic features from three image modalities. Then, the optimal combination of ensemble learning algorithms and image modality in the intratumoral models was selected to develop subsequent peritumoral radiomic models and integrated radiomic models. Finally, a nomogram was developed incorporating the optimal radiomic model with clinical independent predictors to achieve an intuitive representation of the prediction model.

Results: Among the intratumoral radiomic models, the one which combined PET/CT-based radiomic features with SVM classifier outperformed other models. With the addition of peritumoral information, the integrated model based on an integration of intratumoral and 2 mm-peritumoral VOI, was finally approved as the optimal radiomic model with a mean AUC of 0.831 in the internal validation, and a highest AUC of 0.839 (95%CI:0.718-0.960) in the external test. Furthermore, a nomogram incorporating the optimal radiomic model with HBV infection and TNM status, was able to predict the prognosis for HCC with an AUC of 0.889 (95%CI: 0.799-0.979).

Conclusions: The integrated intratumoral and peritumoral radiomic model, especially for a 2 mm peritumoral region, was verified as the optimal radiomic model to predict the overall survival of HCC. Furthermore, combination of integrated radiomic model with significant clinical parameter contributed to further enhance the prediction efficacy.

Trial registration: This study was a retrospective study, so it was free from registration.

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基于18F-FDG PET/ ct的肿瘤内和肿瘤周围放射组学结合集合学习用于肝细胞癌的预后预测:一项多中心研究
背景:结合肿瘤内和肿瘤周围特征的放射组学模型可能有助于提高预测性能。本研究旨在确定预测肝细胞癌(HCC)预后的最佳18F-FDG PET/ ct放射组学模型。方法:回顾性分析两所医院共135例HCC患者。通过扩大肿瘤区域定义4个肿瘤周围区域,厚度分别为2mm、4mm、6mm和8mm。基于瘤内、瘤周和积分感兴趣体积(VOI)的分割,分别提取相应的放射学特征。特征选择后,基于5种集成学习算法和3种图像模态的放射学特征,构建了15个肿瘤内放射学模型。然后,在肿瘤内模型中选择集成学习算法和图像模态的最佳组合来开发后续的肿瘤周围放射学模型和集成放射学模型。最后,将最佳放射学模型与临床独立预测因子相结合,开发了一个nomogram,以实现预测模型的直观表示。结果:在肿瘤内放射组学模型中,基于PET/ ct的放射组学特征与SVM分类器相结合的放射组学模型优于其他模型。在加入肿瘤周围信息后,基于肿瘤内和2 mm肿瘤周围VOI的综合模型最终被认可为最佳放射学模型,内部验证的平均AUC为0.831,外部测试的最高AUC为0.839 (95%CI:0.718-0.960)。此外,结合HBV感染和TNM状态的最佳放射组模型的nomogram能够预测HCC的预后,AUC为0.889 (95%CI: 0.799-0.979)。结论:肿瘤内和肿瘤周围综合放射组学模型,特别是肿瘤周围2 mm区域的放射组学模型,被证实是预测HCC总生存的最佳放射组学模型。综合放射学模型与重要临床参数的结合有助于进一步提高预测效果。试验注册:本研究为回顾性研究,无需注册。
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来源期刊
BMC Cancer
BMC Cancer 医学-肿瘤学
CiteScore
6.00
自引率
2.60%
发文量
1204
审稿时长
6.8 months
期刊介绍: BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.
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