Clinical-radiomics predictors to identify the suitability of transarterial chemoembolization treatment in intermediate-stage hepatocellular carcinoma: A multicenter study

被引:8
|
作者
Wang, Dan-Dan [1 ,2 ]
Zhang, Jin-Feng [3 ]
Zhang, Lin-Han [4 ]
Niu, Meng [5 ]
Jiang, Hui-Jie [1 ]
Jia, Fu-Cang [2 ]
Feng, Shi-Ting [6 ]
机构
[1] Harbin Med Univ, Affiliated Hosp 2, Dept Radiol, Harbin 150086, Peoples R China
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
[3] Harbin Med Univ, Dept Breast Surg, Canc Hosp, Harbin 150081, Peoples R China
[4] Harbin Med Univ, Affiliated Hosp 1, Dept PET CT, Harbin 150007, Peoples R China
[5] China Med Univ, Affiliated Hosp 1, Dept Intervent Therapy, Shenyang 110001, Peoples R China
[6] Sun Yat Sen Univ, Affiliated Hosp 1, Dept Radiol, Guangzhou 510080, Peoples R China
基金
中国国家自然科学基金;
关键词
Transarterial chemoembolization; Hepatocellular carcinoma; Radiomics; Machine learning; Prediction; MANAGEMENT; DIAGNOSIS;
D O I
10.1016/j.hbpd.2022.11.005
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Background: Although transarterial chemoembolization (TACE) is the first-line therapy for intermediatestage hepatocellular carcinoma (HCC), it is not suitable for all patients. This study aimed to determine how to select patients who are not suitable for TACE as the first treatment choice. Methods: A total of 243 intermediate-stage HCC patients treated with TACE at three centers were retrospectively enrolled, of which 171 were used for model training and 72 for testing. Radiomics features were screened using the Spearman correlation analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Subsequently, a radiomics model was established using extreme gradient boosting (XGBoost) with 5-fold cross-validation. The Shapley additive explanations (SHAP) method was used to visualize the radiomics model. A clinical model was constructed using univariate and multivariate logistic regression. The combined model comprising the radiomics signature and clinical factors was then established. This model's performance was evaluated by discrimination, calibration, and clinical application. Generalization ability was evaluated by the testing cohort. Finally, the model was used to analyze overall and progression-free survival of different groups. Results: A third of the patients (81/243) were unsuitable for TACE treatment. The combined model had a high degree of accuracy as it identified TACE-unsuitable cases, at a sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 0.759, 0.885, 0.906 [95% confidence interval (CI): 0.859-0.953] in the training cohort and 0.826, 0.776, and 0.894 (95% CI: 0.815-0.972) in the testing cohort, respectively. Conclusions: The high degree of accuracy of our clinical-radiomics model makes it clinically useful in identifying intermediate-stage HCC patients who are unsuitable for TACE treatment. (c) 2022 First Affiliated Hospital, Zhejiang University School of Medicine in China. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:594 / 604
页数:11
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