Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma

• Published in: BMC cancer 2023-10, Vol.23 (1), p.1-1026, Article 1026
• Main Authors: Zhao, Ying, Zhang, Jian, Wang, Nan, Xu, Qihao, Liu, Yuhui, Liu, Jinghong, Zhang, Qinhe, Zhang, Xinyuan, Chen, Anliang, Chen, Lihua, Sheng, Liuji, Song, Qingwei, Wang, Feng, Guo, Yan, Liu, Ailian
• Format: Article
• Language: English
• Subjects: Abdomen; Ablation; Algorithms; Alkaline phosphatase; Analysis; Cancer therapies; Care and treatment; Chemoembolization; Diagnosis; Hepatectomy; Hepatocellular carcinoma; Hepatoma; Liver; Liver cancer; Liver diseases; Magnetic resonance imaging; Metastasis; Nomograms; Open source software; Patient outcomes; Patients; Phosphatases; Radiomics; Regression analysis; Therapeutic chemoembolization; Transarterial chemoembolization; Transplantation; Treatment response; Tumors
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-023-11491-0

Background Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients. Methods A total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman's rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. Results The T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821-0.936) and 0.911 (95%CI, 0.825-0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854-0.958) and 0.918 (95%CI, 0.831-0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709-0.863) and 0.782 (95%CI, 0.660-0.902)] in the both cohorts and achieved good calibration capability and clinical utility. Conclusions CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE. Keywords: Hepat