Zobrazit v EDS

Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization.

Uloženo v:
Podrobná bibliografie
Název: Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization.
Autoři: Guo, Xinyu, Song, Jingjing, Zhu, Lingyi, Liu, Shuang, Huang, Chaoming, Zhou, Lingling, Chen, Weiyue, Lin, Guihan, Zhao, Zhongwei, Tu, Jianfei, Chen, Minjiang, Chen, Feng, Zheng, Liyun, Ji, Jiansong
Zdroj: BMC Cancer; 4/14/2025, Vol. 25 Issue 1, p1-12, 12p
Témata: CHEMOEMBOLIZATION, FEATURE extraction, MAGNETIC resonance imaging, RECEIVER operating characteristic curves, RADIOMICS
Abstrakt: Background: This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE). Methods: In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve. Results: Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1–2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1–1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805–0.861), internal validation cohort (0.823; 95% CI 0.771–0.876) and external validation cohort (0.846; 95% CI 0.768–0.924). The calibration curve exhibited a satisfactory correspondence. Conclusion: A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis. [ABSTRACT FROM AUTHOR]
Copyright of BMC Cancer is the property of BioMed Central and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
Popis
Abstrakt:Background: This study was undertaken to develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) for predicting recurrence in patients with hepatocellular carcinoma (HCC) following postoperative adjuvant transarterial chemoembolization (PA-TACE). Methods: In this retrospective study, 149 HCC patients (81 for training, 36 for internal validation, 32 for external validation) treated with PA-TACE were included in two medical centers. Multiparametric radiomics features were extracted from three MRI sequences. Least absolute shrinkage and selection operator (LASSO)-COX regression was utilized to select radiomics features. Optimal clinical characteristics selected by multivariate Cox analysis were integrated with Rad-score to develop a recurrence-free survival (RFS) prediction model. The model performance was evaluated by time-dependent receiver operating characteristic (ROC) curves, Harrell's concordance index (C-index), and calibration curve. Results: Fifteen optimal radiomic features were selected and the median Rad-score value was 0.434. Multivariate Cox analysis indicated that neutrophil-to-lymphocyte ratio (NLR) (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.1–2.1, P = 0.022) and tumor size (HR = 1.28, 95% CI: 1.1–1.5, P = 0.001) were the independent predictors of RFS after PA-TACE. A combined model was established by integrating Rad-score, NLR, and tumor size in the training cohort (C-index 0.822; 95% CI 0.805–0.861), internal validation cohort (0.823; 95% CI 0.771–0.876) and external validation cohort (0.846; 95% CI 0.768–0.924). The calibration curve exhibited a satisfactory correspondence. Conclusion: A multiparametric MRI-based radiomics model can predict RFS of HCC patients receiving PA-TACE and a nomogram can be served as an individualized tool for prognosis. [ABSTRACT FROM AUTHOR]
ISSN:14712407
DOI:10.1186/s12885-025-14079-y