MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma

MRI-based radiomics model and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma

Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular car...

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Bibliographic Details
Published in:BMC medical imaging Vol. 23; no. 1; p. 67
Main Authors: Wang, Yuxin, Liu, Zhenhao, Xu, Hui, Yang, Dawei, Jiang, Jiahui, Asayo, Himeko, Yang, Zhenghan
Format: Journal Article
Language:English
Published: England BioMed Central Ltd 30-05-2023
BioMed Central
BMC
Subjects:
Ablation
Cancer therapies
Carcinoma, Hepatocellular - diagnostic imaging
Carcinoma, Hepatocellular - pathology
Carcinoma, Hepatocellular - therapy
Decision analysis
Electrodes
Hepatocellular carcinoma
Hepatoma
Humans
Kinases
Liver cancer
Liver diseases
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - pathology
Liver Neoplasms - therapy
Locoregional treatment
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical prognosis
Medical research
Medicine, Experimental
Multivariate analysis
Nomograms
Patient outcomes
Patients
Prognosis
Radiomics
Regression analysis
Retrospective Studies
Veins & arteries
China
Hepatocellular carcinoma
Magnetic resonance imaging
Locoregional treatment
Radiomics
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Summary:Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogram for predicting the outcome of locoregional treatment in patients with hepatocellular carcinoma. The initial postoperative MRI after locoregional treatment in 100 patients with hepatocellular carcinoma was retrospectively analysed. The outcome was evaluated according to mRECIST at 6 months. We delineated the tumour volume of interest on arterial phase, portal venous phase and T2WI. The radiomics features were selected by using the independent sample t test or nonparametric Mann‒Whitney U test and the least absolute shrinkage and selection operator. The clinical variables were selected by using univariate analysis and multivariate analysis. The radiomics model and combined model were constructed via multivariate logistic regression analysis. A nomogram was constructed that incorporated the Rad score and selected clinical variables. Fifty patients had an objective response, and fifty patients had a nonresponse. Nine radiomics features in the arterial phase were selected, but none of the portal venous phase or T2WI radiomics features were predictive of the treatment response. The best radiomics model showed an AUC of 0.833. Two clinical variables (hCRP and therapy method) were selected. The AUC of the combined model was 0.867. There was no significant difference in the AUC between the combined model and the best radiomics model (P = 0.573). Decision curve analysis demonstrated the nomogram has satisfactory predictive value. MRI-based radiomics analysis may serve as a promising and noninvasive tool to predict outcome of locoregional treatment in HCC patients, which will facilitate the individualized follow-up and further therapeutic strategies guidance.
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content type line 23
ISSN:1471-2342
1471-2342
DOI:10.1186/s12880-023-01030-5