Dataset Information

A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma.

ABSTRACT:

Objectives

The postoperative early recurrence (ER) rate of hepatocellular carcinoma (HCC) is 50%, and no highly reliable predictive tool has been developed yet. The aim of this study was to develop and validate a predictive model with radiomics analysis based on multiparametric magnetic resonance (MR) images to predict early recurrence of HCC.

Methods

In total, 302 patients (training dataset: n = 211; validation dataset: n = 91) with pathologically confirmed HCC who underwent preoperative MR imaging were enrolled in this study. Three-dimensional regions of interest of the entire lesion were accessed by manually drawing along the tumor margins on the multiple sequences of MR images. Least absolute shrinkage and selection operator Cox regression were then applied to select ER-related radiomics features and construct radiomics signatures. Univariate analysis and multivariate Cox regression analysis were used to identify the significant clinico-radiological factors and establish a clinico-radiological model. A predictive model of ER incorporating the fusion radiomics signature and clinico-radiological risk factors was constructed. The diagnostic performance and clinical utility of this model were measured by receiver-operating characteristic (ROC), calibration curve, and decision curve analyses.

Results

The fusion radiomics signature consisting of 6 radiomics features achieved good prediction performance (training dataset: AUC = 0.85, validation dataset: AUC = 0.79). The predictive model of ER integrating clinico-radiological risk factors and the fusion radiomics signature improved the prediction efficacy with AUCs of 0.91 and 0.87 in the training and validation datasets, respectively. Furthermore, the nomogram and ER risk stratification system based on the predictive model demonstrated encouraging predictions of the individualized risk of ER and gave three risk groups with low, intermediate, or high risk of ER.

Conclusions

The proposed predictive model incorporating clinico-radiological factors and the fusion radiomics signature derived from multiparametric MR images may be an effective tool for the individualized prediction of postoperative ER in patients with HCC.

SUBMITTER: Li W

PROVIDER: S-EPMC9534653 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma.

Li Wencui W Shen Hongru H Han Lizhu L Liu Jiaxin J Xiao Bohan B Li Xubin X Ye Zhaoxiang Z

Journal of oncology 20220928

<h4>Objectives</h4>The postoperative early recurrence (ER) rate of hepatocellular carcinoma (HCC) is 50%, and no highly reliable predictive tool has been developed yet. The aim of this study was to develop and validate a predictive model with radiomics analysis based on multiparametric magnetic resonance (MR) images to predict early recurrence of HCC.<h4>Methods</h4>In total, 302 patients (training dataset: <i>n</i> = 211; validation dataset: <i>n</i> = 91) with pathologically confirmed HCC who ...[more]

PMID: 36213823

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Project description:PurposeTo explore the predictive potential of intratumoral and multiregion peritumoral radiomics features extracted from multiparametric MRI for predicting pathological differentiation in hepatocellular carcinoma (HCC) patients.MethodsA total of 265 patients with 277 HCCs (training cohort n = 193, validation cohort n = 84) who underwent preoperative MRI were retrospectively analyzed. The risk factors identified through stepwise regression analysis were utilized to construct a clinical model. Radiomics models based on MRI (arterial phase, portal venous phase, delayed phase) across various regions (entire tumor, Peri_5mm, Peri_10mm, Peri_20mm) were developed using the LASSO approach. The features obtained from the intratumoral region and the optimal peritumoral region were combined to design the IntraPeri fusion model. Model performance was assessed using the area under the curve (AUC).ResultsLarger size, non-smooth margins, and mosaic architecture were risk factors for poorly differentiated HCC (pHCC). The clinical model achieved AUCs of 0.77 and 0.73 in the training and validation cohorts, respectively, while the intratumoral model achieved corresponding AUC values of 0.92 and 0.82. The Peri_10mm model demonstrated superior performance to the Peri_5mm and Peri_20mm models, with AUC values of 0.87 vs. 0.84 vs. 0.73 in the training cohort and 0.80 vs. 0.77 vs. 0.68 in the validation cohort, respectively. The IntraPeri model exhibited remarkable AUC values of 0.95 and 0.86 in predicting pHCC in the training and validation cohorts, respectively.ConclusionsOur study highlights the potential of a multiparametric MRI-based radiomic model that integrates intratumoral and peritumoral features as a tool for predicting HCC differentiation.Critical relevance statementBoth clinical and multiparametric MRI-based radiomic models, particularly the intratumoral radiomic model, are non-invasive tools for predicting HCC differentiation. Importantly, the IntraPeri fusion model exhibited remarkable predictiveness for individualized HCC differentiation.Key points• Both the intratumoral radiomics model and clinical features were useful for predicting HCC differentiation. • The Peri_10mm radiomics model demonstrated better diagnostic ability than other peritumoral region-based models. • The IntraPeri radiomics fusion model outperformed the other models for predicting HCC differentiation.

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Dataset Information

A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma.

Objectives

Methods

Results

Conclusions

Publications

A Multiparametric Fusion Radiomics Signature Based on Contrast-Enhanced MRI for Predicting Early Recurrence of Hepatocellular Carcinoma.

Similar Datasets

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