Differentiating Benign and Hepatocellular Carcinoma Cirrhotic Nodules: Radiomics Analysis of Water Restriction Patterns with Diffusion MRI.
Authors
Affiliations (5)
Affiliations (5)
- Cancer Research Center, Tehran University of Medical Sciences, Tehran, Iran.
- Advanced Diagnostic and Interventional Radiology Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
- Research Centre for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute, Tehran University of Medical Sciences, Tehran, Iran.
- Erfan Niayesh Hospital, Radiology Department, Tehran, Iran.
- Liver Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Abstract
Current study aimed to investigate radiomics features derived from two-center diffusion-MRI to differentiate benign and hepatocellular carcinoma (HCC) liver nodules. A total of 328 patients with 517 LI-RADS 2-5 nodules were included. MR images were retrospectively collected from 3 T and 1.5 T MRI vendors. Lesions were categorized into 242 benign and 275 HCC based on follow-up imaging for LR-2,3 and pathology results for LR4,5 nodules, and randomly divided into training (80%) and test (20%) sets. Preprocessing included resampling and normalization. Radiomics features were extracted from lesion volume-of-interest (VOI) on diffusion Images. Scanner variability was corrected using ComBat harmonization method followed by High-correlation filter, PCA filter, and LASSO to select important features. Best classifier model was selected by 10-fold cross-validation, and accuracy was assessed on the test dataset. 1,434 features were extracted, and subsequent classifiers were constructed based on the 16 most important selected features. Notably, support-vector machine (SVM) demonstrated better performance in the test dataset in distinguishing between benign and HCC nodules, achieving an accuracy of 0.92, sensitivity of 0.94, and specificity of 0.86. Utilizing diffusion-MRI radiomics, our study highlights the performance of SVM, trained on lesions' diffusivity characteristics, in distinguishing benign and HCC nodules, ensuring clinical potential. It is suggested that further evaluations be conducted on multi-center datasets to address harmonization challenges. Integration of diffusion radiomics, for monitoring water restriction patterns as tumor histopathological index, with machine learning models demonstrates potential for achieving a reliable noninvasive method to improve the current diagnosis criteria.