MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models

• Published in: Computerized medical imaging and graphics Vol. 104; p. 102187
• Main Authors: Ruiz-España, Silvia, Ortiz-Ramón, Rafael, Pérez-Ramírez, Úrsula, Díaz-Parra, Antonio, Ciccocioppo, Roberto, Bach, Patrick, Vollstädt-Klein, Sabine, Kiefer, Falk, Sommer, Wolfgang H., Canals, Santiago, Moratal, David
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2023
• Subjects: Alcohol use disorders; Alcoholism - diagnostic imaging; Animals; Brain MRI; Classification algorithms; Cross-Sectional Studies; Humans; Internal Medicine; Longitudinal Studies; Magnetic Resonance Imaging - methods; Models, Animal; Other; Radiomics; Rats; Retrospective Studies; Standardized radiomic features; Striatal network; Standardized radiomic features; Brain MRI; Classification algorithms; Striatal network; Alcohol use disorders; Radiomics
• ISSN: 0895-6111; 1879-0771; 1879-0771
• DOI: 10.1016/j.compmedimag.2023.102187

Alcohol use disorder (AUD) is a complex condition representing a leading risk factor for death, disease and disability. Its high prevalence and severe health consequences make necessary a better understanding of the brain network alterations to improve diagnosis and treatment. The purpose of this study was to evaluate the potential of resting-state fMRI 3D texture features as a novel source of biomarkers to identify AUD brain network alterations following a radiomics approach. A longitudinal study was conducted in Marchigian Sardinian alcohol-preferring msP rats (N = 36) who underwent resting-state functional and structural MRI before and after 30 days of alcohol or water consumption. A cross-sectional human study was also conducted among 33 healthy controls and 35 AUD patients. The preprocessed functional data corresponding to control and alcohol conditions were used to perform a probabilistic independent component analysis, identifying seven independent components as resting-state networks. Forty-three radiomic features extracted from each network were compared using a Wilcoxon signed-rank test with Holm correction to identify the network most affected by alcohol consumption. Features extracted from this network were then used in the machine learning process, evaluating two feature selection methods and six predictive models within a nested cross-validation structure. The classification was evaluated by computing the area under the ROC curve. Images were quantized using different numbers of gray-levels to test their influence on the results. The influence of ageing, data preprocessing, and brain iron accumulation were also analyzed. The methodology was validated using structural scans. The striatal network in alcohol-exposed msP rats presented the most significant number of altered features. The radiomics approach supported this result achieving good classification performance in animals (AUC = 0.915 ± 0.100, with 12 features) and humans (AUC = 0.724 ± 0.117, with 9 features) using a random forest model. Using the structural scans, high accuracy was achieved with a multilayer perceptron in both species (animals: AUC > 0.95 with 2 features, humans: AUC > 0.82 with 18 features). The best results were obtained using a feature selection method based on the p-value. The proposed radiomics approach is able to identify AUD patients and alcohol-exposed rats with good accuracy, employing a subset of 3D features extracted from fMRI. Furthermore, it can help identify relevant networks in drug addiction. •A radiomics MRI approach identifies accurately subjects with AUD.•Functional 3D radiomic features have been validated as potential AUD biomarkers.•Striatal Network has been validated as a key network in alcoholism.•Random forest model identifies AUD subjects with high performance and low variance.