Using MRI Texture Analysis Machine Learning Models to Assess Graft Interstitial Fibrosis and Tubular Atrophy in Patients with Transplanted Kidneys

• Published in: Information (Basel) Vol. 15; no. 9; p. 537
• Main Authors: Trojani, Valeria, Monelli, Filippo, Besutti, Giulia, Bertolini, Marco, Verzellesi, Laura, Sghedoni, Roberto, Iori, Mauro, Ligabue, Guido, Pattacini, Pierpaolo, Giorgi Rossi, Paolo, Ottone, Marta, Piccinini, Alessia, Alfano, Gaetano, Donati, Gabriele, Fontana, Francesco
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2024
• Subjects: Algorithms; Atrophy; Biopsy; Chronic illnesses; Consent; Feasibility studies; Feature selection; Fibrosis; Image filters; Injury analysis; kidney; Kidneys; Machine learning; magnetic resonance; Magnetic resonance imaging; Microscopy; Patients; Radiomics; Reproducibility; Transplantation; Transplants & implants
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info15090537

Objective: Interstitial fibrosis/tubular atrophy (IFTA) is a common, irreversible, and progressive form of chronic kidney allograft injury, and it is considered a critical predictor of kidney allograft outcomes. The extent of IFTA is estimated through a graft biopsy, while a non-invasive test is lacking. The aim of this study was to evaluate the feasibility and accuracy of an MRI radiomic-based machine learning (ML) algorithm to estimate the degree of IFTA in a cohort of transplanted patients. Approach: Patients who underwent MRI and renal biopsy within a 6-month interval from 1 January 2012 to 1 March 2021 were included. Stable MRI sequences were selected, and renal parenchyma, renal cortex and medulla were segmented. After image filtering and pre-processing, we computed radiomic features that were subsequently selected through a LASSO algorithm for their highest correlation with the outcome and lowest intercorrelation. Selected features and relevant patients’ clinical data were used to produce ML algorithms using 70% of the study cases for feature selection, model training and validation with a 10-fold cross-validation, and 30% for model testing. Performances were evaluated using AUC with 95% confidence interval. Main results: A total of 70 coupled tests (63 patients, 35.4% females, mean age 52.2 years) were included and subdivided into a wider cohort of 50 for training and a smaller cohort of 20 for testing. For IFTA ≥ 25%, the AUCs in test cohort were 0.60, 0.59, and 0.54 for radiomic features only, clinical variables only, and a combined radiomic–clinical model, respectively. For IFTA ≥ 50%, the AUCs in training cohort were 0.89, 0.84, and 0.96, and in the test cohort, they were 0.82, 0.83, and 0.86, for radiomic features only, clinical variables only, and the combined radiomic–clinical model, respectively. Significance: An ML-based MRI radiomic algorithm showed promising discrimination capacity for IFTA > 50%, especially when combined with clinical variables. These results need to be confirmed in larger cohorts.