Trojani, V.; Monelli, F.; Besutti, G.; Bertolini, M.; Verzellesi, L.; Sghedoni, R.; Iori, M.; Ligabue, G.; Pattacini, P.; Giorgi Rossi, P.; Ottone, M.; Piccinini, A.; Alfano, G.; Donati, G.; Fontana, F. MRI texture analysis Machine Learning models to assess graft interstitial fibrosis and tubular atrophy in patients with transplanted kidneys. Preprints2024, 2024080113. https://doi.org/10.20944/preprints202408.0113.v1
APA Style
Trojani, V., Monelli, F., Besutti, G., Bertolini, M., Verzellesi, L., Sghedoni, R., Iori, M., Ligabue, G., Pattacini, P., Giorgi Rossi, P., Ottone, M., Piccinini, A., Alfano, G., Donati, G., & Fontana, F. (2024). MRI texture analysis Machine Learning models to assess graft interstitial fibrosis and tubular atrophy in patients with transplanted kidneys. Preprints. https://doi.org/10.20944/preprints202408.0113.v1
Chicago/Turabian Style
Trojani, V., Gabriele Donati and Francesco Fontana. 2024 "MRI texture analysis Machine Learning models to assess graft interstitial fibrosis and tubular atrophy in patients with transplanted kidneys" Preprints. https://doi.org/10.20944/preprints202408.0113.v1
Abstract
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 in estimating the degree of IFTA in a cohort of transplanted patients. Approach: Patients who underwent MRI and renal biopsy within a 6-month interval from 1/1/2012 to 1/3/2021 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 which 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: 70 coupled tests (63 patients, 35.4% females, mean age 52.2 years) have been included and subdivided into a wider cohort of 50 for training and a smaller cohort of 20 for testing. For IFTA ≥ 25%, AUCs in test cohort were 0.60, 0.59, and 0.54 for radiomic features only, clinical variables only, and combined radiomic-clinical model, respectively. For IFTA ≥ 50%, AUCs in training cohort were 0.89, 0.84, 0.96, and in test cohort were 0.82, 0.83, and 0.86, for radiomic features only, clinical variables only, and 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.
Keywords
kidney; Magnetic Resonance; Transplantation; Radiomics
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
