Deep Learning Morphometric Analysis on Protocol Biopsies Predicts Future Graft Function

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Introduction The predictive value of Banff Classification in protocol transplant biopsies without specific lesions is limited. Morphometry provides precise data on microstructures, surpassing semi-quantitative scores but is time-consuming. This study evaluates whether automated morphometric analysis with deep learning can predict glomerular filtration rate at three years using machine learning. Methods This retrospective study included kidney transplant recipients who underwent protocol biopsy without specific lesion. The models were trained and tested on the Training/Test cohort, with external validation on the Application cohort. Eight deep learning algorithms extracted 23 morphometric parameters from whole-slide images. Ten machine learning models were tested for three years glomerular filtration rates prediction. Results A total of 367 patients were included. The means three-year estimated glomerular filtration rates were 53±23 and 53±22 mL/min/1.73m 2 in the Training/Test and Application cohorts, respectively. In the Training/Test cohort, estimated glomerular filtration rate correlated negatively with interstitial fibrosis (r=-0.33; p<0.001), tubular atrophy (r=-0.39; p<0.001), artery luminal stenosis (r=-0.29; p<0.001), and positively with glomerular density (r=0.16; p<0.05) and glomerular epithelial (r=0.33; p<0.001), endothelial (r=0.30; p<0.001), and mesangial (r=0.25; p=0.002) cell densities. Kernel Ridge and Bayesian models achieved the best predictions (Mean Absolute Error=11±1 mL/min/1.73m 2 ). External validation showed good association between predicted with Kernel Ridge model and observed estimated glomerular filtration rate (Mean Absolute Error=13±11 mL/min/1.73m 2 , r=0.68; p<0.001). After correction with Bland Altman Bias, paired analysis showed no significant difference between predicted and observed estimated glomerular filtration rates (p=0.953). Conclusion Integrating Automated morphometric analyses into machine learning models may help predict glomerular filtration rates three years after protocol biopsies.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.