In:
Nephrology Dialysis Transplantation, Oxford University Press (OUP), Vol. 38, No. Supplement_1 ( 2023-06-14)
Abstract:
Prediction of future graft failure with non-invasive investigations is a relevant objective for transplant clinicians. We previously demonstrated a promising discrimination capacity for interstitial fibrosis / tubular atrophy (IFTA) & gt;50% in kidney biopsy of a machine learning (ML) based magnetic resonance imaging (MRI) radiomic algorithm. Aim of the present study is to evaluate accuracy of MRI radiomic-based ML algorithms in predicting future graft failure. Method Single-center retrospective observational cohort study on a previously characterized cohort of kidney transplant recipients who underwent graft biopsy and graft MRI imaging within 6 months from biopsy, both on clinical indication, at the “Azienda Ospedaliero-Universitaria di Modena”, Italy, from 1/1/2012 to 1/3/2021. The primary outcome was to identify the best combination of MRI radiomic features for the prediction of graft failure during follow-up. Segmentation of renal parenchyma, cortex and medulla on MRI sequences was performed using the 3DSlicer software. Radiomic features were then extracted using an in-house software based on pyradiomics applying Wavelet and Gaussian filters. LASSO algorithm was employed to select correlated features with outcome and summarize them in a radiomic signature. Using radiomic signature alone and then merged with meaningful clinical data we trained ML-algorithms using 70% of cases for training/validation, with a 10-fold internal cross-validation, and 30% for model testing. Model performance was assessed using AUC with a 95% confidence interval (CI). Results Seventy coupled tests (63 patients) were included and randomly subdivided into a training/validation cohort (n = 50) and a test cohort (n = 20). Median follow-up was 24.73 months (interquartile range 13.64-46.57). Radiomic model had an AUC of 0.88 (95%_CI 0.70-0.97) in training and 0.57 (95%_CI 0.23-0.86) in test cohort. Radiomic-clinical mixed model had an AUC of 0.90 (95%_CI 0.73-0.98) in training and 0.66 (95%_CI 0.33-0.88) in test cohort. Conclusion We produced an MRI radiomic-based ML algorithm with good prediction ability for future graft failure in patients with kidney transplant. Given the limited number of enrolled patients, validation in larger (and ideally prospective) cohorts is required to confirm our findings. Comparison of radiomic-based ML with histological parameters (i.e., IFTA) for the prediction of graft failure in our cohort is currently under assessment.
Type of Medium:
Online Resource
ISSN:
0931-0509
,
1460-2385
DOI:
10.1093/ndt/gfad063c_6073
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2023
detail.hit.zdb_id:
1465709-0