RtNet: a deep hybrid neural networks for the identification of acute rejection and chronic allograft nephropathy after renal transplantation using multiparametric MRI.

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Abstract

Reliable diagnosis of the cause of renal allograft dysfunction is of clinical importance. The aim of this study is to develop a hybrid deep learning approach for determining acute rejection (AR), chronic allograft nephropathy (CAN) and renal function in kidney-allografted patients by multimodality integration.Clinical and MRI data of 252 kidney-allografted patients who underwent post-transplantation MRI between Dec 2014 and Nov 2019 were retrospectively collected. An end-to-end convolutional neural network, namely RtNet, was designed to discriminate between AR, CAN and stable renal allograft recipient (SR), and secondary, to predict the impaired renal graft function (eGFR ≤ 50 mL/min/1.73 m2). Specially, clinical variables and MRI radiomics features were integrated into the RtNet, resulting a hybrid network (RtNet+). The performance of the conventional radiomics model RtRad, RtNet, and RtNet+ was compared to test effect of multimodality interaction.Out of 252 patients, AR, CAN and SR was diagnosed in 20/252 (7.9%), 92/252 (36.5%) and 140/252 (55.6%) patients, respectively. Of all MRI sequences, T2-weighted imaging and diffusion-weighted imaging with stretched exponential analysis showed better performance than other sequences. On pairwise comparison of resulting prediction models, RtNet+ produced significantly higher macro-area-under-curve (macro-AUC) (0.733 vs 0.745; p = 0.047) than RtNet in discriminating between AR, CAN and SR. RtNet+ performed similarly with the RtNet (macro-AUC, 0.762 vs 0.756; p > 0.05) in discriminating between eGFR ≤ 50 mL/min/1.73 m2 and > 50 mL/min/1.73 m2. With decision curve analysis, adding RtRad and RtNet to clinical variables resulted in more net benefits in diagnostic performance.Our study revealed that the proposed RtNet+ model owned a stable performance in revealing the cause of renal allograft dysfunction, thus might afford important references for individualized diagnostics and treatment strategy.© The Author(s) 2022. Published by Oxford University Press on behalf of the ERA.