#3615 Prediction of response to intensified immunosuppression in childhood steroid-resistant nephrotic syndrome by multimodal machine learning

Abstract Background and Aims There are no established models to predict the varied response to intensified immunosuppression in steroid-resistant nephrotic syndrome. Multimodal machine learning, integrating clinical and genetic data with nephropathology imaging, holds great promise to deliver such classifiers with perfect reproducibility. Here, we present such a theranostic classifier based on a multi-centric dataset. Method The PodoNet cohort with n = 201 biopsies was collected from 14 European centres with large domain shifts in whole slide images. The clinical input data contained 19 parameters including mutation (yes/no) and eGFR at biopsy; the ground truth theranostic endpoint was treatment response as no remission (n = 114), partial (n = 42) and complete (n = 45), defined by serum albumin and proteinuria. We trained our proprietary multimodal MorphSet++ architecture in a weakly supervised fashion. MorphSet++ integrates the clinical data vector from a shallow network at various stages with the transformer-based deep network analysing the nephropathology imaging data. Results are given as mean after 5-fold internal cross-validation. Results Mean AUC was .80 for complete, .77 for partial and .81 and xx for no remission. Mean positive predictive value was 89.72, 89.83, 89.27, mean negative predictive value was 94.83, 93.43 and 97.59; mean sensitivity was 84.21, 67.09, 98.47; mean specificity was 96.77, 98.40, 83.94; mean F1 Score was 86.88, 76.81, 93.65; mean accuracy was 84.21, 67.09, 98.47; mean balanced accuracy was 90.49, 82.75, 91.21 for no, partial and complete remission, respectively. Performance was best with both clinical data and histology as input. Conclusion Our MorphSet++ architecture shows promising results as a theranostic tool, predicting response to immunosuppression in SRNS. MorphSet++ allows for rapid up-scaling with additional, larger datasets for even better and more robust performance. This might lead to a re-appraisal of nephropathology in the clinical management of SRNS.