Wilms’ tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms’ tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms’ tumors. A total of 63 patients (age range: 6 months–14 years) were included in this study, after receiving their guardians’ informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based features from Wilms’ tumors before chemotherapy. The proposed system consists of six steps: (i) delineate the tumors’ images across the three contrast phases; (ii) characterize the texture of the tumors using first- and second-order textural features; (iii) extract the shape features by applying a parametric spherical harmonics model, sphericity, and elongation; (iv) capture the intensity changes across the contrast phases to describe the tumors’ functionality; (v) apply features fusion based on the extracted features; and (vi) determine the final prediction as responsive or non-responsive via a tuned support vector machine classifier. The system achieved an overall accuracy of 95.24%, with 95.65% sensitivity and 94.12% specificity. Using the support vector machine along with the integrated features led to superior results compared with other classification models. This study integrates novel imaging markers with a machine learning classification model to make early predictions about how a Wilms’ tumor will respond to preoperative chemotherapy. This can lead to personalized management plans for Wilms’ tumors.

Kidney transplantation is the preferred treatment for end-stage renal failure, but the limited availability of donors and the risk of immune rejection pose significant challenges. Early detection of acute renal rejection is a critical step to increasing the lifespan of the transplanted kidney. Investigating the clinical, genetic, and histopathological markers correlated to acute renal rejection, as well as finding noninvasive markers for early detection, is urgently needed. It is also crucial to identify which markers are associated with different types of acute renal rejection to manage treatment effectively. This short review summarizes recent studies that investigated various markers, including genomics, histopathology, and clinical markers, to differentiate between different types of acute kidney rejection. Our review identifies the markers that can aid in the early detection of acute renal rejection, potentially leading to better treatment and prognosis for renal-transplant patients.

Nashat A, et al. Investig Clin Urol. 2025 Jan;66(1):47-55. https://doi.org/10.4111/icu.20240135

Background/Objectives: Accurate kidney segmentation from magnetic resonance imaging (MRI) in kidney-transplant patients is essential for quantitative graft assessment, yet it remains challenging due to low tissue contrast, intensity inhomogeneity, and inter-patient anatomical variability introduced by surgical graft placement. Methods: We propose a 3D adversarial segmentation framework that incorporates probabilistic appearance and anatomical shape priors into a residual conditional generative adversarial network (GAN). The framework integrates image-driven and prior-guided information to improve boundary delineation under challenging imaging conditions and is evaluated on 100 kidney-transplant patients across T2-weighted imaging, BOLD-MRI, and DW-MRI using leave-one-out cross-validation. Results: The proposed method achieves mean Dice scores of 90.86% on T2-weighted imaging, 92.02% on BOLD-MRI, and 94.00% on DW-MRI. Consistent performance across all modalities demonstrates robustness under heterogeneous MRI acquisitions. The incorporation of prior guidance improves segmentation stability and anatomical consistency, particularly in low-contrast modalities. Conclusions: The proposed framework enables reliable kidney delineation across multiple MRI sequences, supporting consistent extraction of quantitative imaging biomarkers. This capability facilitates noninvasive assessment of renal graft function and supports longitudinal monitoring of transplant patients.