Background: Accurate and noninvasive assessment of split renal dysfunction is crucial, while there is lack of corresponding method clinically.

Purpose: To investigate the feasibility of using diffusion-weighted imaging (DWI)-based radiomics models to evaluate split renal dysfunction.

Methods: We enrolled patients with impaired and normal renal function undergoing renal DWI examination. Glomerular filtration rate (GFR, mL/min) was measured using 99mTc-DTPA scintigraphy, which is reference standard of GFR measurement. The kidneys were classified into normal (GFR ≥40), mildly impaired (20≤ GFR < 40), moderately impaired (10≤ GFR < 20), and severely impaired (GFR < 10) renal function groups. Optimized subsets of radiomics features were selected from renal DWI images and radiomics scores (Rad-score) calculated to discriminate groups with different renal function. The radiomics model (Rad-score based) was developed in a training cohort and validated in a test cohort. Evaluations were conducted on the discrimination, calibration, and clinical application of the method.

Results: The final analysis included 330 kidneys. Logistic regression was used to develop three radiomics models, model A, B, and C, which were used to distinguish normal from impaired, mild from moderate, and moderate from severe renal function, respectively. The area under the curve of the three models were 0.822, 0.704, and 0.887 in the training cohort and 0.843, 0.717, and 0.897 in the test cohort, respectively, indicating efficient discrimination performance.

Conclusions: DWI-based radiomics models have potential for evaluating split renal dysfunction and discriminating between normal and impaired renal function groups and their subgroups.

Download full-text PDF

Source
http://dx.doi.org/10.1002/mp.17131DOI Listing

Publication Analysis

Top Keywords

split renal
12
renal dysfunction
8
diffusion-weighted imaging
8
renal
5
evaluation split
4
dysfunction radiomics
4
radiomics based
4
based magnetic
4
magnetic resonance
4
resonance diffusion-weighted
4

Similar Publications

Introduction: As humanity progresses further into space, astronauts must be increasingly independent from mission control, especially in high-consequence medical scenarios. The high-utility and low-mass nature of point-of-care ultrasound (POCUS) makes this imaging modality ideal for spaceflight mission deployment. However, POCUS operator skill degrades over time, presenting an operational barrier to continuous, effective use.

View Article and Find Full Text PDF

Chronic kidney disease (CKD) imposes a high burden with high mortality and morbidity rates. Early detection of CKD is imperative in preventing the adverse outcomes attributed to the later stages. Therefore, this study aims to utilize machine learning techniques to predict CKD at early stages.

View Article and Find Full Text PDF

Aims: Mycophenolic acid (MPA), the active component of enteric-coated mycophenolate sodium (EC-MPS), exhibits highly variable pharmacokinetics. Only a few population pharmacokinetic (popPK) models and Bayesian estimators (MAP-BE) exist for estimating MPA AUC and all in renal transplantation. This study aimed to develop a popPK model and MAP-BE for MPA AUC estimation using a limited sampling strategy (LSS) in solid organ transplant (SOT), haematopoietic stem cell (HSC) recipients and patients with autoimmune diseases (AID) on EC-MPS.

View Article and Find Full Text PDF

Background: En bloc kidney transplantation (EBKT) involves transplantation of two kidneys, the aorta, and inferior vena cava from a deceased pediatric donor into an adult recipient. Recent articles have shown that EBKT is associated with excellent long-term allograft performance and patient survival. Developmental differences exist between the two transplanted kidneys after EBKT, and it is crucial to assess split renal function.

View Article and Find Full Text PDF

Background: Continuous renal replacement therapy (CRRT) is the favored renal replacement therapy in critically ill patients. Predicting clinical outcomes for CRRT patients is difficult due to population heterogeneity, varying clinical practices, and limited sample sizes.

Objective: We aimed to predict survival to ICUs and hospital discharge in children and young adults receiving CRRT using machine learning (ML) techniques.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!