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Kidney Transplant I: Introduction

Kidney Transplant I: Introduction

A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...

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Updated: Sep 20, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic

Léo Milecki¹, Sylvain Bodard², Vicky Kalogeiton³

¹MICS, CentraleSupelec, Paris-Saclay University, Inria Saclay, 9 Rue Joliot Curie, 91190 Gif-sur-Yvette, France (L.M., M.V.).

Academic Radiology

|May 24, 2025

Summary

This summary is machine-generated.

Artificial intelligence can predict renal transplant survival using radiomic features from early MRI scans. This approach shows promise for improving patient outcomes and managing kidney transplant care.

Keywords:

Deep learning Dynamic contrast-enhanced MRI Radiomics Renal transplantation Survival analysis

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Last Updated: Sep 20, 2025

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Non-invasive Imaging of Acute Allograft Rejection after Rat Renal Transplantation Using 18F-FDG PET

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Published on: April 28, 2013

Area of Science:

Nephrology
Radiology
Artificial Intelligence

Background:

End-stage renal disease necessitates renal transplantation, the most effective treatment.
Graft survival prediction traditionally relies on clinical and pathological data.
Medical imaging is currently limited to assessing transplant status.

Purpose of the Study:

To investigate unsupervised deep learning algorithms for identifying radiomic features linked to renal transplant survival.
To analyze early dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data for predictive markers.

Main Methods:

Retrospective cohort of 108 renal transplant patients (2013-2015).
Deep convolutional neural networks with unsupervised contrastive learning for AI radiomics feature extraction.
5-year graft survival analysis using penalized Cox models and Kaplan-Meier estimates.

Main Results:

AI radiomics features from 1-month post-transplantation MRI showed a 72.7% concordance index for 5-year graft survival.
Unsupervised clustering of radiomic features significantly stratified patients (p=0.029).

Conclusions:

AI algorithms can extract relevant radiomic features for predicting renal transplant survival.
This proof-of-concept study highlights the potential of AI in transplant care.
Further research is needed to validate and integrate this technique into clinical practice.