Machine Learning with Multiparametric MRI and Clinical Biomarkers for Noninvasive Renal Interstitial Fibrosis Staging

Bioengineering 2026 Open access

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Authors: Kexin Wang, Tao Zhao, Tao Su, Y Jiang, Lei Jiang, Jianxing Qiu, Shuo Quan, Jiangtao Liu, Rui Wang
DOI: 10.3390/bioengineering13060704
Cited by: 1
References: 26

Abstract

Renal interstitial fibrosis (RIF) is currently assessed by invasive biopsy. This prospective study developed and validated a noninvasive random forest model combining multiparametric MRI and clinical biomarkers for identifying severe RIF in 116 patients with biopsy-confirmed renal disease. Quantitative parameters were extracted from IVIM, ASL, phase-contrast MRI, T1 mapping, and BOLD sequences. Fibrosis was classified as mild (

Fields of Study & Subjects

MRI in cancer diagnosis Chronic Kidney Disease and Diabetes Renal cell carcinoma treatment

Publication details

Journal / source
Bioengineering
Publisher
MDPI AG
Volume
13
Issue
6
Pages
704–704
Publication date
19 June 2026
Language
EN