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Clinical and Radiological Fusion: A New Frontier in Predicting Post-Transplant Diabetes Mellitus
by
Cheungpasitporn, Wisit
, Budhiraja, Pooja
, Wadei, Hani M.
, Kline, Timothy L.
, Jadlowiec, Caroline C.
, Kudva, Yogish C.
, Alajous, Salah
, Stegall, Mark D.
, Rios, Ian P.
, Smith, Byron H.
, Kukla, Aleksandra
, Chakkera, Harini A.
, Me, Hay Me
, Misra, Suman S.
, Korfiatis, Panagiotis
in
Abdomen
/ Adipose tissue
/ adiposity
/ Adult
/ Aged
/ Automation
/ Biomarkers
/ Body Composition
/ Body fat
/ Body Mass Index
/ Computed tomography
/ Deep Learning
/ Diabetes
/ Diabetes mellitus
/ Diabetes Mellitus - diagnosis
/ Diabetes Mellitus - diagnostic imaging
/ Diabetes Mellitus - etiology
/ Family medical history
/ Female
/ Glucose
/ Health Archive
/ Humans
/ kidney transplant
/ Kidney transplantation
/ Kidney Transplantation - adverse effects
/ Kidney transplants
/ Kidneys
/ Male
/ Medical imaging
/ Metabolism
/ Middle Aged
/ Mortality
/ Muscles
/ Musculoskeletal system
/ Obesity
/ post transplant diabetes
/ Postoperative Complications - diagnostic imaging
/ Prediction models
/ Retrospective Studies
/ Review boards
/ Risk Factors
/ Skeletal muscle
/ Steroids
/ Tomography, X-Ray Computed
/ Triglycerides
/ Vertebrae
/ visceral diposity
2025
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Clinical and Radiological Fusion: A New Frontier in Predicting Post-Transplant Diabetes Mellitus
by
Cheungpasitporn, Wisit
, Budhiraja, Pooja
, Wadei, Hani M.
, Kline, Timothy L.
, Jadlowiec, Caroline C.
, Kudva, Yogish C.
, Alajous, Salah
, Stegall, Mark D.
, Rios, Ian P.
, Smith, Byron H.
, Kukla, Aleksandra
, Chakkera, Harini A.
, Me, Hay Me
, Misra, Suman S.
, Korfiatis, Panagiotis
in
Abdomen
/ Adipose tissue
/ adiposity
/ Adult
/ Aged
/ Automation
/ Biomarkers
/ Body Composition
/ Body fat
/ Body Mass Index
/ Computed tomography
/ Deep Learning
/ Diabetes
/ Diabetes mellitus
/ Diabetes Mellitus - diagnosis
/ Diabetes Mellitus - diagnostic imaging
/ Diabetes Mellitus - etiology
/ Family medical history
/ Female
/ Glucose
/ Health Archive
/ Humans
/ kidney transplant
/ Kidney transplantation
/ Kidney Transplantation - adverse effects
/ Kidney transplants
/ Kidneys
/ Male
/ Medical imaging
/ Metabolism
/ Middle Aged
/ Mortality
/ Muscles
/ Musculoskeletal system
/ Obesity
/ post transplant diabetes
/ Postoperative Complications - diagnostic imaging
/ Prediction models
/ Retrospective Studies
/ Review boards
/ Risk Factors
/ Skeletal muscle
/ Steroids
/ Tomography, X-Ray Computed
/ Triglycerides
/ Vertebrae
/ visceral diposity
2025
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Clinical and Radiological Fusion: A New Frontier in Predicting Post-Transplant Diabetes Mellitus
by
Cheungpasitporn, Wisit
, Budhiraja, Pooja
, Wadei, Hani M.
, Kline, Timothy L.
, Jadlowiec, Caroline C.
, Kudva, Yogish C.
, Alajous, Salah
, Stegall, Mark D.
, Rios, Ian P.
, Smith, Byron H.
, Kukla, Aleksandra
, Chakkera, Harini A.
, Me, Hay Me
, Misra, Suman S.
, Korfiatis, Panagiotis
in
Abdomen
/ Adipose tissue
/ adiposity
/ Adult
/ Aged
/ Automation
/ Biomarkers
/ Body Composition
/ Body fat
/ Body Mass Index
/ Computed tomography
/ Deep Learning
/ Diabetes
/ Diabetes mellitus
/ Diabetes Mellitus - diagnosis
/ Diabetes Mellitus - diagnostic imaging
/ Diabetes Mellitus - etiology
/ Family medical history
/ Female
/ Glucose
/ Health Archive
/ Humans
/ kidney transplant
/ Kidney transplantation
/ Kidney Transplantation - adverse effects
/ Kidney transplants
/ Kidneys
/ Male
/ Medical imaging
/ Metabolism
/ Middle Aged
/ Mortality
/ Muscles
/ Musculoskeletal system
/ Obesity
/ post transplant diabetes
/ Postoperative Complications - diagnostic imaging
/ Prediction models
/ Retrospective Studies
/ Review boards
/ Risk Factors
/ Skeletal muscle
/ Steroids
/ Tomography, X-Ray Computed
/ Triglycerides
/ Vertebrae
/ visceral diposity
2025
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Clinical and Radiological Fusion: A New Frontier in Predicting Post-Transplant Diabetes Mellitus
Journal Article
Clinical and Radiological Fusion: A New Frontier in Predicting Post-Transplant Diabetes Mellitus
2025
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Overview
This study developed a predictive model for Post-Transplant Diabetes Mellitus (PTDM) by integrating clinical and radiological data to identify at-risk kidney transplant recipients. In a retrospective analysis across three Mayo Clinic sites, clinical metrics were combined with deep learning analysis of pre-transplant CT images, focusing on body composition parameters like adipose tissue and muscle mass instead of BMI or other biomarkers. Among 2,005 nondiabetic kidney recipients, 335 (16.7%) developed PTDM within the first year. PTDM patients were older, had higher BMIs, elevated triglycerides, and were more likely to be male and non-White. They exhibited lower skeletal muscle area, greater visceral adipose tissue (VAT), more intermuscular fat, and higher subcutaneous fat (all p < 0.001). Multivariable analysis identified age (OR: 1.05, 95% CI: 1.03–1.08, p < 0.0001), family diabetes history (OR: 1.55, CI: 1.14–2.09, p = 0.0061), White race (OR: 0.43, CI: 0.28–0.66, p < 0.0001), and VAT area (OR: 1.37, CI: 1.14–1.64, p = 0.0009) as predictors. The combined model achieved C-statistic of 0.724 (CI: 0.692–0.757), outperforming the clinical-only model (C-statistic 0.68). Patients with PTDM in the first year had higher mortality than those without PTDM. This model improves predictive precision, enabling accurate identification and intervention for at risk patients.
Publisher
Frontiers Media SA,Frontiers Media S.A
Subject
/ Adult
/ Aged
/ Body fat
/ Diabetes
/ Diabetes Mellitus - diagnosis
/ Diabetes Mellitus - diagnostic imaging
/ Diabetes Mellitus - etiology
/ Female
/ Glucose
/ Humans
/ Kidney Transplantation - adverse effects
/ Kidneys
/ Male
/ Muscles
/ Obesity
/ Postoperative Complications - diagnostic imaging
/ Steroids
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