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Automated abdominal CT imaging biomarkers and clinical frailty measures associated with postoperative deceased-donor liver transplant outcomes.

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Title: Automated abdominal CT imaging biomarkers and clinical frailty measures associated with postoperative deceased-donor liver transplant outcomes.
Authors: Liu, Daniel1 (AUTHOR), Ji, David1 (AUTHOR), Garrett, John W.1 (AUTHOR), Zea, Ryan1 (AUTHOR), Kuchnia, Adam2 (AUTHOR), Summers, Ronald M.3 (AUTHOR), Mezrich, Joshua D.2 (AUTHOR), Pickhardt, Perry J.1 (AUTHOR) ppickhardt2@uwhealth.org
Source: European Radiology. Sep2025, Vol. 35 Issue 9, p5514-5524. 11p.
Subjects: Liver transplantation, Frailty, Treatment effectiveness, Prognosis, Body composition, Artificial intelligence, Computed tomography
Abstract: Objective: To quantify the potential of fully automated CT-based body composition metrics and clinical frailty data in predicting liver transplant recipient postoperative outcomes. Methods: AI-enabled body composition tools were applied to pre-transplant abdominal CT scans in a retrospective cohort of first-time deceased-donor liver transplant recipients. Clinical frailty data (Fried frailty score) was obtained from an established transplant database. Age- and sex-corrected hazard ratios (HRs) were analyzed according to highest-risk quartiles compared with the other three quartiles combined. Area under the receiver operating characteristic curve (ROC AUC) analysis in univariate and multivariate scenarios was also performed. Results: 598 liver transplant recipients (median age, 56 years [IQR, 49–61]; 383 men/215 women) were included from 2005 to 2021. Mean clinical follow-up interval after transplant was 8.6 ± 4.5 years, with 224 deaths (mean interval, 5.3 ± 3.9 years post-transplant) and 246 graft failures (mean interval, 4.7 ± 4.0 years post-transplant) observed. Univariate HRs for post-transplant survival included 1.53 (95% CI, 1.14–2.06) for muscle attenuation, 1.66 (95% Cl, 1.24–2.22) for aortic Agatston score, 1.35 (1.02–1.80) for SAT area, and 1.82 (1.35–2.46) for liver volume. For those meeting the frailty criteria, HR was 2.14 (1.08–4.22). Multivariate 10-year AUC for predicting mortality was 0.675 using liver volume, aortic Agatston score, and muscle attenuation. 10-year univariate AUC for clinical frailty assessment was 0.601 but increased to 0.878 when combined with CT measures. Conclusion: Automated CT measurements of muscle density (myosteatosis), aortic calcification, subcutaneous fat, and liver volume are predictive of mortality in liver transplant recipients. Frailty was likewise predictive. Combining CT and clinical frailty assessment was complementary. Key Points: QuestionWhat is the prognostic value of pre-transplant CT-based body composition measures for deceased-donor liver transplant outcomes, and how do they correlate with frailty assessment? FindingsIncreased post-transplant mortality was associated with pre-transplant increased liver volume, increased abdominal aortic Agatston score, decreased skeletal muscle attenuation, and decreased subcutaneous adipose tissue area. Clinical relevancePre-transplant AI-enabled body composition measures have predictive value for post-transplant survival, offering a novel and objective diagnostic tool to identify high-risk transplant recipients that are complementary to clinical assessments. [ABSTRACT FROM AUTHOR]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Automated abdominal CT imaging biomarkers and clinical frailty measures associated with postoperative deceased-donor liver transplant outcomes.
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  Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Sep2025, Vol. 35 Issue 9, p5514-5524. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Liver+transplantation%22">Liver transplantation</searchLink><br /><searchLink fieldCode="DE" term="%22Frailty%22">Frailty</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Prognosis%22">Prognosis</searchLink><br /><searchLink fieldCode="DE" term="%22Body+composition%22">Body composition</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: To quantify the potential of fully automated CT-based body composition metrics and clinical frailty data in predicting liver transplant recipient postoperative outcomes. Methods: AI-enabled body composition tools were applied to pre-transplant abdominal CT scans in a retrospective cohort of first-time deceased-donor liver transplant recipients. Clinical frailty data (Fried frailty score) was obtained from an established transplant database. Age- and sex-corrected hazard ratios (HRs) were analyzed according to highest-risk quartiles compared with the other three quartiles combined. Area under the receiver operating characteristic curve (ROC AUC) analysis in univariate and multivariate scenarios was also performed. Results: 598 liver transplant recipients (median age, 56 years [IQR, 49–61]; 383 men/215 women) were included from 2005 to 2021. Mean clinical follow-up interval after transplant was 8.6 ± 4.5 years, with 224 deaths (mean interval, 5.3 ± 3.9 years post-transplant) and 246 graft failures (mean interval, 4.7 ± 4.0 years post-transplant) observed. Univariate HRs for post-transplant survival included 1.53 (95% CI, 1.14–2.06) for muscle attenuation, 1.66 (95% Cl, 1.24–2.22) for aortic Agatston score, 1.35 (1.02–1.80) for SAT area, and 1.82 (1.35–2.46) for liver volume. For those meeting the frailty criteria, HR was 2.14 (1.08–4.22). Multivariate 10-year AUC for predicting mortality was 0.675 using liver volume, aortic Agatston score, and muscle attenuation. 10-year univariate AUC for clinical frailty assessment was 0.601 but increased to 0.878 when combined with CT measures. Conclusion: Automated CT measurements of muscle density (myosteatosis), aortic calcification, subcutaneous fat, and liver volume are predictive of mortality in liver transplant recipients. Frailty was likewise predictive. Combining CT and clinical frailty assessment was complementary. Key Points: QuestionWhat is the prognostic value of pre-transplant CT-based body composition measures for deceased-donor liver transplant outcomes, and how do they correlate with frailty assessment? FindingsIncreased post-transplant mortality was associated with pre-transplant increased liver volume, increased abdominal aortic Agatston score, decreased skeletal muscle attenuation, and decreased subcutaneous adipose tissue area. Clinical relevancePre-transplant AI-enabled body composition measures have predictive value for post-transplant survival, offering a novel and objective diagnostic tool to identify high-risk transplant recipients that are complementary to clinical assessments. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00330-025-11523-2
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      – Code: eng
        Text: English
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      – SubjectFull: Liver transplantation
        Type: general
      – SubjectFull: Frailty
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      – SubjectFull: Treatment effectiveness
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              Text: Sep2025
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