应用人工智能量化腹部ct上的身体成分，更好地预测肾移植等待名单死亡率

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology Pub Date : 2025-03-01 DOI:10.1016/j.jacr.2025.01.004

Karim Yatim MD , Guilherme T. Ribas PhD , Daniel C. Elton PhD , Marcio A.B.C. Rockenbach MD , Ayman Al Jurdi MD , Perry J. Pickhardt MD , John W. Garrett PhD , Keith J. Dreyer DO, PhD , Bernardo C. Bizzo MD, PhD , Leonardo V. Riella MD, PhD

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引用次数: 0

摘要

背景：肾移植前的常规评估包括术前腹部CT血管评估。从这些CT检查中可以获得丰富的身体成分数据，但它们仍然是一个未充分利用的数据来源，通常在预测模型中缺失，因为这些测量需要器官分割，而不是放射科医生在临床上常规进行的。我们假设人工智能有助于准确提取腹部CT身体成分数据，从而更好地预测结果。方法：我们对2007年1月1日至2017年12月31日期间等待肾移植候选人进行了回顾性、单中心观察研究，并提供了可用的CT数据。经过验证的深度学习模型量化了身体成分，包括脂肪、主动脉钙化、骨密度和肌肉质量。采用Logistic回归比较体成分数据与移植后预期生存评分（EPTS）作为5年等待名单死亡率的预测因子。结果899例患者共随访943天（四分位数范围320 ~ 1697）。899人中有589人（65.5%）是男性，899人中有680人（75.6%）是白人，非西班牙裔。在899名患者中，167名患者（18.6%）在等待名单上死亡。肌骨化病（定义为肌肉衰减的最低分位数）、总主动脉和腹部钙化增加与5年等候名单死亡率增加相关。Logistic回归显示，影像学参数与EPTS在预测5年等待名单死亡率方面的表现相似（受试者工作特征曲线下面积分别为0.70[0.64-0.75]和0.67[0.62-0.72]），并且将身体组成参数与EPTS结合可以轻微改善生存预测（受试者工作特征曲线下面积= 0.72,95%置信区间为0.66-0.76）。结论肾移植候选体成分全自动定量分析是可行的。骨骼肌病和动脉粥样硬化与5年等候名单死亡率相关。

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Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality

Background

Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing from prognostication models, as these measurements require organ segmentation not routinely performed clinically by radiologists. We hypothesize that artificial intelligence facilitates accurate extraction of abdominal CT body composition data, allowing better prediction of outcomes.

Methods

We conducted a retrospective, single-center observational study of kidney transplant candidates wait-listed between January 1, 2007, and December 31, 2017, with available CT data. Validated deep learning models quantified body composition including fat, aortic calcification, bone density, and muscle mass. Logistic regression was used to compare body composition data to Expected Post-Transplant Survival Score (EPTS) as a predictor of 5-year wait-list mortality.

Results

In all, 899 patients were followed for a median 943 days (interquartile range 320-1,697). Of 899, 589 (65.5%) were men and 680 of 899 (75.6%) were White, non-Hispanic. Of 899, 167 patients (18.6%) died while on the waiting list. Myosteatosis (defined as the lowest tertile of muscle attenuation) and increased total aortic and abdominal calcification were associated with increased 5-year wait-list mortality. Logistic regression showed that imaging parameters performed similarly to EPTS at predicting 5-year wait-list mortality (area under receiver operating characteristic curve 0.70 [0.64-0.75] versus 0.67 [0.62-0.72], respectively), and combining body composition parameters with EPTS led to a slight improved survival prediction (area under receiver operating characteristic curve = 0.72, 95% confidence interval 0.66-0.76).

Conclusions

Fully automated quantification of body composition in kidney transplant candidates is feasible. Myosteatosis and atherosclerosis are associated with 5-year wait-list mortality.

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来源期刊

Journal of the American College of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

6.30

自引率

8.90%

发文量

312

审稿时长

34 days

期刊介绍： The official journal of the American College of Radiology, JACR informs its readers of timely, pertinent, and important topics affecting the practice of diagnostic radiologists, interventional radiologists, medical physicists, and radiation oncologists. In so doing, JACR improves their practices and helps optimize their role in the health care system. By providing a forum for informative, well-written articles on health policy, clinical practice, practice management, data science, and education, JACR engages readers in a dialogue that ultimately benefits patient care.