Weiss Jakob, Bernatz Simon, Johnson Justin, Thiriveedhi Vamsi, Mak Raymond H, Fedorov Andriy, Lu Michael T, Aerts Hugo J W L
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine (HIM), Boston, Massachusetts, USA.
Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.
J Intern Med. 2025 Mar;297(3):276-288. doi: 10.1111/joim.20053. Epub 2025 Jan 27.
Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease.
To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST).
We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4 ± 5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [BMI] < 25 kg/m) and overweight (BMI ≥ 25 kg/m). The primary outcome was all-cause mortality. Cox proportional hazard regression assessed the association between (1) SLD and mortality at baseline and (2) the association between a change in HFF and mortality within 1 year.
There were 5.1% (1000/19,760) all-cause deaths over a median follow-up of 6 (range, 0.8-6) years. At baseline, SLD was associated with increased mortality in lean but not in overweight/obese participants as compared to participants without SLD (hazard ratio [HR] adjusted for risk factors: 1.93 [95% confidence interval 1.52-2.45]; p = 0.001). Individuals with an increase in HFF within 1 year had a significantly worse outcome than participants with stable HFF (HR adjusted for risk factors: 1.29 [1.01-1.65]; p = 0.04).
SLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors.
脂肪性肝病（SLD）是一种潜在可逆的病症，但往往未被察觉，存在发展为终末期肝病的风险。
在肺癌筛查胸部计算机断层扫描（CT）中机会性地评估SLD，并研究其在参与国家肺癌筛查试验（NLST）的重度吸烟者中的预后价值。
我们使用深度学习模型，对19774名NLST参与者（年龄61.4±5.0岁；41.2%为女性）的非增强胸部CT扫描图像进行肝脏分割，基线时以及在未检测到癌症的情况下进行1年随访扫描时均进行分割。SLD定义为根据分割肝脏的亨氏单位测量得出的肝脂肪分数（HFF）≥5%。患有SLD的参与者分为瘦型（体重指数[BMI]<25kg/m²）和超重型（BMI≥25kg/m²）。主要结局是全因死亡率。Cox比例风险回归评估了（1）基线时SLD与死亡率之间的关联，以及（2）HFF变化与1年内死亡率之间的关联。
在中位随访6（范围0.8 - 6）年期间，全因死亡率为5.1%（1000/19760）。与无SLD的参与者相比，基线时，瘦型参与者中SLD与死亡率增加相关，而超重/肥胖参与者中则不然（校正风险因素后的风险比[HR]：1.93[95%置信区间1.52 - 2.45]；p = 0.001）。1年内HFF增加的个体结局明显比HFF稳定的参与者差（校正风险因素后的HR：1.29[1.01 - 1.65]；p = 0.04）。
SLD是重度吸烟者长期死亡率的独立预测因素，超出了已知的临床风险因素。
