Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Lung Cancer. 2023 May;179:107189. doi: 10.1016/j.lungcan.2023.107189. Epub 2023 Apr 8.
To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence.
We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence.
Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years.
Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.
评估计算机断层扫描（CT）扫描得出的身体成分对肺癌术后复发的影响。
我们创建了一个回顾性队列，纳入 363 例接受肺切除术且有经证实的复发、死亡或至少 5 年无事件随访的肺癌患者。基于术前全身 CT 扫描（作为 PET-CT 扫描的一部分采集）和胸部 CT 扫描，分别自动分割和量化 5 种关键身体组织和 10 种肿瘤特征。使用考虑死亡竞争事件的时间依赖性分析来分析身体成分、肿瘤特征、临床信息和病理特征对术后肺癌复发的影响。使用标准化因素的风险比（HR）进行单变量和综合模型的个体显著性评估。5 折交叉验证时间依赖性接受者操作特征分析，重点是 3 年 ROC 曲线下面积（AUC），用于描述预测肺癌复发的能力。
单独预测肺癌复发的身体组织包括内脏脂肪组织（VAT）体积（HR=0.88，p=0.047）、皮下脂肪组织（SAT）密度（HR=1.14，p=0.034）、肌肉间脂肪组织（IMAT）体积（HR=0.83，p=0.002）、肌肉密度（HR=1.27，p<0.001）和总脂肪体积（HR=0.89，p=0.050）。CT 衍生的肌肉和肿瘤特征显著增加了包括临床病理因素的模型，预测 3 年复发的 AUC 为 0.78（95%CI：0.75-0.83）。
当与临床病理因素结合时，身体成分特征（例如肌肉密度或肌肉和肌肉间脂肪组织体积）可以提高复发的预测。
