术前预测内脏胸膜侵犯(VPI)很重要,因为它使胸外科医师能够选择合适的手术计划。本研究旨在开发和验证一种多变量逻辑回归模型,该模型结合了最大标准化摄取值(SUVmax)和有价值的计算机断层扫描(CT)体征,用于无创预测胸膜下临床分期IA肺腺癌患者的VPI状态。
共招募140例胸膜下临床IA期周围型肺腺癌患者,并将其分为训练集(n=98)和验证集(n=42),根据正电子发射断层扫描/CT检查时间顺序,以7:3的比例。接下来,根据病理结果形成VPI阳性和VPI阴性组。在训练集中,临床信息,SUVmax,肿瘤和胸膜的关系,CT特征采用单因素分析。将差异显著的变量纳入多变量分析,构建预测模型。建立了基于多变量分析的列线图,并在验证集中验证了其预测性能。
固体成分的大小,巩固与肿瘤的比率,固体成分胸膜接触长度,SUVmax,密度类型,胸膜凹陷,刺突,在训练集中的单变量分析中,血管收敛信号显示VPI阳性(n=40)和VPI阴性(n=58)病例之间存在显着差异。多元逻辑回归模型包含SUVmax[优势比(OR):1.753,P=0.002],实质成分胸膜接触长度(OR:1.101,P=0.034),胸膜凹陷(OR:5.075,P=0.041),以血管收敛信号(OR:13.324,P=0.025)为最佳预测因子组合,均为训练组VPI的独立危险因素。列线图表明有希望的歧视,曲线下面积值为0.892[95%置信区间(CI),在训练集中为0.813-0.946],在验证集中为0.885(95%CI,0.748-0.962)。校准曲线表明其预测概率与实际概率一致。决策曲线分析表明,当前的列线图将增加更多的净收益。
结合SUVmax和CT特征的列线图可以无创预测胸膜下临床分期IA肺腺癌患者术前的VPI状态。
Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUVmax) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery.
A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUVmax, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set.
The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUVmax [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit.
The nomogram integrating the SUVmax and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.