PDF(Pubmed)
Abstract：
BACKGROUND: Identification of micropapillary and solid subtypes components in small-sized (≤ 2 cm) lung adenocarcinoma plays a crucial role in determining optimal surgical procedures. This study aims to propose a straightforward prediction method utilizing preoperative available indicators.
METHODS: From January 2019 to July 2022, 341 consecutive patients with small-sized lung adenocarcinoma who underwent curative resection in thoracic surgery department of Xuanwu Hospital, Capital Medical University were retrospectively analyzed. The patients were divided into two groups based on whether solid or micropapillary components ≥ 5% or not (S/MP5+ and S/MP5-). Univariate analysis and multivariate logistic regression analysis were utilized to identify independent predictors of S/MP5+. Then a nomogram was constructed to intuitively show the results. Finally, the calibration curve with a 1000 bootstrap resampling and the receiver operating characteristic (ROC) curve were depicted to evaluate its performance.
RESULTS: According to postoperative pathological results, 79 (23.2%) patients were confirmed as S/MP5+ while 262 (76.8%) patients were S/MP5-. Based on multivariate analysis, maximum diameter (p = 0.010), consolidation tumor ratio (CTR) (p < 0.001) and systemic immune-inflammation index (SII) (p < 0.001) were identified as three independent risk factors and incorporated into the nomogram. The calibration curve showed good concordance between the predicted and actual probability of S/MP5+. Besides, the model showed certain discrimination, with an area under ROC curve of 0.893.
CONCLUSIONS: The model constructed based on SII is a practical tool to predict high-grade subtypes components of small-sized lung adenocarcinoma preoperatively and contribute to determine the optimal surgical approach.
METHODS: From January 2019 to July 2022, 341 consecutive patients with small-sized lung adenocarcinoma who underwent curative resection in thoracic surgery department of Xuanwu Hospital, Capital Medical University were retrospectively analyzed. The patients were divided into two groups based on whether solid or micropapillary components ≥ 5% or not (S/MP5+ and S/MP5-). Univariate analysis and multivariate logistic regression analysis were utilized to identify independent predictors of S/MP5+. Then a nomogram was constructed to intuitively show the results. Finally, the calibration curve with a 1000 bootstrap resampling and the receiver operating characteristic (ROC) curve were depicted to evaluate its performance.
RESULTS: According to postoperative pathological results, 79 (23.2%) patients were confirmed as S/MP5+ while 262 (76.8%) patients were S/MP5-. Based on multivariate analysis, maximum diameter (p = 0.010), consolidation tumor ratio (CTR) (p < 0.001) and systemic immune-inflammation index (SII) (p < 0.001) were identified as three independent risk factors and incorporated into the nomogram. The calibration curve showed good concordance between the predicted and actual probability of S/MP5+. Besides, the model showed certain discrimination, with an area under ROC curve of 0.893.
CONCLUSIONS: The model constructed based on SII is a practical tool to predict high-grade subtypes components of small-sized lung adenocarcinoma preoperatively and contribute to determine the optimal surgical approach.
摘要：
背景：小尺寸（≤2cm）肺腺癌中微乳头状和实体亚型成分的鉴定在确定最佳手术程序中起着至关重要的作用。本研究旨在提出一种利用术前可用指标的直接预测方法。
方法：自2019年1月至2022年7月，在宣武医院胸外科接受根治性切除术的小肺腺癌患者341例，对首都医科大学进行回顾性分析。根据固体或微乳头状成分是否≥5％（S/MP5和S/MP5-）将患者分为两组。单因素分析和多因素logistic回归分析用于确定S/MP5+的独立预测因子。然后构造一个列线图以直观地显示结果。最后,描绘了具有1000个自举重新采样的校准曲线和接收器工作特征（ROC）曲线以评估其性能。
结果：根据术后病理结果，79例(23.2%)患者为S/MP5+，262例(76.8%)患者为S/MP5-。基于多变量分析，最大直径（p=0.010），合并肿瘤比率（CTR）（p<0.001）和全身免疫炎症指数（SII）（p<0.001）被确定为三个独立的危险因素，并纳入列线图。校准曲线显示S/MP5+的预测概率和实际概率之间的良好一致性。此外,该模型显示出一定的歧视，ROC曲线下面积为0.893。
结论：基于SII构建的模型是预测小型肺腺癌术前高级别亚型成分的实用工具，有助于确定最佳手术方式。
方法：自2019年1月至2022年7月，在宣武医院胸外科接受根治性切除术的小肺腺癌患者341例，对首都医科大学进行回顾性分析。根据固体或微乳头状成分是否≥5％（S/MP5和S/MP5-）将患者分为两组。单因素分析和多因素logistic回归分析用于确定S/MP5+的独立预测因子。然后构造一个列线图以直观地显示结果。最后,描绘了具有1000个自举重新采样的校准曲线和接收器工作特征（ROC）曲线以评估其性能。
结果：根据术后病理结果，79例(23.2%)患者为S/MP5+，262例(76.8%)患者为S/MP5-。基于多变量分析，最大直径（p=0.010），合并肿瘤比率（CTR）（p<0.001）和全身免疫炎症指数（SII）（p<0.001）被确定为三个独立的危险因素，并纳入列线图。校准曲线显示S/MP5+的预测概率和实际概率之间的良好一致性。此外,该模型显示出一定的歧视，ROC曲线下面积为0.893。
结论：基于SII构建的模型是预测小型肺腺癌术前高级别亚型成分的实用工具，有助于确定最佳手术方式。
