基于活检标本预测早期肺腺癌通过气腔扩散的预测模型
Prediction of spread through air spaces with biopsy specimens in early-stage adenocarcinoma of the lung.
作者
作者单位
- Division of Thoracic Surgery, Department of Surgery, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
- Division of Thoracic Surgery, Department of Surgery, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan. Electronic address: mshimomu@koto.kpu-m.ac.jp.
- Department of Surgical Pathology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
- Department of Pulmonary Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
- Department of Biostatistics, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
- Department of Pathology and Applied Biology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
摘要
中文
本研究旨在利用临床及活检来源的因素,开发一种术前风险评分用于预测小尺寸肺腺癌中的气腔扩散（STAS）。我们回顾性分析了 142 例临床 I 期腺癌（≤ 2 cm）患者资料,这些患者均在术前活检后接受手术。评估了包括患者特征、影像学表现及活检标本中的组织学亚型等潜在预测因素。采用 logistic 回归分析筛选 STAS 的独立预测因子,并构建风险评分,各因素根据最终模型的回归系数加权。通过 1000 次自助法（bootstrapping）进行内部验证,以估算经乐观校正的 AUROC。48 例（33.8%）患者在切除标本中病理确诊为 STAS。多因素分析确定了三个显著预测因子：实变肿瘤比 = 1、最大标准化摄取值（SUVmax）≥ 1.36 以及活检标本中存在 3 级成分（包括实性、微乳头、复杂腺体或筛状结构）。STAS 预测评分定义为：2 分（存在 3 级成分）+ 1 分（SUVmax ≥ 1.36）+ 1 分（实变肿瘤比 = 1）。该评分模型表现出良好的区分度、可接受的校准度,并在内部验证后保持其预测效能。利用结合活检和影像学表现的风险评分系统可在术前预测 STAS。该模型可能有助于早期腺癌患者手术决策中合理选择切除范围。
English
We aimed to develop a preoperative risk score to predict spread through air spaces (STAS) in small-sized adenocarcinoma of the lung using clinical and biopsy-derived factors. We retrospectively analyzed the data of 142 patients with clinical stage I adenocarcinoma (≤2 cm) who underwent surgery after preoperative biopsy. Potential predictive factors, including patient characteristics, imaging findings, and histological subtypes in biopsy specimens, were evaluated. Logistic regression analysis was used to identify independent predictors of STAS and construct a risk score. Each factor was weighted based on regression coefficients of the final model. Internal validation was performed using 1,000x bootstrapping to estimate the optimism corrected AUROC. STAS was pathologically confirmed in resected specimens in 48 patients (33.8%). Multivariable analysis identified three significant predictors: consolidation-to-tumor ratio = 1, maximum standardized uptake value ≥ 1.36, and presence of Grade 3 components in biopsy samples (including solid, micropapillary, complex glandular, or cribriform patterns). The STAS prediction score was defined as: 2 points (Grade 3 component presence) + 1 point (maximum standardized uptake value ≥ 1.36) + 1 point (consolidation-to-tumor ratio = 1). The scoring model demonstrated good discrimination, acceptable calibration and maintained its performance after internal validation. STAS can be predicted preoperatively using a risk scoring system that incorporates biopsy and imaging findings. This model may assist surgical decision-making for the appropriate extent of resection in early-stage adenocarcinoma.
分类与指标
- 研究类型
- 临床研究
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 5.3
- 新锐分区
- 2区