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肿瘤影像学特征与周围型 I 期肺腺癌气腔内播散的相关性:倾向评分匹配分析。

The correlation between tumor radiological features and spread through air spaces in peripheral stage IA lung adenocarcinoma: a propensity score-matched analysis.

机构信息

Department of Radiology, The Xuzhou Hospital Affiliated to Jiangsu University, Xu Zhou, Jiang Su, 221004, People's Republic of China.

Department of Thoracic Surgery, Xuzhou Cancer Hospital, Xuzhou, 221000, People's Republic of China.

出版信息

J Cardiothorac Surg. 2024 Jan 23;19(1):19. doi: 10.1186/s13019-024-02498-0.

DOI:10.1186/s13019-024-02498-0
PMID:38263158
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10804508/
Abstract

BACKGROUND

The consolidation tumor ratio (CTR) is a predictor of invasiveness in peripheral T1N0M0 lung adenocarcinoma. However, its association with spread through air spaces (STAS) remains largely unexplored. We aimed to explore the correlation between the CTR of primary tumors and STAS in peripheral T1N0M0 lung adenocarcinoma.

METHODS

We collected data from patients who underwent surgery for malignant lung neoplasms between January and November 2022. Univariate and multivariate analyses following propensity-score matching with sex, age, BMI, were performed to identify the independent risk factors for STAS. The incidence of STAS was compared based on pulmonary nodule type. A smooth fitting curve between CTR and STAS was produced by the generalized additive model (GAM) and a multiple regression model was established using CTR and STAS to determine the dose-response relationship and calculate the odds ratio (OR) and 95% confidence interval (CI).

RESULTS

17 (14.5%) were diagnosed with STAS. The univariate analysis demonstrated that the history of the diabetes, size of solid components, spiculation, pleural indentation, pulmonary nodule type, consolidation/tumor ratio of the primary tumor were statistically significant between the STAS-positive and STAS-negative groups following propensity-score matching(p = 0.047, 0.049, 0.030, 0.006, 0.026, and < 0.001, respectively), and multivariate analysis showed that the pleural indentation was independent risk factors for STAS (with p-value and 95% CI of 0.043, (8.543-68.222)). Moreover, the incidence of STAS in the partially solid nodule was significantly different from that in the solid nodule and ground-glass nodule (Pearson Chi-Square = 7.49, p = 0.024). Finally, the smooth fitting curve showed that CTR tended to be linearly associated with STAS by GAM, and the multivariate regression model based on CTR showed an OR value of 1.24 and a p-value of 0.015.

CONCLUSIONS

In peripheral stage IA lung adenocarcinoma, the risk of STAS was increased with the solid component of the primary tumor. The pleural indentation of the primary tumor could be used as a predictor in evaluating the risk of the STAS.

摘要

背景

在 T1N0M0 期周围型肺腺癌中,肿瘤实变率(CTR)是侵袭性的预测因子。然而,其与气腔内播散(STAS)的相关性仍在很大程度上尚未得到探索。我们旨在探讨 T1N0M0 期周围型肺腺癌中,原发肿瘤 CTR 与 STAS 之间的相关性。

方法

我们收集了 2022 年 1 月至 11 月期间因恶性肺肿瘤接受手术的患者的数据。采用倾向性评分匹配,对性别、年龄、BMI 进行单因素和多因素分析,以确定 STAS 的独立危险因素。根据肺结节类型比较 STAS 的发生率。通过广义加性模型(GAM)生成 CTR 与 STAS 之间的平滑拟合曲线,并建立多回归模型,以确定剂量反应关系并计算比值比(OR)和 95%置信区间(CI)。

结果

17 例(14.5%)诊断为 STAS。单因素分析表明,在经过倾向性评分匹配后,STAS 阳性组与 STAS 阴性组之间,糖尿病史、实性成分大小、分叶征、胸膜凹陷征、肺结节类型、原发肿瘤的实变/肿瘤比值具有统计学意义(p=0.047、0.049、0.030、0.006、0.026 和 <0.001),多因素分析表明胸膜凹陷征是 STAS 的独立危险因素(p 值和 95%CI 为 0.043,(8.543-68.222))。此外,部分实性结节的 STAS 发生率与实性结节和磨玻璃结节显著不同(Pearson Chi-Square=7.49,p=0.024)。最后,GAM 显示的平滑拟合曲线表明,CTR 与 STAS 呈线性相关,基于 CTR 的多变量回归模型显示 OR 值为 1.24,p 值为 0.015。

结论

在 I 期周围型肺腺癌中,原发肿瘤实性成分的增加会增加 STAS 的风险。原发肿瘤的胸膜凹陷征可作为评估 STAS 风险的预测因子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/04e5e1bf2bc3/13019_2024_2498_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/cd6720f88015/13019_2024_2498_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/ee665efa7d93/13019_2024_2498_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/9a86b4f1e960/13019_2024_2498_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/04e5e1bf2bc3/13019_2024_2498_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/cd6720f88015/13019_2024_2498_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/ee665efa7d93/13019_2024_2498_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/9a86b4f1e960/13019_2024_2498_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/58cb/10804508/04e5e1bf2bc3/13019_2024_2498_Fig3_HTML.jpg

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本文引用的文献

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Quant Imaging Med Surg. 2022 Jan;12(1):159-171. doi: 10.21037/qims-21-394.
2
The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015.2021 年世卫组织肺肿瘤分类:自 2015 年以来的进展影响。
J Thorac Oncol. 2022 Mar;17(3):362-387. doi: 10.1016/j.jtho.2021.11.003. Epub 2021 Nov 20.
3
Nomogram model for the preoperative prediction of spread through air spaces in sub-centimeter non-small cell lung cancer.
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J Cardiothorac Surg. 2025 Apr 23;20(1):218. doi: 10.1186/s13019-025-03441-7.
4
Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.利用自动分割深度学习模型预测外周I期肺腺癌肿瘤在气腔内的扩散。
Respir Res. 2025 Mar 8;26(1):94. doi: 10.1186/s12931-025-03174-0.
5
Prediction of early lung adenocarcinoma spread through air spaces by machine learning radiomics: a cross-center cohort study.通过机器学习影像组学预测早期肺腺癌的气腔播散:一项跨中心队列研究
Transl Lung Cancer Res. 2024 Dec 31;13(12):3443-3459. doi: 10.21037/tlcr-24-565. Epub 2024 Dec 27.
6
Prognostic Impact and Clinical Features of Spread through Air Spaces in Operated Lung Cancer: Real-World Analysis.肺癌术中空气传播蔓延对预后的影响及临床特征:真实世界分析。
Medicina (Kaunas). 2024 Aug 22;60(8):1374. doi: 10.3390/medicina60081374.
Lung cancer.
肺癌。
Lancet. 2021 Aug 7;398(10299):535-554. doi: 10.1016/S0140-6736(21)00312-3. Epub 2021 Jul 21.
4
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Clin Lung Cancer. 2021 May;22(3):e431-e437. doi: 10.1016/j.cllc.2020.06.013. Epub 2020 Jun 19.
5
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N Engl J Med. 2020 Feb 6;382(6):503-513. doi: 10.1056/NEJMoa1911793. Epub 2020 Jan 29.
6
Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment.非小细胞肺癌:流行病学、筛查、诊断和治疗。
Mayo Clin Proc. 2019 Aug;94(8):1623-1640. doi: 10.1016/j.mayocp.2019.01.013.
7
Limited Resection Is Associated With a Higher Risk of Locoregional Recurrence than Lobectomy in Stage I Lung Adenocarcinoma With Tumor Spread Through Air Spaces.在以气腔播散为特征的Ⅰ期肺腺癌中,与肺叶切除术相比,局限性切除术与更高的局部区域复发风险相关。
Am J Surg Pathol. 2019 Aug;43(8):1033-1041. doi: 10.1097/PAS.0000000000001285.
8
Centrally located lung cancer and risk of occult nodal disease: an objective evaluation of multiple definitions of tumour centrality with dedicated imaging software.中央型肺癌与隐匿性淋巴结疾病风险:利用专用成像软件对肿瘤中央性的多种定义进行客观评估。
Eur Respir J. 2019 May 9;53(5). doi: 10.1183/13993003.02220-2018. Print 2019 May.
9
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Ann Thorac Surg. 2019 May;107(5):1500-1506. doi: 10.1016/j.athoracsur.2018.11.046. Epub 2018 Dec 21.
10
Lung Cancer Screening with Low-Dose CT: Baseline Screening Results in Shanghai.低剂量 CT 肺癌筛查:上海基线筛查结果。
Acad Radiol. 2019 Oct;26(10):1283-1291. doi: 10.1016/j.acra.2018.12.002. Epub 2018 Dec 14.