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多原发肺腺癌生存的预后因素:283 例患者的回顾性分析。

Prognostic Factors for Survival in Multiple Primary Lung Adenocarcinomas: A Retrospective Analysis of 283 Patients.

机构信息

Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Hubei Province Key Laboratory of Molecular Imaging, Wuhan, People's Republic of China.

出版信息

Technol Cancer Res Treat. 2023 Jan-Dec;22:15330338231185278. doi: 10.1177/15330338231185278.

DOI:10.1177/15330338231185278
PMID:37365877
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10327415/
Abstract

In recent years, a rising number of multiple primary lung cancers have been detected with the advancement of imaging technology. No detailed study has assessed the prognosis of multiple primary lung adenocarcinomas based on computed tomography characteristics. The present study aimed to analyze outcomes and determine valuable factors for predicting the prognosis of multiple primary lung adenocarcinoma. This single-center retrospective study was performed from January 2013 to October 2021. All patients were divided into 3 groups based on tumor density as follows: multi-pure ground-glass nodules, at least one part-solid nodule without solid nodules, and at least one solid nodule. Clinicopathologic features, computed tomography signs, and survival outcomes were compared between these groups. The Kaplan-Meier method was used for survival analysis. The multivariable Cox proportional hazards regression model was used to identify independent predictors for recurrence-free survival and overall survival. The sample included 283 patients with 623 lesions who met the inclusion criteria for multiple primary lung adenocarcinoma. Of these patients, 71 (25.1%) presented with multi-pure ground-glass nodules, 100 (35.3%) with at least one part-solid nodule without solid nodule, and 112 (39.6%) with at least one solid nodule. The 3 groups had distinguished clinicopathologic and radiological features of age, adjuvant therapy, types of tumor resection, TNM stage, pathological subtypes, pleural indentation, spicule, and vacuole (all  < .001). Multivariate analysis found that lesion number was an independent predictor for both recurrence-free survival (hazard ratio 2.41; 95% confidence interval 1.12-5.19;  = .025) and overall survival (hazard ratio 4.78; 95% confidence interval 1.88-12.18;  = .001), and the at least one solid nodule was an independent predictor for overall survival (hazard ratio 5.307; 95% confidence interval 1.16-24.31; = .032). Stage III (hazard ratio 5.71; 95% confidence interval 1.94-16.81; = .002) and adjuvant therapy (hazard ratio 2.52; 95% confidence interval 1.24-5.13; = .011) influenced the recurrence-free survival. Survival of multiple primary lung adenocarcinoma patients is strongly correlated with the lesion number and the at least one solid nodule tumors in radiological. This information may be useful for predicting survival and making clinical decisions in future studies.

摘要

近年来,随着影像学技术的进步,越来越多的多原发肺癌被检测到。目前还没有详细的研究基于 CT 特征来评估多原发肺腺癌的预后。本研究旨在分析结果并确定有价值的因素来预测多原发肺腺癌的预后。

这项单中心回顾性研究于 2013 年 1 月至 2021 年 10 月进行。所有患者均根据肿瘤密度分为 3 组:多纯磨玻璃结节、至少一个部分实性结节无实性结节和至少一个实性结节。比较了这些组之间的临床病理特征、CT 征象和生存结果。使用 Kaplan-Meier 法进行生存分析。使用多变量 Cox 比例风险回归模型确定无复发生存和总生存的独立预测因素。

样本包括 283 名符合多原发肺腺癌纳入标准的患者,共 623 个病灶。其中 71 例(25.1%)表现为多纯磨玻璃结节,100 例(35.3%)为至少一个部分实性结节无实性结节,112 例(39.6%)为至少一个实性结节。三组具有不同的临床病理和影像学特征,包括年龄、辅助治疗、肿瘤切除术类型、TNM 分期、病理亚型、胸膜凹陷、刺突和空泡(均<0.001)。多变量分析发现,病变数量是无复发生存(危险比 2.41;95%置信区间 1.12-5.19;=0.025)和总生存(危险比 4.78;95%置信区间 1.88-12.18;=0.001)的独立预测因素,至少一个实性结节是总生存的独立预测因素(危险比 5.307;95%置信区间 1.16-24.31;=0.032)。III 期(危险比 5.71;95%置信区间 1.94-16.81;=0.002)和辅助治疗(危险比 2.52;95%置信区间 1.24-5.13;=0.011)影响无复发生存。

多原发肺腺癌患者的生存与影像学中的病变数量和至少一个实性结节密切相关。这些信息可能有助于预测未来研究中的生存并做出临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/d80b25ac46a7/10.1177_15330338231185278-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/8591f81c81f1/10.1177_15330338231185278-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/9ad9a47de1ec/10.1177_15330338231185278-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/17f437804fb3/10.1177_15330338231185278-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/d80b25ac46a7/10.1177_15330338231185278-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/8591f81c81f1/10.1177_15330338231185278-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/9ad9a47de1ec/10.1177_15330338231185278-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/17f437804fb3/10.1177_15330338231185278-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4c6/10327415/d80b25ac46a7/10.1177_15330338231185278-fig4.jpg

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