{"title":"利用胸膜标签的长度来预判肺腺癌的胸膜侵犯。","authors":"Yingdong Chen, Qianwen Huang, Zeyang Lin, Xiaoxi Guo, Yiting Liao, Zhe Li, Anqi Li","doi":"10.3389/fonc.2024.1463568","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Pleural contact is present when the underlying pathology of the pleural tag (PT) involves the pleura. This study aimed to preoperatively predict PI by lung adenocarcinomas (ACCs) with PT, exploring CT imaging parameters indicative of PT consisting of pleura and tumor invasiveness.</p><p><strong>Methods: </strong>This single-center, retrospective study included 84 consecutive patients diagnosed with solid ACCs with PT, who underwent resection at our hospital between May 2019 and July 2023. CT imaging parameters analyzed included: LPT (the length of PT), defined as the shortest distance from the tumor edge to the retracted pleura. Patients were divided into PI -ve group and PI +ve group according to PI status. Regression analyses were used to determine predictive factors for PI.</p><p><strong>Results: </strong>The study evaluated 84 patients (mean age, 62.0 ± 13.8 years; 45 females) pathologically diagnosed with ACCs with PT on CT. Multivariate regression analysis identified tumor size (OR 1.18, 95% CI 1.09-1.29, <i>p</i> = 0.000), LPT (OR 0.48, 95% CI 0.25-0.91, <i>p</i> = 0.03) and multiple PTs to multiple types of pleura (OR 3.58, 95% CI 1.13-11.20, <i>p</i> = 0.03) as independent predictors for PI. The combination of these CT features improved the predictive performance for preoperatively identifying PI, achieving high specificity and moderate accuracy. The sensitivity of predicting PI with only LPT < 3 mm was 96.9%.</p><p><strong>Conclusion: </strong>This study determined that LPT is effective for predetermining PI in ACCs with PT.</p>","PeriodicalId":12482,"journal":{"name":"Frontiers in Oncology","volume":"14 ","pages":"1463568"},"PeriodicalIF":3.5000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11563982/pdf/","citationCount":"0","resultStr":"{\"title\":\"Using the length of pleural tag to predetermine pleural invasion by lung adenocarcinomas.\",\"authors\":\"Yingdong Chen, Qianwen Huang, Zeyang Lin, Xiaoxi Guo, Yiting Liao, Zhe Li, Anqi Li\",\"doi\":\"10.3389/fonc.2024.1463568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Pleural contact is present when the underlying pathology of the pleural tag (PT) involves the pleura. This study aimed to preoperatively predict PI by lung adenocarcinomas (ACCs) with PT, exploring CT imaging parameters indicative of PT consisting of pleura and tumor invasiveness.</p><p><strong>Methods: </strong>This single-center, retrospective study included 84 consecutive patients diagnosed with solid ACCs with PT, who underwent resection at our hospital between May 2019 and July 2023. CT imaging parameters analyzed included: LPT (the length of PT), defined as the shortest distance from the tumor edge to the retracted pleura. Patients were divided into PI -ve group and PI +ve group according to PI status. Regression analyses were used to determine predictive factors for PI.</p><p><strong>Results: </strong>The study evaluated 84 patients (mean age, 62.0 ± 13.8 years; 45 females) pathologically diagnosed with ACCs with PT on CT. Multivariate regression analysis identified tumor size (OR 1.18, 95% CI 1.09-1.29, <i>p</i> = 0.000), LPT (OR 0.48, 95% CI 0.25-0.91, <i>p</i> = 0.03) and multiple PTs to multiple types of pleura (OR 3.58, 95% CI 1.13-11.20, <i>p</i> = 0.03) as independent predictors for PI. The combination of these CT features improved the predictive performance for preoperatively identifying PI, achieving high specificity and moderate accuracy. 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引用次数: 0
摘要
导言:当胸膜标签(PT)的潜在病理涉及胸膜时,就会出现胸膜接触。本研究旨在术前预测伴有PT的肺腺癌(ACCs)的PI,探讨CT成像参数对PT包括胸膜和肿瘤侵袭性的指示作用:这项单中心回顾性研究纳入了2019年5月至2023年7月期间在我院接受切除术的84例连续确诊为伴有PT的实性ACC患者。分析的 CT 成像参数包括LPT(PT长度),定义为肿瘤边缘到牵拉胸膜的最短距离。根据 PI 状态将患者分为 PI -ve 组和 PI +ve 组。回归分析用于确定PI的预测因素:研究评估了 84 例经病理诊断为 ACC 并在 CT 上显示 PT 的患者(平均年龄为 62.0 ± 13.8 岁,女性 45 例)。多变量回归分析发现,肿瘤大小(OR 1.18,95% CI 1.09-1.29,p = 0.000)、LPT(OR 0.48,95% CI 0.25-0.91,p = 0.03)和多种胸膜类型的多个 PT(OR 3.58,95% CI 1.13-11.20,p = 0.03)是 PI 的独立预测因素。这些 CT 特征的组合提高了术前识别 PI 的预测性能,达到了高特异性和中等准确性。仅用 LPT < 3 mm 预测 PI 的灵敏度为 96.9%:本研究发现,LPT 可有效预测有 PT 的 ACC 的 PI。
Using the length of pleural tag to predetermine pleural invasion by lung adenocarcinomas.
Introduction: Pleural contact is present when the underlying pathology of the pleural tag (PT) involves the pleura. This study aimed to preoperatively predict PI by lung adenocarcinomas (ACCs) with PT, exploring CT imaging parameters indicative of PT consisting of pleura and tumor invasiveness.
Methods: This single-center, retrospective study included 84 consecutive patients diagnosed with solid ACCs with PT, who underwent resection at our hospital between May 2019 and July 2023. CT imaging parameters analyzed included: LPT (the length of PT), defined as the shortest distance from the tumor edge to the retracted pleura. Patients were divided into PI -ve group and PI +ve group according to PI status. Regression analyses were used to determine predictive factors for PI.
Results: The study evaluated 84 patients (mean age, 62.0 ± 13.8 years; 45 females) pathologically diagnosed with ACCs with PT on CT. Multivariate regression analysis identified tumor size (OR 1.18, 95% CI 1.09-1.29, p = 0.000), LPT (OR 0.48, 95% CI 0.25-0.91, p = 0.03) and multiple PTs to multiple types of pleura (OR 3.58, 95% CI 1.13-11.20, p = 0.03) as independent predictors for PI. The combination of these CT features improved the predictive performance for preoperatively identifying PI, achieving high specificity and moderate accuracy. The sensitivity of predicting PI with only LPT < 3 mm was 96.9%.
Conclusion: This study determined that LPT is effective for predetermining PI in ACCs with PT.
期刊介绍:
Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.