Stana Loredana Gabriela, Mederle Alexandru Ovidiu, Avram Claudiu, Bratosin Felix, Barata Paula Irina
Department I, Discipline of Anatomy and Embriology, Victor Babes University of Medicine and Pharmacy, Eftimie Murgu Square 2, 300041 Timisoara, Romania.
Department of Surgery, Victor Babes University of Medicine and Pharmacy, 300041 Timisoara, Romania.
Biomedicines. 2025 Jan 8;13(1):130. doi: 10.3390/biomedicines13010130.
: The current study aimed to compare the effectiveness of the Lung Imaging Reporting and Data System (Lung-RADS) Version 2022 and the British Thoracic Society (BTS) guidelines in differentiating lung metastases from de novo primary lung cancer on CT scans in patients without prior cancer diagnosis. : This retrospective study included 196 patients who underwent chest CT scans between 2015 and 2022 without a known history of cancer but with detected pulmonary nodules. CT images characterized nodules based on size, number, location, margins, attenuation, and growth patterns. Nodules were classified according to Lung-RADS Version 2022 and BTS guidelines. Statistical analyses compared the sensitivity and specificity of Lung-RADS and BTS guidelines in distinguishing metastases from primary lung cancer. Subgroup analyses were conducted based on nodule characteristics. : Of the 196 patients, 148 (75.5%) had de novo primary lung cancer, and 48 (24.5%) had lung metastases from occult primary tumors. Lung-RADS Version 2022 demonstrated higher specificity than BTS guidelines (87.2% vs. 72.3%, < 0.001) while maintaining similar sensitivity (91.7% vs. 93.8%, = 0.68) in differentiating lung metastases from primary lung cancer. Lung metastases were more likely to present with multiple nodules (81.3% vs. 18.2%, < 0.001), lower lobe distribution (58.3% vs. 28.4%, < 0.001), and smooth margins (70.8% vs. 20.3%, < 0.001), whereas primary lung cancers were associated with solitary nodules, upper lobe location, and spiculated margins. : Lung-RADS Version 2022 provides higher specificity than the BTS guidelines in differentiating lung metastases from primary lung cancer on CT scans in patients without prior cancer diagnosis. Recognizing characteristic imaging features can improve diagnostic accuracy and guide appropriate management.
本研究旨在比较2022版肺影像报告和数据系统(Lung-RADS)与英国胸科学会(BTS)指南在未患过癌症的患者的CT扫描中区分肺转移瘤和原发性肺癌的有效性。
这项回顾性研究纳入了196例在2015年至2022年间接受胸部CT扫描的患者,这些患者无已知癌症病史但检测到肺部结节。CT图像根据大小、数量、位置、边缘、密度和生长模式对结节进行特征描述。结节根据2022版Lung-RADS和BTS指南进行分类。统计分析比较了Lung-RADS和BTS指南在区分转移瘤和原发性肺癌方面的敏感性和特异性。基于结节特征进行亚组分析。
在196例患者中,148例(75.5%)患有原发性肺癌,48例(24.5%)患有隐匿性原发性肿瘤的肺转移瘤。在区分肺转移瘤和原发性肺癌方面,2022版Lung-RADS的特异性高于BTS指南(87.2%对72.3%,P<0.001),而敏感性相似(91.7%对93.8%)。肺转移瘤更可能表现为多发结节(81.3%对18.2%,P<0.001)、下叶分布(58.3%对28.4%,P<0.001)和边缘光滑(70.8%对20.3%,P<0.001),而原发性肺癌与孤立结节、上叶位置和毛刺状边缘相关。
在未患过癌症的患者的CT扫描中,2022版Lung-RADS在区分肺转移瘤和原发性肺癌方面比BTS指南具有更高的特异性。识别特征性影像学表现可提高诊断准确性并指导适当的管理。