Retrospective Analysis Comparing Lung-RADS v2022 and British Thoracic Society Guidelines for Differentiating Lung Metastases from Primary Lung Cancer - Loredana Gabriela Stana; Alexandru Ovidiu Mederle; Claudiu Avram; Felix Bratosin; Paula Irina Barata (Biomedicines)
Background and Objectives: 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. Materials and Methods: 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. Results: 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%, p < 0.001) while maintaining similar sensitivity (91.7% vs. 93.8%, p = 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%, p < 0.001), lower lobe distribution (58.3% vs. 28.4%, p < 0.001), and smooth margins (70.8% vs. 20.3%, p < 0.001), whereas primary lung cancers were associated with solitary nodules, upper lobe location, and spiculated margins. Conclusions: 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.