Core Viewpoint - The article emphasizes the importance of prevention, early detection, and scientific treatment of lung cancer, highlighting the need for public awareness and proactive measures against the disease [1]. Group 1: Prevention - Smoking is identified as the primary risk factor for lung cancer, with a direct correlation between smoking quantity and the age of initiation to the likelihood of developing the disease. Early cessation and avoidance of secondhand smoke are crucial [2]. - Long-term exposure to dust and harmful chemicals, as well as poor air quality, should be avoided to reduce lung cancer risk [2]. Group 2: Screening - Four high-risk groups are identified for regular low-dose spiral CT screening: long-term smokers, individuals with a family history of malignancies (especially lung cancer), those with chronic lung diseases or occupational exposure, and elderly individuals around 70 years old. Early detection through screening can lead to a clinical cure rate of over 90% for early-stage lung cancer [3]. Group 3: Management of Nodules - The article provides guidance on managing lung nodules, emphasizing that most nodules are benign. For low-risk individuals, follow-up for nodules smaller than 5mm can be done every 2-3 years, while high-risk individuals may require more frequent monitoring [4][5]. Group 4: Treatment Strategies - Personalized treatment plans based on cancer staging can significantly enhance treatment efficacy. Early-stage lung cancer has a cure rate of 80%-90% post-surgery, while mid-stage patients can see survival rates increase from 30%-40% to over 65% with appropriate therapies [6]. - For late-stage patients, targeted therapies and immunotherapy offer new hope for long-term survival, with advancements in domestic drug efficacy and reduced economic burden for patients [7].

Core Insights - Lung cancer is one of the most aggressive and prevalent cancers globally, with an estimated 2.2 million new cases and 1.8 million deaths in 2020, leading to a five-year survival rate of less than 10% due to late-stage diagnosis [2] Group 1: Research Findings - A multi-center benchmarking study evaluated six large language models (LLMs) for clinical decision support in lung cancer screening, revealing that Claude 3 Opus had the highest readability, while GPT-4 achieved the highest clinical accuracy [3][7] - The study involved a cross-sectional analysis of 148 anonymized low-dose computed tomography (LDCT) reports from three medical institutions, assessing the performance of LLMs in providing management recommendations for incidental lung nodules [6][8] - The results indicated that the performance differences among LLMs were not significant across different hospital reports, highlighting their robustness and practicality in various medical environments [7][10] Group 2: Implications for Clinical Practice - The findings suggest that LLMs could enhance clinical decision support in lung cancer screening, particularly in managing incidental findings from LDCT scans, which is a pressing challenge in cancer screening management [6][10] - The study underscores the potential of LLMs to assist outpatient physicians in making timely decisions regarding follow-up interventions or surveillance strategies for lung nodules [5][6]