Background/Objectives:Lung cancer remains a major cause of cancer-related mortality, with regional differences in incidence and patient characteristics. This study aimed to verify and quantify a perceived dramatic increase in lung cancer cases at a Romanian center, identify distinct patient phenotypes using unsupervised machine learning, and characterize contributing factors, including demographic shifts, changes in the healthcare system, and geographic patterns.Methods:A comprehensive retrospective analysis of 4206 lung cancer patients admitted between 2013 and 2024 was conducted, with detailed molecular characterization of 398 patients from 2023 to 2024. Temporal trends were analyzed using statistical methods, while k-means clustering on 761 clinical features identified patient phenotypes. The geographic distribution, smoking patterns, respiratory comorbidities, and demographic factors were systematically characterized across the identified clusters.Results:We confirmed an 80.5% increase in lung cancer admissions between pre-pandemic (2013–2020) and post-pandemic (2022–2024) periods, exceeding the 51.1% increase in total hospital admissions and aligning with national Romanian trends. Five distinct patient clusters emerged: elderly never-smokers (28.9%) with the highest metastatic rates (44.3%), heavy-smoking males (27.4%), active smokers with comprehensive molecular testing (31.7%), young mixed-gender cohort (7.3%) with balanced demographics, and extreme heavy smokers (4.8%) concentrated in rural areas (52.6%) with severe comorbidity burden. Clusters demonstrated significant differences in age (p< 0.001), smoking intensity (p< 0.001), geographic distribution (p< 0.001), as well as molecular characteristics. COPD prevalence was exceptionally high (44.8–78.9%) across clusters, while COVID-19 history remained low (3.4–8.3%), suggesting a limited direct association between the pandemic and cancer.Conclusions:This study presents the first comprehensive machine learning-based stratification of lung cancer patients in Romania, confirming genuine epidemiological increases beyond healthcare system artifacts. The identification of five clinically meaningful phenotypes—particularly rural extreme smokers and age-stratified never-smokers—demonstrates the value of unsupervised clustering for regional healthcare planning. These findings establish frameworks for targeted screening programs, personalized treatment approaches, and resource allocation strategies tailored to specific high-risk populations while highlighting the potential of artificial intelligence in identifying actionable clinical patterns for the implementation of precision medicine.
**背景/目的：** 肺癌仍是癌症相关死亡的主要原因，其发病率和患者特征存在地区差异。本研究旨在验证并量化罗马尼亚某中心肺癌病例的显著增长趋势，通过无监督机器学习识别不同的患者表型，并分析相关影响因素，包括人口结构变化、医疗体系变革及地理分布模式。 **方法：** 对2013年至2024年间收治的4206例肺癌患者进行回顾性分析，并对2023年至2024年间的398例患者进行详细的分子特征分析。采用统计学方法评估时间趋势，同时基于761项临床特征进行k均值聚类以识别患者表型。系统分析了不同聚类群体的地理分布、吸烟模式、呼吸系统合并症及人口学特征。 **结果：** 研究证实，与疫情前（2013–2020年）相比，疫情后（2022–2024年）肺癌住院病例增加了80.5%，远超全院总住院量51.1%的增幅，且与罗马尼亚全国趋势一致。研究识别出五个显著不同的患者聚类：老年非吸烟者（28.9%）转移率最高（44.3%）、重度吸烟男性（27.4%）、接受全面分子检测的现吸烟者（31.7%）、人口特征均衡的年轻混合性别群体（7.3%），以及集中于农村地区（52.6%）、合并症负担极重的极端重度吸烟者（4.8%）。各聚类在年龄（p<0.001）、吸烟强度（p<0.001）、地理分布（p<0.001）及分子特征方面均存在显著差异。所有聚类的慢性阻塞性肺疾病患病率均异常高（44.8–78.9%），而COVID-19病史比例较低（3.4–8.3%），提示疫情与癌症的直接关联有限。 **结论：** 本研究首次基于机器学习对罗马尼亚肺癌患者进行全面分层，证实了肺癌发病率的真实流行病学增长而非医疗系统因素所致。识别出的五个具有临床意义的表型——尤其是农村极端吸烟者和按年龄分层的非吸烟者——凸显了无监督聚类在区域医疗规划中的价值。这些发现为针对特定高危人群制定靶向筛查计划、个性化治疗方案和资源分配策略提供了框架，同时彰显了人工智能在识别可操作性临床模式、推动精准医疗实施方面的潜力。
肿瘤(癌症)患者之家