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.

Background/Objectives: Indoor air pollution is an increasingly recognized cause of lung cancer, yet evidence remains fragmented across exposure categories. This systematic review aimed to consolidate epidemiological findings on the relationship between household pollutants and lung cancer risk across diverse settings. Methods: A systematic search of PubMed, Web of Science, Scopus, and Cochrane was conducted to identify observational studies published between 2015 and 2025. Eligible articles evaluated indoor exposure in relation to primary lung cancer. Maximally adjusted effect estimates were extracted. Random effects models were used to calculate pooled odds ratios (ORs) and hazard ratios (HRs) when appropriate. Study quality was assessed using the Newcastle–Ottawa Scale. Results: Thirty-eight studies comprising 475,565 participants were included. Environmental tobacco smoke (ETS) was associated with lung cancer risk (pooled OR 1.97, 95% CI 1.63–2.37; pooled HR 1.44, 95% CI 1.19–1.74). Cooking oil fumes showed a pooled OR of 1.83 (95% CI 1.53–2.21). Solid fuel and biomass combustion were also associated with increased lung cancer risk, with pooled estimates indicating elevated odds and hazard ratios (pooled OR 2.26, 95% CI 1.36–3.77; pooled HR 1.66, 95% CI 1.37–2.02). Incense burning was evaluated in a single study (OR 3.05, 95% CI 1.06–8.84), with wide confidence intervals. Two studies explored gene–environment interactions, suggesting possible variability in susceptibility, although statistical robustness was limited. Conclusions: Across multiple exposure categories, indoor air pollution was consistently associated with lung cancer risk, although the effect magnitude and precision varied between studies. Given the observational nature of the evidence and methodological heterogeneity, further prospective research with standardized exposure assessment is needed to clarify the strength and consistency of these associations.

Background: Asthma, a chronic respiratory disease characterized by airway inflammation and hyperresponsiveness, exhibits significant heterogeneity in its presentation. This study aimed to investigate age-related comorbidity patterns, seasonal variations, and demographic trends among a cohort of asthma patients within a defined geographical region. Methods: A retrospective analysis of 13,695 asthma patients admitted to a Romanian hospital from 2013 to 2023 was conducted. Comorbidity patterns were analyzed using network analysis across age groups, and seasonal trends were investigated through spectral analysis. Results: Asthma admissions exhibited non-linear trends with female predominance (57.72%). The pandemic significantly impacted admission rates, with males experiencing greater COVID-19-related effects. Female admissions showed distinct seasonal patterns potentially linked to domestic responsibilities. Comorbidity patterns evolved with age, shifting from lifestyle factors in younger patients to complex cardiovascular and neurological disorders in older groups. The 60–69 age group showed the highest integration of comorbidity communities. Conclusions: The study revealed that asthma management focus should shift with patient age from the disease itself to addressing underlying comorbidities. Understanding these complex patterns may help personalize treatment strategies and improve long-term prognosis for asthma patients.