Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.

CT-guided transthoracic core-needle biopsy (CT-TTNB) is a minimally invasive procedure that plays a crucial role in diagnosing pulmonary nodules. With high diagnostic yield and low complication rates, CT-TTNB is favored over traditional surgical biopsies, providing accuracy in detecting both malignant and benign conditions. This literature review aims to present a comprehensive overview of CT-TTNB, focusing on its indications, procedural techniques, diagnostic yield, and safety considerations. Studies published between 2013 and 2024 were systematically reviewed from PubMed, Web of Science, Scopus, and Cochrane Library using the SANRA methodology. The results highlight that CT-TTNB has a diagnostic yield of 85–95% and sensitivity rates for detecting malignancies between 92 and 97%. Several factors, including nodule size, lesion depth, needle passes, and imaging techniques, influence diagnostic success. Complications such as pneumothorax and pulmonary hemorrhage were noted, with incidence rates varying from 12 to 45% for pneumothorax and 4 to 27% for hemorrhage. Preventative strategies and management algorithms are essential for minimizing and addressing these risks. In conclusion, CT-TTNB remains a reliable and effective method for diagnosing pulmonary nodules, particularly in peripheral lung lesions. Advancements such as PET/CT fusion imaging, AI-assisted biopsy planning, and robotic systems further enhance precision and safety. This review emphasizes the importance of careful patient selection and procedural planning to maximize outcomes while minimizing risks, ensuring that CT-TTNB continues to be an indispensable tool in pulmonary diagnostics.

This prospective observational study aimed to investigate the utility of lung ultrasound (LUS) in diagnosing and managing pediatric respiratory infections, specifically focusing on viral, bacterial, and SARS-CoV-2 infections. Conducted over a period of 1 year and 8 months, this research involved 85 pediatric patients (showcasing a median age of 14 months) recruited based on specific criteria, including age, confirmed infection through multiplex PCR tests, and willingness to undergo LUS imaging. This study employed a 12-area scoring system for LUS examinations, utilizing the lung ultrasound score (LUSS) to evaluate lung abnormalities. The PCR examination results reveal diverse respiratory pathogens, with SARS-CoV-2, influenza, and bacterial co-infections being prominent among the cases. As an observational study, this study was not registered in the registry. Distinct LUS patterns associated with different pathogens were identified, showcasing the discriminatory potential of LUS in differentiating between viral and bacterial etiologies. Bacterial infections demonstrated more severe lung involvement, evident in significantly higher LUSS values compared with viral cases (p < 0.0001). The specific abnormalities found in bacterial superinfection can be integrated into diagnostic and management protocols for pediatric respiratory infections. Overall, this research contributes valuable insights into optimizing LUS as a diagnostic tool in pediatric pneumonia, facilitating more informed and tailored healthcare decisions.

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: Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. Immune checkpoint inhibitors (ICIs) have transformed treatment paradigms, but assessing response remains challenging due to atypical patterns such as pseudoprogression, hyperprogression and dissociated response. Conventional evaluation criteria, such as RECIST 1.1, may not fully capture these patterns, leading to potential misclassification and premature therapy discontinuation. This review explores the role of 18F-FDG PET/CT in assessing immunotherapy response and highlights novel imaging criteria to enhance clinical decision-making. Methods: A systematic literature review was conducted across PubMed, Web of Science, Scopus, and Cochrane Library, selecting relevant studies published between 2013 and 2024. The review focuses on the strengths and limitations of PET-based imaging in monitoring NSCLC immunotherapy outcomes. Specific attention was given to evolving evaluation frameworks, including iRECIST, PERCIST, imPERCIST, and iPERCIST, as well as metabolic biomarkers such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Results: Compared with anatomical-based assessment, metabolic imaging using 18F-FDG PET/CT may offer deeper insights into tumor behavior during immunotherapy. PET-derived parameters seem to improve the detection of immune-related response patterns, providing a more refined approach to differentiate true progression from pseudoprogression. Emerging evidence indicates that metabolic biomarkers such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG) could serve as useful predictors of therapeutic efficacy and support treatment adaptation. Nevertheless, current findings are mainly based on small, heterogeneous, and predominantly retrospective studies, with variable PET timing and threshold definitions that limit the generalizability of these results. Conclusions: 18F-FDG PET/CT represents a promising complementary tool for assessing immunotherapy response in NSCLC. Its integration with advanced imaging criteria and metabolic tumor biomarkers may enhance response evaluation and assist clinical decision-making. Nonetheless, the current evidence remains preliminary, and further standardization and large prospective validation studies are required before its routine implementation in clinical practice.

Introduction: The 9th edition of the TNM classification for lung cancer implemented significant revisions, notably the subdivision of the N2 and M1c categories, to enhance anatomical precision and prognostic accuracy. Nonetheless, the actual effects of these modifications on stage distribution, histology-specific patterns, and clinical interpretation remain to be fully evaluated. Objectives: To compare lung cancer staging distributions between the 8th and 9th TNM editions, analyze patterns of stage migration, and evaluate histology-specific reclassification trends. Although TNM 9 applies the same descriptors across all histological subtypes, the magnitude of stage migration varies. In our cohort and in international datasets, adenocarcinoma demonstrated a higher likelihood of reclassification into advanced stages compared to other subtypes. Methods: A retrospective analysis was performed on a cohort of lung cancer patients staged according to the 8th and 9th editions of the TNM classification. Stage distribution alterations were analyzed by chi-squared tests, whereas McNemar’s test examined the directional shifts in upstaging and downstaging. Further investigations evaluated the correlation between histological subtype and stage reclassification. Results: A statistically significant redistribution of stages was noted (χ² = 1013.03, df = 64, p < 0.0001), with a notable prevalence of upstaging (p = 0.0019). The most significant proportional increase was observed in stage IIIA, mostly attributable to the N2 subdivision (N2a vs. N2b). Adenocarcinoma was the predominant histological subtype at all stages and showed a greater tendency for reclassification into advanced stages, specifically IIIA and IIIB. Squamous cell carcinoma was predominantly observed in stages IIB and IIIA, whereas small cell and large cell carcinomas were concentrated in advanced stages. These histology-specific patterns correspond with international findings, including research confirming the prognostic relevance of N2 subdivision. Conclusions: The 9th edition of the TNM classification results in significant stage migration, particularly in adenocarcinoma cases, indicating the improved sensitivity of the updated criteria in identifying advanced nodal disease. These modifications significantly impact prognostic evaluation and global comparability of clinical cohorts, supporting the implementation of TNM 9 as a more anatomically and biologically relevant staging system.