Background: Long COVID has become a burden on healthcare systems worldwide. Research into the etiology and risk factors has been impeded by observing all diverse manifestations as part of a single entity. We aimed to determine patterns of symptoms in convalescing COVID-19 patients. Methods: Symptomatic patients were recruited from four countries. Data were collected regarding demographics, comorbidities, acute disease and persistent symptoms. Factor analysis was performed to elucidate symptom patterns. Associations of the patterns with patients’ characteristics, features of acute disease and effect on daily life were sought. Results: We included 1027 symptomatic post-COVID individuals in the analysis. The majority of participants were graded as having a non-severe acute COVID-19 (N = 763, 74.3%). We identified six patterns of symptoms: cognitive, pain-syndrome, pulmonary, cardiac, anosmia-dysgeusia and headache. The cognitive pattern was the major symptoms pattern, explaining 26.2% of the variance; the other patterns each explained 6.5–9.5% of the variance. The cognitive pattern was higher in patients who were outpatients during the acute disease. The pain-syndrome pattern was associated with acute disease severity, higher in women and increased with age. The pulmonary pattern was associated with prior lung disease and severe acute disease. Only two of the patterns (cognitive and cardiac) were associated with failure to return to pre-COVID occupational and physical activity status. Conclusion: Long COVID diverse symptoms can be grouped into six unique patterns. Using these patterns in future research may improve our understanding of pathophysiology and risk factors of persistent COVID, provide homogenous terminology for clinical research, and direct therapeutic interventions.

Non-expandable lung (NEL) occurs when the lung fails to fully re-expand after pleural fluid drainage, complicating management and limiting therapeutic options. Diagnosis, based on clinical symptoms, pleural manometry, and traditional imaging, is often delayed to the peri- or post-procedural stages, leading to improper management, complications, and higher healthcare costs. Therefore, early, pre-procedural diagnostic methods are needed. Thoracic ultrasound (TUS) has emerged as a non-invasive tool with the potential to enhance diagnostic accuracy and guide clinical decisions, yet, it remains inadequately studied within the context of NEL. We conducted a non-systematic narrative review using a structured methodology, including a comprehensive database search, predefined inclusion criteria, and QUADAS-2 quality assessment. This approach ensured a rigorous synthesis of evidence on TUS in NEL, with the aim of identifying knowledge gaps and guiding future studies. Non-invasive, real-time, bedside M-mode TUS has demonstrated efficacy in predicting NEL prior to thoracentesis by detecting an absent sinusoidal sign and reduced atelectatic lung movement. Emerging experimental techniques, including 2D shear wave elastography (SWE), speckle tracking imaging (STI) strain analysis, the lung/liver echogenicity (LLE) ratio, TUS assessment of dynamic air bronchograms, and pleural thickening evaluation, show additional potential to enhance pre-procedural NEL detection. However, all these methods have significant limitations that require further comprehensive investigation. Despite their significant promise, TUS modalities for early NEL detection still require rigorous validation and standardization before broad clinical use. A multimodal diagnostic approach, combining clinical manifestations, pleural manometry, radiologic and ultrasonographic findings, along with emerging techniques (once fully validated), may provide the most extensive framework for NEL. Regardless of advancements, patient-centered care and shared decision-making remain essential. Further research is needed to improve outcomes, reduce healthcare costs, and enhance long-term treatment strategies.

Background: Drug-induced interstitial lung disease (DI-ILD) encompasses a heterogeneous spectrum of potentially severe pulmonary toxicities associated with various pharmacological agents. Diagnosis is often delayed due to nonspecific symptoms and imaging findings that can mimic other ILDs or infections, particularly in patients receiving oncological or immunomodulatory therapies. Novel approaches are needed to improve early recognition and management. Methods: We conducted a non-systematic, narrative literature review across major biomedical databases up to June 2025, aimed at describing current and emerging applications of artificial intelligence (AI) and radiomics in the early diagnosis, risk stratification, and personalised management of DI-ILD. Results: Radiomics applied to computed tomography and positron emission tomography/computed tomography enables extraction of high-dimensional quantitative features capturing subclinical alterations undetectable by visual assessment. In DI-ILD (particularly immune checkpoint inhibitor-related pneumonitis), radiomic models show potential diagnostic utility in distinguishing overlapping imaging patterns and predicting fibrotic progression. Integration with clinical, radiological and pharmacogenomic data has improved model performance in several studies. Additionally, AI approaches, including convolutional neural networks and ensemble learning methods, demonstrate promise in enhancing pattern recognition and risk stratification. Conclusions: Radiomics and AI are emerging complementary tools in multidisciplinary management of DI-ILD, offering objective imaging biomarkers and facilitating multimodal data integration to improve diagnostic precision and personalised therapeutic decisions. Nonetheless, reproducibility remains limited by variability in imaging protocols and lack of large-scale prospective multicentre validation. Clinical implementation requires standardised protocols to ensure consistency and reliability. The development of transparent, interpretable models seamlessly integrated into healthcare workflows is essential to fully leverage their real-world potential in proactive patient care.

The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55–69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.