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.

Background: Pleural effusion (PE) is a common condition where accurate detection is essential for management. Thoracic ultrasound (TUS) is the first-line modality owing to safety, portability, and high sensitivity, but accuracy is operator-dependent. Artificial intelligence (AI)-based automated analysis has been explored as an adjunct, with early evidence suggesting potential to reduce variability and standardise interpretation. This review evaluates the diagnostic accuracy of AI-assisted TUS for PE detection. Methods: This review was registered with PROSPERO (CRD420251128416) and followed PRISMA guidelines. MEDLINE, Scopus, Google Scholar, IEEE Xplore, Cochrane CENTRAL, and ClinicalTrials.gov were searched through 20 August 2025 for studies assessing AI-based TUS analysis for PE. Eligible studies required recognised reference standards (expert interpretation or chest CT). Risk of bias was assessed with QUADAS-2, and certainty with GRADE. Owing to heterogeneity, structured narrative synthesis was performed instead of meta-analysis. Results: Five studies (7565 patients) published between 2021–2025 were included. All used convolutional neural networks with varied architectures (ResNet, EfficientNet, U-net). Sensitivity ranged 70.6–100%, specificity 67–100%, and AUC 0.77–0.99. Performance was reduced for small, trace, or complex effusions and in critically ill patients. External validation showed attenuation compared with internal testing. All studies had high risk of bias in patient selection and index test conduct, reflecting retrospective designs and inadequate dataset separation. Conclusions: AI-assisted TUS shows promising diagnostic performance for PE detection in curated datasets; however, evidence is inconsistent and limited by key methodological weaknesses. Overall certainty is low-to-moderate, constrained by retrospective designs, limited dataset separation, and scarce external validation. Current evidence is insufficient to support routine clinical use. Robust prospective multicentre studies with rigorous independent validation and evaluation of clinically meaningful outcomes are essential before clinical implementation can be considered.

Hemorrhagic complications during pleural interventions—such as thoracentesis and chest tube insertion—remain a significant clinical concern, primarily due to inadvertent injury of the intercostal artery (ICA). The highly variable ICA anatomy is frequently not visualized on conventional imaging, limiting the reliability of landmark-based techniques. Color Doppler thoracic ultrasound (CDUS) has emerged as a non-invasive, real-time modality capable of identifying ICAs and their anatomical variants prior to pleural access. This narrative review synthesizes current evidence on CDUS-guided ICA screening, focusing on its technical principles, diagnostic performance, and clinical applicability. While feasibility and utility are supported by multiple observational studies, robust evidence demonstrating a reduction in bleeding complications is still lacking. Barriers to widespread implementation include heterogeneous scanning protocols, operator dependency, and the absence of standardized training. We discuss the anatomical rationale for pre-procedural vascular mapping and highlight emerging protocols aimed at standardizing ICA visualization. Although not yet incorporated into major clinical guidelines, CDUS represents a promising tool to enhance procedural safety. Emerging AI applications may further improve vessel detection by reducing operator dependency and enhancing reproducibility. High-quality prospective studies are essential to validate potential clinical benefits, optimize implementation strategies, and support integration into routine pleural practice.

BACKGROUND: Participants in LCS may exhibit different characteristics according to selection criteria. AIM: To preliminary compare demographic, clinical and smoking characteristics of participants in two LCS conducted in Pisa, Italy, in 2004 (ITL1) and in 2022 (ITL2), and to assess their relationship with positive baseline LDCT outcome. METHODS: 351 subjects from ITL1 (randomized trial, enrolled from GPs records, 55-69 yrs, ≥20 pky) were compared with 287 from ITL2 (non-randomized pilot study, volunteers informed by physicians or social media, age range 55-75 yrs, ≥30 pky). Two logistic regressions were applied, with being ITL2 or having positive LDCT (detected ≥1 nodules) as dependent variables, and age, sex, current smoking, pky, lung cancer family history as covariates. RESULTS: As compared to ITL1, ITL2 subjects showed significantly (p<0.001) higher mean age and prevalence of current smoking, current occupation, higher education, cardiovascular, respiratory or other comorbidities, lung cancer family history. Pky and positive LDCT did not differed between ITL1-ITL2. In logistic regressions: age ≥59 yrs (p=0.000), female sex (p=0.001), current smoking (p=0.000), and lung cancer family history (p=0.016) were independently associated to being participants in ITL2; no significant predictors were associated with positive baseline LDCT. CONCLUSIONS: While two different groups of participants in LCS differ for baseline characteristics, there are no significant predictors associated with a positive LDCT. Further analyses are needed to define those characteristics which may support criteria to select subjects eligible for LCS.