Lung cancer is the leading cause of cancer-related death worldwide, with 1.8 million deaths annually. Early detection is vital for improving patient outcomes; however, survival rates remain low due to late-stage diagnoses. Accumulating data supports the idea that screening methods are useful for improving early diagnosis in high-risk patients. However, several barriers limit the application of lung cancer screening in real-world settings. The widespread diffusion of artificial intelligence (AI), radiomics, and machine learning has dramatically changed the current diagnostic landscape. This review explores the potential of AI and biomarker-driven methods, particularly liquid biopsy, in enhancing early lung cancer detection. We report the findings of major randomized controlled trials, cohort studies, and research on AI algorithms that use multi-modal imaging (e.g., CT and PET scans) and liquid biopsy to identify early molecular alterations. AI algorithms enhance diagnostic accuracy by automating image analysis and reducing inter-reader variability. Biomarker-driven methods identify molecular alterations in patients before imaging signs of cancer are evident. Both AI and liquid biopsy show the potential to improve sensitivity and specificity, enabling the detection of early-stage cancers that traditional methods, like low-dose CT (LDCT) scans, might miss. Integrating AI and biomarker-driven methods offers significant promise for transforming lung cancer screening. These technologies could enable earlier, more accurate detection, ultimately improving survival outcomes. AI-driven lung cancer screening can achieve over 90% sensitivity, compared to 70–80% with traditional methods, and can reduce false positives by up to 30%. AI also boosts specificity to 85–90%, with faster processing times (a few minutes vs. 30–60 min for radiologists). However, challenges remain in standardizing these approaches and integrating them into clinical practice. Ongoing research is essential to fully realize their clinical benefits and enhance timely interventions.

The proper management of suspicious radiologic findings is crucial to optimize the effectiveness of low-dose computed tomography (LDCT) lung cancer screening trials. In the BioMILD study, we evaluated the utility of combining a plasma 24-microRNA signature classifier (MSC) and LDCT to define the individual risk and personalize screening strategies. Here we aim to assess the utility of repeated MSC testing during annual screening rounds in 1024 participants with suspicious LDCT findings. The primary outcome was two-year lung cancer incidence in relation to MSC test results, reported as relative risk (RR) with 95% confidence interval (CI). Lung cancer incidence and mortality were estimated using extended Cox models for time-dependent covariates, yielding the respective hazard ratios (HR). Clinicaltrials.gov ID: NCT02247453. With a median follow-up of 8.5 years, the full study set included 1403 indeterminate LDCT (CTind) and 584 positive LDCT (CT+) results. A lung cancer RR increase in MSC+ compared to MSC- participants was observed in both the CTind (RR: 2.5; 95% CI: 1.4-4.32) and CT+ (RR: 2.6; 95% CI: 1.81-3.74) groups and was maintained when considering stage I or resectable tumors only. A 98% negative predictive value in CTind/MSC- and a 30% positive predictive value in CT+/MSC+ lesions were recorded. At seven years' follow-up, MSC+ participants had a cumulative HR of 4.4 (95% CI: 3.0-6.4) for lung cancer incidence and of 8.1 (95% CI: 2.7-24.5) for lung cancer mortality. Our study shows that MSC can be reliably performed during LDCT screening rounds to increase the accuracy of lung cancer risk and mortality prediction and supports its clinical utility in the management of LDCT findings of uncertain malignancy. Italian Association for Cancer Research; Italian Ministry of Health; Horizon2020; National Cancer Institute (NCI); Gensignia LifeScience.

The extra-pleural pneumonectomy (EPP) is a standardised surgical procedure born for pleural tuberculosis and later used in pleural cancer treatment, especially in malignant pleural mesothelioma (MPM). This systematic review aimed to focus on the actual overall EPP role in surgical oncology. The literature search was performed from January 1985 to January 2018 In PubMed, Embase, and Cochrane according to PRISMA protocol. The search was restricted to publications in English with the research words "extrapleural pneumonectomy", "malignant pleural mesothelioma", "pleural malignancies". The results were then filtered focusing only on papers with series of patients treated with EPP, for mesothelioma and non-mesothelioma malignancies. The search was restricted to publications in English. We found a 5-year overall survival (OS) ranging from 0 to 78%. The peri-operative mortality and morbidity ranged from 0 to 11.8% and 0 to 82.6%, respectively. The most represented and described post-operative complications reported were ARDS, pericardial tamponade, cardiac herniation, pulmonary embolism, respiratory infections, respiratory failure, atrial arrhythmia, myocardial infarction. In referral centres and selected patients, EPP is a cytoreductive or radical surgical treatment in extended pleural malignancies. Prospective studies are needed to standardise the timing of the procedure in a multimodality treatment program, according to the oncological and functional indications, to keep an acceptable complications rate and post-operative quality of life status.

Background: Combined large cell neuroendocrine carcinoma (CoLCNEC) is given by the association of LCNEC with adeno or squamous or any non-neuroendocrine carcinoma. Molecular bases of CoLCNEC pathogenesis are scant and no standardized therapies are defined. Methods: 44 CoLCNECs: 26 with adenocarcinoma (CoADC), 7 with squamous cell carcinoma (CoSQC), 3 with small cell carcinoma (CoSCLC), 4 with atypical carcinoid (CoAC) and 4 napsin-A positive LCNEC (NapA+), were assessed for alterations in 409 genes and transcriptomic profiling of 20,815 genes. Results: Genes altered included TP53 (n = 30), RB1 (n = 14) and KRAS (n = 13). Targetable alterations included six KRAS G12C mutations and ALK-EML4 fusion gene. Comparison of CoLCNEC transcriptomes with 86 lung cancers of pure histology (8 AC, 19 ADC, 19 LCNEC, 11 SCLC and 29 SQC) identified CoLCNEC as a separate entity of neuroendocrine tumours with three different molecular profiles, two of which showed a non-neuroendocrine lineage. Hypomethylation, activation of MAPK signalling and association to immunotherapy signature specifically characterized each of three CoLCNEC molecular clusters. Prognostic stratification was also provided. Conclusions: CoLCNECs are an independent histologic category. Our findings support the extension of routine evaluation of KRAS mutations, fusion genes and immune-related markers to offer new perspectives in the therapeutic management of CoLCNEC.

To retrospectively assess the agreement between human and automated AI-based readings for low-dose computed tomography (LDCT) outcomes according to LungRADS v1.1 in lung cancer screening (LCS); to test the diagnostic performance of both readings. We included 4104 baseline LDCTs from the BioMILD trial. Original readings were retrospectively classified into "negative" (LungRADSv1.1 categories 1, 2) and "positive" (categories 3, 4) by a radiologist and analyzed by AI software for category assignment. Diagnosis of lung cancer (LC) at 2 years served as reference standard to assess sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of both human and AI. Agreement between readers was measured by the k-Cohen Index with Fleiss-Cohen weights (K_w) with 95 % CI. Median age of participants was 60 years; 60.8 % were male and 79.2 % current smokers; 68/4104 (1.7 %) were diagnosed with LC; 6/68 (8.8 %) and 7/68 (10.3 %) LDCT were classified as negative by AI and human reading, respectively. The agreement between human and AI readings for negative and positive LDCTs was 83.5 % (K_w 0.47; 95 %CI: 0.43-0.50). Sensitivity and specificity were 91.2 % and 75.7 % for AI, and 89.7 % and 90.0 % for human reading (p-value 0.5637 and < 0.0001). PPV and NPV were 6.0 % and 99.8 % for AI, and 13.1 % and 99.8 % for human reading (p-value < 0.0001 and 0.9351). The expected reduction in LDCT reading workload when using AI as first reader was 74.7 %. AI reading showed comparable sensitivity but lower specificity than human reading. High NPV of AI may support its use as a first reader in LCS.