Chin Med J Pulm Crit Care Med. 2026 Jun 6;4(2):99-116. doi: 10.1016/j.pccm.2026.05.005. eCollection 2026 Jun.

ABSTRACT

Lung diseases-including lung cancer, chronic obstructive pulmonary disease (COPD), asthma, interstitial lung diseases (ILDs), and rare conditions like cystic fibrosis-remain major drivers of global morbidity and mortality. Timely diagnosis and individualized treatment are frequently challenged by heterogeneous clinical phenotypes and the complexity of multimodal data. This review provides a critical synthesis of the transformative role of artificial intelligence (AI) in respiratory care, tracing the paradigm shift from classical machine learning to emerging large language models (LLMs) and multimodal foundation models. We evaluate the performance of AI across the patient care continuum: beginning with radiologist-level nodule detection and automated diagnostics, advancing into AI-powered clinical decision support systems (CDSS) and surgical/radiotherapeutic interventions, and culminating in prognostic modeling and "digital twin" simulations for longitudinal patient management. Furthermore, we explore the translational frontier of precision medicine, examining how AI leverages multi-omics and liquid biopsies to drive novel biomarker discovery and accelerate drug repurposing. Finally, we address persistent sociotechnical barriers-including data sovereignty, legal liability, and the critical need for prospective clinical validation-proposing a translational roadmap for the safe integration of generalist medical AI into clinical workflows.

PMID:42396189 | PMC:PMC13323542 | DOI:10.1016/j.pccm.2026.05.005

Ann Med. 2026 Dec;58(1):2654291. doi: 10.1080/07853890.2026.2654291. Epub 2026 Apr 17.

ABSTRACT

BACKGROUND: To develop and validate an integrated radiopathomics nomogram combining multiphase CT images, H&E-stained slides, and clinicopathological variables for predicting microvascular patterns (MVPs) in non-small cell lung cancer (NSCLC).

METHODS: We retrospectively included consecutive surgically resected NSCLC patients from two centers (n = 258). Patients from center 1 were randomly divided into training and internal validation cohorts, while patients from center 2 formed external validation cohort. CD34-immunohistochemistry was used as the reference standard for MVPs to classify patients into non-angiogenic alveolar (NAA) and non-NAA groups. Radiomics and pathomics features were extracted to construct single-phase radiomics, combined radiomics, and pathomics models. Rad-score and Path-score were derived from combined radiomics and pathomics models, respectively. Rad-score, Path-score, and clinicopathological independent predictors were integrated to develop a nomogram. Model performance was assessed by area under the curve (AUC), calibration curve, decision curve analysis (DCA), and DeLong test.

RESULTS: On multivariable analysis, histological grade was an independent predictor of NAA MVP. Combined radiomics model for predicting MVPs achieved AUCs of 0.863, 0.856, and 0.849 in training, internal validation, and external validation cohorts, showing better performance than single-phase models. Pathomics model yielded AUCs of 0.878, 0.860, and 0.833, however, its specificity markedly decreased in validation cohorts. Nomogram model achieved the superior performance across all cohorts, with AUCs of 0.911, 0.903, and 0.901, outperforming single-modality models (DeLong test: all p < 0.05).

CONCLUSION: The nomogram demonstrated high accuracy and robustness in predicting MVPs in NSCLC, offering a promising tool for characterizing the tumor microenvironment and supporting individualized treatment.

PMID:41992828 | DOI:10.1080/07853890.2026.2654291

Redox Biol. 2026 Jan 14;92:104026. doi: 10.1016/j.redox.2026.104026. Online ahead of print.

ABSTRACT

Primary graft dysfunction (PGD) caused by ischemia-reperfusion injury (IRI) is a major complication after lung transplantation, yet its underlying mechanisms remain unclear. Triggering receptor expressed on myeloid cells 1 (Trem1) is an important mediator of inflammation, but its role in neutrophil function and metabolic reprogramming during lung IRI is not well understood. In this study, we used a murine orthotopic lung transplantation model with cold ischemia and reperfusion, and Trem1 knockout (Trem1-/-) and myeloid-specific Trem1 conditional knockout mice (Lysm^CreTrem1^fl) to explore the role of Trem1 in neutrophil recruitment, neutrophil extracellular trap (NET) formation, and metabolism. Our results show that Trem1 expression increases in both mouse and human lungs after reperfusion and correlates with neutrophil infiltration and lung injury. Trem1 deficiency significantly reduced neutrophil and macrophage recruitment, NET formation, and tissue damage. Multi-omics analysis revealed that Trem1 deletion suppressed oxidative phosphorylation (OXPHOS) and induced a metabolic shift in neutrophils toward glycolysis. In clinical samples, the abundance of TREM1+ neutrophils was correlated with PGD severity and OXPHOS activity. These findings identify Trem1 as a key regulator of neutrophil metabolism and recruitment in lung IRI, and suggest that targeting Trem1 may provide a novel therapeutic strategy to mitigate PGD and improve lung transplant outcomes.

PMID:41861599 | DOI:10.1016/j.redox.2026.104026