With the increasing utilization of cancer therapy, the incidence of lung injury associated with these treatments continues to rise. The recognition of pulmonary toxicity related to cancer therapy has become increasingly critical, for which interstitial lung disease (ILD) is a common cause of mortality. Cancer therapy-related ILD (CT-ILD) can result from a variety of treatments including chemotherapy, targeted therapy, immune checkpoint inhibitors, antibody-drug conjugates, and radiotherapy. CT-ILD may progress rapidly and even be life-threatening; therefore, prompt diagnosis and timely treatment are crucial for effective management. This review aims to provide valuable information on the risk factors associated with CT-ILD; elucidate its underlying mechanisms; discuss its clinical features, imaging, and histological manifestations; and emphasize the clinical-related views of its diagnosis. In addition, this review provides an overview of grading, typing, and staging treatment strategies used for the management of CT-ILD.
Humans ; Lung Diseases, Interstitial/diagnosis* ; Neoplasms/therapy* ; Risk Factors ; Immune Checkpoint Inhibitors/adverse effects* ; Antineoplastic Agents/therapeutic use*

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
Humans ; Lung Neoplasms/diagnosis* ; Artificial Intelligence ; Early Detection of Cancer/methods* ; Precision Medicine ; Image Processing, Computer-Assisted/methods* ; Tomography, X-Ray Computed ; Radiomics

Small cell lung cancer (SCLC) is a highly malignant disease that has garnered significant attention in terms of treatment modalities and course management. Gaining an understanding of the clinical characteristics of SCLC, acquiring proficiency in screening, diagnosis, and treatment methods for this condition, as well as promptly addressing any adverse reactions to treatment are essential foundations for developing a scientific and rational pathological management plan for SCLC. By utilizing an intelligent whole course follow-up management platform, dynamic follow-up, timely warnings, and early interventions can enable high-quality whole life cycle management. This article aims to review the current treatment landscape of SCLC while exploring the challenges associated with implementing a comprehensive process-oriented management approach. The goal is to provide valuable insights for better managing SCLC patients and ultimately improving their quality of life and prognosis.
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Humans ; Small Cell Lung Carcinoma/diagnosis* ; Lung Neoplasms/diagnosis* ; Quality of Life ; Follow-Up Studies

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas, some patients cannot undergo traditional genetic testing. The aim of this study is to establish a machine learning (ML) model using non-invasive peripheral blood markers to explore the biomarkers closely related to EGFR mutation status in NSCLC and evaluate their potential prognostic value. METHODS: 2642 lung cancer patients who visited Jiangsu Cancer Hospital from November 2016 to May 2023 were retrospectively enrolled and finally 175 NSCLC patients with complete follow-up data were included in the study. The ML model was constructed based on peripheral blood indicators and divided into training set and test set according to the ratio of 8:2. Unsupervised learning algorithms were used for clustering blood features and mutual information method for feature selection, and an ensemble learning algorithm based on Shapley value was designed to calculate the contribution of each feature to the model prediction result. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model. RESULTS: Through the feature extraction and contribution analysis of the predictive results of the interpretable ML model based on the Shapley value, the top ten indicators with the highest contribution were: pathological type, phosphorus, eosinophils, monocyte count, activated partial thromboplastin time, potassium, total bilirubin, sodium, eosinophil percentage, and total cholesterol. The area under the curve (AUC) of the model was 0.80. In addition, patients with hyponatremia and squamous cell carcinoma group had a poor prognosis (P<0.05). CONCLUSIONS The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Machine Learning ; Lung Neoplasms/diagnosis* ; Male ; Female ; Mutation ; Middle Aged ; ErbB Receptors/genetics* ; Prognosis ; Aged ; Retrospective Studies ; Adult ; Biomarkers, Tumor/genetics*

Accurate staging is the fundamental basis for the treatment and prognosis of non-small cell lung cancer (NSCLC), and whether the tumor involves the pleura or chest wall is a critical aspect in assessing the staging of peripheral lung cancer. Imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) and positron emission tomography (PET) are widely used to determine pleural invasion in NSCLC. There has been an increasing number of studies evaluating whether NSCLC invades the pleura and the extent of such invasion. This article provides a review of the staging and the imaging diagnostic criteria of pleural invasion, aiming to offer references for peers in the precise diagnosis of pleural or chest wall invasion.
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Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Lung Neoplasms/diagnosis* ; Thoracic Wall/diagnostic imaging* ; Pleura/diagnostic imaging* ; Neoplasm Invasiveness ; Tomography, X-Ray Computed

BACKGROUND: Lung adenocarcinoma (LUAD) is the predominant subtype of non-small cell lung cancer (NSCLC). Damage-specific DNA binding protein 1 (DDB1), as a core protein of the CUL4-DDB1 ubiquitin ligase complex, is involved in the regulation of DNA damage repair, epigenetic modification, and cell cycle checkpoint activation. While the involvement of DDB1 in tumour progression through DNA repair and RNA transcriptional regulation has been reported, its expression and role in LUAD remain to be elucidated. This study aims to investigate the expression and role of DDB1 in LUAD. METHODS: The expression, clinicopathological features and prognosis of DDB1 in LUAD were analysed using databases such as UALCAN, Kaplan-Meier Plotter and GEPIA; The interaction network and enriched functional pathways were constructed by GeneMANIA and Metascape; the correlation between DDB1 and immune cells by combining with TISIDB infiltration was evaluated, and the clustering results of cell subtypes and the expression of DDB1 in different immune cell subpopulations were analysed by single-cell sequencing; finally, tissue microarrays were used to further verify the expression and prognostic value of DDB1 in LUAD. RESULTS: The mRNA and protein expression of DDB1 in LUAD tissues were significantly higher than those in normal tissues (P<0.01), and the high expression correlated with later clinical stage (P<0.001), lymph node metastasis (P<0.001) and poor prognosis (P<0.001). Functional enrichment showed that DDB1 was involved in DNA repair and RNA transcriptional regulation, and TISIDB evaluation revealed that DDB1 was negatively correlated with the expression level of immune cells, suggesting the potential regulation of the immune microenvironment. Single cell analysis showed that DDB1 was mainly expressed in T cells, alveolar macrophages and dendritic cells. Tissue microarrays confirmed that overall survival was shorter in the DDB1 high expression group (P<0.001), and Cox multifactorial analysis showed that DDB1 was an independent predictor of LUAD prognosis. CONCLUSIONS DDB1 is highly expressed in LUAD, which is associated with poor prognosis, and is closely related to tumor immune cell infiltration, and is involved in tumourigenesis and development through DNA repair and RNA transcriptional regulation. DDB1 can be used as a potential prognostic marker and therapeutic target for LUAD.
Humans ; Adenocarcinoma of Lung/immunology* ; DNA-Binding Proteins/metabolism* ; Lung Neoplasms/diagnosis* ; Gene Expression Regulation, Neoplastic ; Prognosis ; Male ; Female ; Middle Aged

Lung cancer has the highest incidence and mortality of any cancer in the world. In recent years, with the development of microbial detection technology, the intratumor microbiota has gradually become a hot spot and frontier in the field of lung cancer research. Studies have found that the microbiota present in tumors can influence the development of lung cancer in a variety of ways. In addition, the intratumor microbiota can be used as a potential biomarker for the diagnosis and prognosis assessment of lung cancer, and the regulation of the intratumor microbiota of lung cancer is expected to become a new type of lung cancer treatment. In this paper, we reviewed the latest research progress on the relationship between intratumor microbiota and lung cancer, summarized the origin and characteristics of intratumor microbiota, discussed the mechanism of its influence on the occurrence and development of lung cancer, and explored its potential applications in the diagnosis, treatment and prognosis of lung cancer.
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Humans ; Lung Neoplasms/diagnosis* ; Microbiota ; Animals ; Prognosis

In recent years, the widespread application of chest computed tomography (CT) screening has led to a significant increase in the detection rate of pulmonary nodules. As a critical diagnostic tool for early-stage lung cancer, video-assisted thoracic surgery (VATS) has emerged as the preferred therapeutic approach for pulmonary nodules. Clinical evidence demonstrates that precise preoperative localization significantly enhances surgical success rates (reducing conversion to thoracotomy), minimizes complications, and shortens operation time. This comprehensive review systematically evaluates six cutting-edge localization techniques: percutaneous puncture-assisted localization, electromagnetic navigation bronchoscopy (ENB) localization, 3D-printed auxiliary localization, basin-analysis-based localization, robotic navigation system localization, and mixed reality (MR)-guided localization. By critically analyzing their operational principles, efficacy, safety profiles, and clinical applicability, this paper aims to provide evidence-based recommendations for optimizing clinical decision-making in pulmonary nodule management.
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Humans ; Lung Neoplasms/diagnosis* ; Solitary Pulmonary Nodule/diagnostic imaging* ; Thoracic Surgery, Video-Assisted/methods* ; Multiple Pulmonary Nodules/diagnostic imaging* ; Tomography, X-Ray Computed

Oligometastasis represents a transitional state between early localized disease and widespread metastasis, characterized by limited tumor burden and distinct tumor biological behavior. Due to the relatively restricted number of metastatic lesions and involved organs, aggressive systemic therapy combined with local consolidative therapy offers potential for cure. With rapid advancements in molecular targeted therapies and immunotherapy, comprehensive management of oligometastatic non-small cell lung cancer (NSCLC) has gained increasing attention. This review summarizes the definition of NSCLC oligometastasis, recent therapeutic progress, and existing challenges, aiming to provide insights for clinical diagnosis and treatment strategies.
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Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Lung Neoplasms/diagnosis* ; Neoplasm Metastasis

Immune checkpoint inhibitors (ICIs) have been approved for the treatment of a variety of solid tumors and hematological malignancies. Adverse reactions caused by ICIs have been gradually focused on. Cytomegalovirus (CMV) gastritis after ICIs treatment is relatively rare. Here we reported a case of advanced lung adenocarcinoma who experienced recurrent upper abdominal pain and vomiting after Pembrolizumab treatment. CMV gastritis was diagnosed through gastroscopy. The patient's symptoms improved after antiviral treatment. During the treatment of ICIs, attention should be paid to the differential diagnosis of upper abdominal pain symptoms, and vigilance should be maintained against CMV gastritis. It is difficult to differentiate CMV gastritis and immune-related gastritis judging from symptoms, and gastroscopy is important for differential diagnosis.
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Humans ; Adenocarcinoma of Lung/drug therapy* ; Cytomegalovirus/physiology* ; Cytomegalovirus Infections ; Gastritis/diagnosis* ; Immune Checkpoint Inhibitors/therapeutic use* ; Lung Neoplasms/drug therapy*