With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective:To explore the effects of miR-125b on the proliferation, invasion and migration of pancreatic cancer cells by targeting SMYD2 signaling pathway.Methods:The expression of miRNA-125b in Aspc-1 and BxPC-3 lines of pancreatic cancer cells were detected. miRDB, ENCORI and TargetScan databases were used to predict the potential target genes of miRNA-125b. The downstream target genes of miRNA-125b were identified by qPCR assay and double luciferase reporter gene assay. Western blot analysis was performed to detect SMYD2 protein expression after transfection with miRNA-125b inhibitor. EdU staining, Annexin V-FITC/PI assay and Annexin V-FITC/PI assay were used to detect the effects of miRNA-125b inhibitor transfection and simultaneous transfection of miRNA-125b and SMYD2 inhibitor on cell proliferation, clonogenesis and apoptosis.Results:The expression level of miRNA-125b in pancreatic cancer cell lines was higher than that in normal pancreatic duct cells ( P<0.05). The downstream target gene of miRNA-125b was identified as SMYD2 by qPCR assay and double luciferase reporter gene assay. The expression of SMYD2 protein in miR-125b inhibitor group was higher than that in NC group ( P<0.01). EdU cell proliferation assay showed that the number of miRNA-125b positive cells in inhibitor group was lower than that in NC group and Inhibitor NC group ( P<0.05). The number of clones in miR-125b inhibitor+si-SMYD2 group was more than that in miR-125b inhibitor group ( P<0.01). Annexin V-FITC/PI assay showed that the apoptosis number of cell cells in miR-125b inhibitor+si-SMYD2 group was lower than that in miR-125b inhibitor group ( P<0.01) . Conclusion:miRNA-125b is highly expressed in pancreatic cancer cells, and can directly affect the expression of SMYD2 gene, thereby promoting the proliferation and inhibiting the apoptosis of pancreatic cancer cells.

Objective:To explore the factors associated with the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in the treatment of neuropathic pain (NP) following spinal cord injury (SCI).Methods:This was a retrospective study of 89 SCI survivors with NP receiving high-frequency rTMS. Those with a ≥30% reduction in their Numeric Rating Scales (NRS) scores after 2 weeks of treatment were termed Responders ( n=36), with the others classified as non-responders ( n=53). Demographic data (gender, education level, age), SCI characteristics (injury etiology, injury severity, neurological injury level, injury duration), NP characteristics (pain type, pain intensity, analgesic use), functional assessment (Modified Ashworth Scale score, Spinal Cord Independence Measure score, Modified Barthel Index score, American Spinal Injury Association motor/sensory score) were collected. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by binary logistic regression to identify factors associated with treatment efficacy. Results:Among the 89 patients, 36 (40.4%) were Responders to high-frequency rTMS. Binary logistic regression revealed that those with a cervical spinal cord injury and/or spasticity and women were more likely to respond to high-frequency rTMS.Conclusions:Female gender, cervical spinal cord injury, and spasticity are independent factors predicting rTMS efficacy in treating SCI, with spasticity demonstrating the strongest association.

Objective:To explore the factors associated with the efficacy of high-frequency repetitive transcranial magnetic stimulation (rTMS) in the treatment of neuropathic pain (NP) following spinal cord injury (SCI).Methods:This was a retrospective study of 89 SCI survivors with NP receiving high-frequency rTMS. Those with a ≥30% reduction in their Numeric Rating Scales (NRS) scores after 2 weeks of treatment were termed Responders ( n=36), with the others classified as non-responders ( n=53). Demographic data (gender, education level, age), SCI characteristics (injury etiology, injury severity, neurological injury level, injury duration), NP characteristics (pain type, pain intensity, analgesic use), functional assessment (Modified Ashworth Scale score, Spinal Cord Independence Measure score, Modified Barthel Index score, American Spinal Injury Association motor/sensory score) were collected. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by binary logistic regression to identify factors associated with treatment efficacy. Results:Among the 89 patients, 36 (40.4%) were Responders to high-frequency rTMS. Binary logistic regression revealed that those with a cervical spinal cord injury and/or spasticity and women were more likely to respond to high-frequency rTMS.Conclusions:Female gender, cervical spinal cord injury, and spasticity are independent factors predicting rTMS efficacy in treating SCI, with spasticity demonstrating the strongest association.

Clamshell incision is widely used in the surgical resection of complex mediastinal tumors, but it is also controversial. Its advantages are the full exposure of the field of vision and the huge operating space, while its disadvantages are the destruction of chest wall muscles and respiratory function impairment. At present, there is a lack of norms for the selection and application of Clamshell incisions, and the definition of complex mediastinal tumors is ambiguous and the dimensions are single. Therefore, this article reviews the literature and combines practical experience to systematically summarize the application progress of Clamshell incisions in the surgical resection of complex mediastinal tumors, with the aim of providing guidance for clinical work.

Objective To explore a nomogram model based on clinical characteristics and immune indicators for predicting the efficacy of tyrosine kinase inhibitor(TKI)against chronic myeloid leukemia(CML).Methods Clinical data was retrospectively collected from 100 patients with CML treated with TKI between January 2021 and January 2023 in Tangshan Gongren Hospital.Patients were divided into the best response and warning/treatment failure groups according to therapeutic efficacy.Factors affecting therapeutic efficacy were analyzed using logistic regression analysis,and a nomogram model was constructed.Results The best response and warning/treatment failure groups showed significant differences in red blood cell distribution width(RDW),platelet count(PLT),ELTS score,Th 1/CD4+,Treg/CD4+ratio,white blood cell count,and absolute value of natural killer cells(P＜0.05).Logistic regression confirmed that the above indicators were influencing factors(P＜0.05),indicating that the model was meaningful,and had a high goodness of fit as well as high predictive value.Conclusion The nomogram model constructed based on RDW,PLT,and other factors can effectively predict the thera-peutic efficacy of AKI in treating CML.

Objective:To explore the effects of miR-125b on the proliferation, invasion and migration of pancreatic cancer cells by targeting SMYD2 signaling pathway.Methods:The expression of miRNA-125b in Aspc-1 and BxPC-3 lines of pancreatic cancer cells were detected. miRDB, ENCORI and TargetScan databases were used to predict the potential target genes of miRNA-125b. The downstream target genes of miRNA-125b were identified by qPCR assay and double luciferase reporter gene assay. Western blot analysis was performed to detect SMYD2 protein expression after transfection with miRNA-125b inhibitor. EdU staining, Annexin V-FITC/PI assay and Annexin V-FITC/PI assay were used to detect the effects of miRNA-125b inhibitor transfection and simultaneous transfection of miRNA-125b and SMYD2 inhibitor on cell proliferation, clonogenesis and apoptosis.Results:The expression level of miRNA-125b in pancreatic cancer cell lines was higher than that in normal pancreatic duct cells ( P<0.05). The downstream target gene of miRNA-125b was identified as SMYD2 by qPCR assay and double luciferase reporter gene assay. The expression of SMYD2 protein in miR-125b inhibitor group was higher than that in NC group ( P<0.01). EdU cell proliferation assay showed that the number of miRNA-125b positive cells in inhibitor group was lower than that in NC group and Inhibitor NC group ( P<0.05). The number of clones in miR-125b inhibitor+si-SMYD2 group was more than that in miR-125b inhibitor group ( P<0.01). Annexin V-FITC/PI assay showed that the apoptosis number of cell cells in miR-125b inhibitor+si-SMYD2 group was lower than that in miR-125b inhibitor group ( P<0.01) . Conclusion:miRNA-125b is highly expressed in pancreatic cancer cells, and can directly affect the expression of SMYD2 gene, thereby promoting the proliferation and inhibiting the apoptosis of pancreatic cancer cells.

Objective To explore a nomogram model based on clinical characteristics and immune indicators for predicting the efficacy of tyrosine kinase inhibitor(TKI)against chronic myeloid leukemia(CML).Methods Clinical data was retrospectively collected from 100 patients with CML treated with TKI between January 2021 and January 2023 in Tangshan Gongren Hospital.Patients were divided into the best response and warning/treatment failure groups according to therapeutic efficacy.Factors affecting therapeutic efficacy were analyzed using logistic regression analysis,and a nomogram model was constructed.Results The best response and warning/treatment failure groups showed significant differences in red blood cell distribution width(RDW),platelet count(PLT),ELTS score,Th 1/CD4+,Treg/CD4+ratio,white blood cell count,and absolute value of natural killer cells(P＜0.05).Logistic regression confirmed that the above indicators were influencing factors(P＜0.05),indicating that the model was meaningful,and had a high goodness of fit as well as high predictive value.Conclusion The nomogram model constructed based on RDW,PLT,and other factors can effectively predict the thera-peutic efficacy of AKI in treating CML.