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.

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: Hypertrophy ligamentum flavum (LFH) is a common cause of lumbar spinal stenosis, resulting in significant disability and morbidity. Although long noncoding RNAs (lncRNAs) have been associated with various biological processes and disorders, their involvement in LFH remains not fully understood. Methods: Human ligamentum flavum samples were analyzed using lncRNA sequencing followed by validation through quantitative real-time polymerase chain reaction. To explore the potential biological functions of differentially expressed lncRNA-associated genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. We also studied the impact of lncRNA PARD3-AS1 on the progression of LFH in vitro. Results: In the LFH tissues when compared to that in the nonhypertrophic ligamentum flavum (LFN) tissues, a total of 1,091 lncRNAs exhibited differential expression, with 645 upregulated and 446 downregulated. Based on GO analysis, the differentially expressed transcripts primarily participated in metabolic processes, organelles, nuclear lumen, cytoplasm, protein binding, nucleic acid binding, and transcription factor activity. Moreover, KEGG pathway analysis indicated that the differentially expressed lncRNAs were associated with the hippo signaling pathway, nucleotide excision repair, and nuclear factor-kappa B signaling pathway. The expression of PARD3-AS1, RP11-430G17.3, RP1-193H18.3, and H19 was confirmed to be consistent with the sequencing analysis. Inhibition of PARD3-AS1 resulted in the suppression of fibrosis in LFH cells, whereas the overexpression of PARD3-AS1 promoted fibrosis in LFH cells in vitro. Conclusion This study identified distinct expression patterns of lncRNAs that are linked to LFH, providing insights into its underlying mechanisms and potential prognostic and therapeutic interventions. Notably, PARD3-AS1 appears to play a significant role in the pathophysiology of LFH.

Objective: Hypertrophy ligamentum flavum (LFH) is a common cause of lumbar spinal stenosis, resulting in significant disability and morbidity. Although long noncoding RNAs (lncRNAs) have been associated with various biological processes and disorders, their involvement in LFH remains not fully understood. Methods: Human ligamentum flavum samples were analyzed using lncRNA sequencing followed by validation through quantitative real-time polymerase chain reaction. To explore the potential biological functions of differentially expressed lncRNA-associated genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. We also studied the impact of lncRNA PARD3-AS1 on the progression of LFH in vitro. Results: In the LFH tissues when compared to that in the nonhypertrophic ligamentum flavum (LFN) tissues, a total of 1,091 lncRNAs exhibited differential expression, with 645 upregulated and 446 downregulated. Based on GO analysis, the differentially expressed transcripts primarily participated in metabolic processes, organelles, nuclear lumen, cytoplasm, protein binding, nucleic acid binding, and transcription factor activity. Moreover, KEGG pathway analysis indicated that the differentially expressed lncRNAs were associated with the hippo signaling pathway, nucleotide excision repair, and nuclear factor-kappa B signaling pathway. The expression of PARD3-AS1, RP11-430G17.3, RP1-193H18.3, and H19 was confirmed to be consistent with the sequencing analysis. Inhibition of PARD3-AS1 resulted in the suppression of fibrosis in LFH cells, whereas the overexpression of PARD3-AS1 promoted fibrosis in LFH cells in vitro. Conclusion This study identified distinct expression patterns of lncRNAs that are linked to LFH, providing insights into its underlying mechanisms and potential prognostic and therapeutic interventions. Notably, PARD3-AS1 appears to play a significant role in the pathophysiology of LFH.

Objective: Hypertrophy ligamentum flavum (LFH) is a common cause of lumbar spinal stenosis, resulting in significant disability and morbidity. Although long noncoding RNAs (lncRNAs) have been associated with various biological processes and disorders, their involvement in LFH remains not fully understood. Methods: Human ligamentum flavum samples were analyzed using lncRNA sequencing followed by validation through quantitative real-time polymerase chain reaction. To explore the potential biological functions of differentially expressed lncRNA-associated genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. We also studied the impact of lncRNA PARD3-AS1 on the progression of LFH in vitro. Results: In the LFH tissues when compared to that in the nonhypertrophic ligamentum flavum (LFN) tissues, a total of 1,091 lncRNAs exhibited differential expression, with 645 upregulated and 446 downregulated. Based on GO analysis, the differentially expressed transcripts primarily participated in metabolic processes, organelles, nuclear lumen, cytoplasm, protein binding, nucleic acid binding, and transcription factor activity. Moreover, KEGG pathway analysis indicated that the differentially expressed lncRNAs were associated with the hippo signaling pathway, nucleotide excision repair, and nuclear factor-kappa B signaling pathway. The expression of PARD3-AS1, RP11-430G17.3, RP1-193H18.3, and H19 was confirmed to be consistent with the sequencing analysis. Inhibition of PARD3-AS1 resulted in the suppression of fibrosis in LFH cells, whereas the overexpression of PARD3-AS1 promoted fibrosis in LFH cells in vitro. Conclusion This study identified distinct expression patterns of lncRNAs that are linked to LFH, providing insights into its underlying mechanisms and potential prognostic and therapeutic interventions. Notably, PARD3-AS1 appears to play a significant role in the pathophysiology of LFH.

Objective: Hypertrophy ligamentum flavum (LFH) is a common cause of lumbar spinal stenosis, resulting in significant disability and morbidity. Although long noncoding RNAs (lncRNAs) have been associated with various biological processes and disorders, their involvement in LFH remains not fully understood. Methods: Human ligamentum flavum samples were analyzed using lncRNA sequencing followed by validation through quantitative real-time polymerase chain reaction. To explore the potential biological functions of differentially expressed lncRNA-associated genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. We also studied the impact of lncRNA PARD3-AS1 on the progression of LFH in vitro. Results: In the LFH tissues when compared to that in the nonhypertrophic ligamentum flavum (LFN) tissues, a total of 1,091 lncRNAs exhibited differential expression, with 645 upregulated and 446 downregulated. Based on GO analysis, the differentially expressed transcripts primarily participated in metabolic processes, organelles, nuclear lumen, cytoplasm, protein binding, nucleic acid binding, and transcription factor activity. Moreover, KEGG pathway analysis indicated that the differentially expressed lncRNAs were associated with the hippo signaling pathway, nucleotide excision repair, and nuclear factor-kappa B signaling pathway. The expression of PARD3-AS1, RP11-430G17.3, RP1-193H18.3, and H19 was confirmed to be consistent with the sequencing analysis. Inhibition of PARD3-AS1 resulted in the suppression of fibrosis in LFH cells, whereas the overexpression of PARD3-AS1 promoted fibrosis in LFH cells in vitro. Conclusion This study identified distinct expression patterns of lncRNAs that are linked to LFH, providing insights into its underlying mechanisms and potential prognostic and therapeutic interventions. Notably, PARD3-AS1 appears to play a significant role in the pathophysiology of LFH.