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 advancement of China's healthcare reform,enhancing the capacity of primary healthcare services has become a pivotal task.Colorectal cancer,one of the most prevalent malignancies in China,highlights the critical importance of early screening and diagnosis to improve patient survival rates.This study,guided by the principles of Party building and Xi Jinping Thought on Socialism with Chinese Characteristics,examines the implementation and outcomes of a rural outreach program focused on colorectal cancer screening and diagnostic technologies.By promoting the dissemination of colorectal cancer screening initiatives,the paper aims to provide empirical evidence to support the deepening of primary-care services,foster high-quality ad-vancement of grassroots health services,and align with the national Healthy China Initiative,thereby more effectively safeguarding population health.

With the advancement of China's healthcare reform,enhancing the capacity of primary healthcare services has become a pivotal task.Colorectal cancer,one of the most prevalent malignancies in China,highlights the critical importance of early screening and diagnosis to improve patient survival rates.This study,guided by the principles of Party building and Xi Jinping Thought on Socialism with Chinese Characteristics,examines the implementation and outcomes of a rural outreach program focused on colorectal cancer screening and diagnostic technologies.By promoting the dissemination of colorectal cancer screening initiatives,the paper aims to provide empirical evidence to support the deepening of primary-care services,foster high-quality ad-vancement of grassroots health services,and align with the national Healthy China Initiative,thereby more effectively safeguarding population health.

PURPOSE: This study was designed to assess the protein levels of transformation/transcription domain-associated protein (TRRAP) in invasive ductal breast carcinomas, and investigated the association between TRRAP and the clinicopathological features of breast cancer. METHODS: We examined TRRAP protein expression in 470 breast cancer tissues and normal breast tissues by tissue microarray to study the correlation between TRRAP expression and clinicopathological features. This was analyzed using the chi-square test. Kaplan-Meier survival curves and log-rank tests were applied to analyze the survival status. Cox regression was applied for multivariate analysis of prognosis. RESULTS: The data demonstrated that expression of TRRAP was significantly lower in breast carcinomas (36.6%) than in corresponding normal breast tissues (50.8%). In addition, TRRAP protein levels negatively correlated with tumor size, and indicated poor differentiation, increased nodal involvement, and low p53-positive rates. Analysis of survival revealed that lower TRRAP expression correlated with shorter survival time. Univariate analyses identified TRRAP and progesterone receptor as independent protective factors for breast cancer prognosis. However, Ki-67, tumor size, and nodal involvement appeared to be independent risk factors. CONCLUSION: The findings indicate a significant correlation between TRRAP protein levels and adverse prognosis in breast cancer. Therefore, TRRAP could be a prognostic biomarker for breast cancer. In addition, TRRAP is also a predictive biomarker of breast cancer treatment.
Biomarkers ; Breast Neoplasms* ; Breast* ; Kaplan-Meier Estimate ; Multivariate Analysis ; Prognosis ; Receptors, Progesterone ; Risk Factors