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.

Objective To propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

Cancer,as a major public health issue that seriously threatens public health,the im-provement of its three-level prevention system is crucial for reducing the disease burden.Integrative medicine,leveraging the combined advantages of the holistic concept and syndrome differentiation theory of traditional Chinese medicine(TCM)and the precise detection and standardized treat-ment techniques of Western medicine,demonstrates unique value in the field of cancer preven-tion.Currently,TCM has achieved certain results in the three-level prevention of cancer.However,it still faces challenges such as insufficient public awareness,lack of multi-disciplinary collabo-ration mechanisms,and a weak evidence-based research system.This paper systematically ana-lyzes the practical status quo,existing problems,and development directions of integrative medicine in the three-level prevention of cancer,aiming to provide theoretical basis and practical references for constructing an efficient and collaborative prevention system and promoting the standardization and internationalization of integrative medicine in cancer prevention.

Objective:To evaluate the safety and efficacy of accelerator-based boron neutron capture therapy (AB-BNCT) in the treatment of recurrent and refractory malignant tumors.Methods:The data of 14 patients admitted to Xiamen Humanity Hospital from September 2022 to April 2023 were prospectively collected, including 7 patients with primary brain malignancies and 7 patients with locally recurrent inoperable head and neck malignancies. All patients received intravenous infusion of boron drug (NBB-001, p-dihydroxyborylphe nylalanine, a patented freeze-dried formulation) at a total nominal dosage of 500 mg/kg (11 patients) or 750 mg/kg (3 patients), and were irradiated with neutrons (operating with NeuPex system). Adverse events after treatment were recorded and assessed. The primary efficacy endpoint was the 90 d objective response rate (ORR), while the secondary endpoints included progression-free survival (PFS) and complete response rate (CRR). Data were compiled and analyzed by SAS 9.4 software. The rate and 95% CI were calculated using Clopper-Pearson method. Results:The median dose delivered to 80% of the target volume (D 80%) was 16.80 GyE (range: 8.93-23.79 GyE). The most common adverse reactions were hyperamylasemia, alopecia, and hyperprolactinemia. Five patients experienced 8 cases of grade 3 or above adverse events, including 1 case of grade 4 acute kidney injury and 7 cases of grade 3 adverse events. All adverse events were recovered after observation or treatment. At 90 d after treatment, the ORR of all patients was 9/14 (64%, 95% CI: 35%-87%), disease control rate (DCR) was 10/14 (71%, 95% CI: 42%-92%), CRR was 2/14 (14%, 95% CI: 2%-42%); and the best overall response during the entire course included an ORR of 10/14 (71% ,95% CI: 42%-92%), DCR of 13/14 (93%, 95% CI: 66%-100%), and CRR of 3/14 (21% ,95% CI: 5%-51%). The 1-year survival rate for head and neck malignancies was 71.4%, and the 2-year survival rate was 42.8%. The 1-year survival rate for recurrent brain malignancies was 42.8%. Conclusion:AB-BNCT demonstrates favorable safety and promising efficacy in treating primary brain malignancies and recurrent/refractory head and neck malignancies, representing a potential therapeutic option.

Radiation-induced esophageal injury (RIEI) is a frequent complication following radiotherapy for thoracic and head-neck malignancies, which may lead to severe sequelae including esophageal stricture and perforation, adversely affecting patients' quality of life and therapeutic outcomes. With advancements in radiotherapy techniques — particularly the adoption of unconventional fractionation regimens, concurrent chemoradiotherapy, and combined molecular targeted / immunotherapy — the incidence of RIEI has been increasing. In this review, recent advances in understanding the pathogenesis, clinical manifestations, risk factors, and management strategies for RIEI were comprehensively summarized. Current therapeutic approaches have evolved beyond conventional anti-inflammatory and nutritional support to include novel interventions such as targeted therapy, free radical scavengers, and microbiota modulation, etc. Future research should prioritize the development of optimized, individualized prevention and treatment protocols to mitigate RIEI risk and improve patient prognosis.

Objective:To evaluate the safety and efficacy of accelerator-based boron neutron capture therapy (AB-BNCT) in the treatment of recurrent and refractory malignant tumors.Methods:The data of 14 patients admitted to Xiamen Humanity Hospital from September 2022 to April 2023 were prospectively collected, including 7 patients with primary brain malignancies and 7 patients with locally recurrent inoperable head and neck malignancies. All patients received intravenous infusion of boron drug (NBB-001, p-dihydroxyborylphe nylalanine, a patented freeze-dried formulation) at a total nominal dosage of 500 mg/kg (11 patients) or 750 mg/kg (3 patients), and were irradiated with neutrons (operating with NeuPex system). Adverse events after treatment were recorded and assessed. The primary efficacy endpoint was the 90 d objective response rate (ORR), while the secondary endpoints included progression-free survival (PFS) and complete response rate (CRR). Data were compiled and analyzed by SAS 9.4 software. The rate and 95% CI were calculated using Clopper-Pearson method. Results:The median dose delivered to 80% of the target volume (D 80%) was 16.80 GyE (range: 8.93-23.79 GyE). The most common adverse reactions were hyperamylasemia, alopecia, and hyperprolactinemia. Five patients experienced 8 cases of grade 3 or above adverse events, including 1 case of grade 4 acute kidney injury and 7 cases of grade 3 adverse events. All adverse events were recovered after observation or treatment. At 90 d after treatment, the ORR of all patients was 9/14 (64%, 95% CI: 35%-87%), disease control rate (DCR) was 10/14 (71%, 95% CI: 42%-92%), CRR was 2/14 (14%, 95% CI: 2%-42%); and the best overall response during the entire course included an ORR of 10/14 (71% ,95% CI: 42%-92%), DCR of 13/14 (93%, 95% CI: 66%-100%), and CRR of 3/14 (21% ,95% CI: 5%-51%). The 1-year survival rate for head and neck malignancies was 71.4%, and the 2-year survival rate was 42.8%. The 1-year survival rate for recurrent brain malignancies was 42.8%. Conclusion:AB-BNCT demonstrates favorable safety and promising efficacy in treating primary brain malignancies and recurrent/refractory head and neck malignancies, representing a potential therapeutic option.

Radiation-induced esophageal injury (RIEI) is a frequent complication following radiotherapy for thoracic and head-neck malignancies, which may lead to severe sequelae including esophageal stricture and perforation, adversely affecting patients' quality of life and therapeutic outcomes. With advancements in radiotherapy techniques — particularly the adoption of unconventional fractionation regimens, concurrent chemoradiotherapy, and combined molecular targeted / immunotherapy — the incidence of RIEI has been increasing. In this review, recent advances in understanding the pathogenesis, clinical manifestations, risk factors, and management strategies for RIEI were comprehensively summarized. Current therapeutic approaches have evolved beyond conventional anti-inflammatory and nutritional support to include novel interventions such as targeted therapy, free radical scavengers, and microbiota modulation, etc. Future research should prioritize the development of optimized, individualized prevention and treatment protocols to mitigate RIEI risk and improve patient prognosis.

Cancer,as a major public health issue that seriously threatens public health,the im-provement of its three-level prevention system is crucial for reducing the disease burden.Integrative medicine,leveraging the combined advantages of the holistic concept and syndrome differentiation theory of traditional Chinese medicine(TCM)and the precise detection and standardized treat-ment techniques of Western medicine,demonstrates unique value in the field of cancer preven-tion.Currently,TCM has achieved certain results in the three-level prevention of cancer.However,it still faces challenges such as insufficient public awareness,lack of multi-disciplinary collabo-ration mechanisms,and a weak evidence-based research system.This paper systematically ana-lyzes the practical status quo,existing problems,and development directions of integrative medicine in the three-level prevention of cancer,aiming to provide theoretical basis and practical references for constructing an efficient and collaborative prevention system and promoting the standardization and internationalization of integrative medicine in cancer prevention.

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 construct a predictive model for clinical pregnancy outcomes in frozen-thawed embryo transfer(FET)cycles in women with advanced maternal age(age≥35 years)and to analyze its influencing factors.Methods A retrospective analysis was conducted on the clinical data of 2717 older patients who underwent FET treatment at the First Affiliated Hospital of Kunming Medical University from January 2018 to December 2023.These Patients were divided into a clinical pregnancy group(n=851)and a non-clinical pregnancy group(n=1866)based on whether the clinical pregnancy had occurred.The general characteristics and transplantation details of the two groups were compared,and a logistic regression analysis model was constructed.Results The clinical pregnancy rate(CPR)was 31.32%.The CPR for women aged 35-40 years(40.06%)was higher than that for women aged≥40 years(19.35%),with a statistically significant difference(χ2=133.371,P<0.05).The model results showed that the higher anti-Mullerian hormone(AMH)levels(OR=1.053,95%CI:1.012-1.095),the more high-quality blastocysts were transferred(OR=1.704,95%CI:1.143-2.542;OR=2.861,95%CI:1.921-4.262);the more high-quality blastocysts were transferred(OR=2.033,95%CI:1.077-3.836;OR=3.886,95%CI:2.035-7.420),the thicker the endometrial lining on the day of transfer(OR=1.150,95%CI:1.092-1.212)and it could increase the probability of clinical pregnancy.However,for women over 40 years of age(OR=0.551,95%CI:0.437-0.694)and secondary infertility(OR=0.704,95%CI:0.552-0.896),the probability of clinical pregnancy would be reduced;ROC curve analysis results showed that the AUC for predicting clinical pregnancy occurrence in the training set and validation set of the predictive model were 0.723(95%CI:0.699-0.748)and 0.726(95%CI:0.689-0.764),respectively,with cutoff values of 0.262 and 0.260 and the model fit was good(P>0.05).Conclusion Female age,AMH level,type of infertility,number of high-quality embryos(cleavage embryos,blastocysts)transferred,and endometrial thickness on the day of transfer are important factors affecting FET cycles in advanced maternal age women.The constructed prediction model based on these factors has a certain predictive ability for clinical pregnancy.