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 construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

Malignant tumors are a class of diseases that pose a serious threat to human health, and radiotherapy (RT) is one of the essential treatment modalities. Virtual reality (VR) technology, with its unique characteristics of immersion, interactivity, and imagination, demonstrates tremendous application potential in radiotherapy. Several randomized controlled trials have confirmed that VR, as a non-pharmacological intervention, can effectively alleviate anxiety in patients, especially children, and improve treatment adherence. In medical education and professional training, VR provides safe training platforms for radiation oncologists, medical physicists, and radiation therapists through the construction of high-fidelity virtual environments. These platforms can be used for training skills in target volume delineation, treatment plan evaluation, and equipment quality control, thereby significantly improving training efficiency and quality. Furthermore, VR technology enables three-dimensional visualization and intuitive evaluation of treatment plans, facilitates multidisciplinary team (MDT) collaboration, and provides new paradigms for the development of real-time guidance technologies that integrate with augmented reality. This article systematically reviews the current applications of VR technology in the clinical practice, medical education, radiation protection, and scientific research of radiotherapy. It also discusses future prospects for integrating VR with artificial intelligence (AI), augmented reality (AR), and haptic feedback technologies. This review provides a valuable reference for the standardized application of VR technology in radiotherapy and radiation protection in China.

Malignant tumors are a class of diseases that pose a serious threat to human health, and radiotherapy (RT) is one of the essential treatment modalities. Virtual reality (VR) technology, with its unique characteristics of immersion, interactivity, and imagination, demonstrates tremendous application potential in radiotherapy. Several randomized controlled trials have confirmed that VR, as a non-pharmacological intervention, can effectively alleviate anxiety in patients, especially children, and improve treatment adherence. In medical education and professional training, VR provides safe training platforms for radiation oncologists, medical physicists, and radiation therapists through the construction of high-fidelity virtual environments. These platforms can be used for training skills in target volume delineation, treatment plan evaluation, and equipment quality control, thereby significantly improving training efficiency and quality. Furthermore, VR technology enables three-dimensional visualization and intuitive evaluation of treatment plans, facilitates multidisciplinary team (MDT) collaboration, and provides new paradigms for the development of real-time guidance technologies that integrate with augmented reality. This article systematically reviews the current applications of VR technology in the clinical practice, medical education, radiation protection, and scientific research of radiotherapy. It also discusses future prospects for integrating VR with artificial intelligence (AI), augmented reality (AR), and haptic feedback technologies. This review provides a valuable reference for the standardized application of VR technology in radiotherapy and radiation protection in China.

Malignant tumors are a class of diseases that pose a serious threat to human health, and radiotherapy (RT) is one of the essential treatment modalities. Virtual reality (VR) technology, with its unique characteristics of immersion, interactivity, and imagination, demonstrates tremendous application potential in radiotherapy. Several randomized controlled trials have confirmed that VR, as a non-pharmacological intervention, can effectively alleviate anxiety in patients, especially children, and improve treatment adherence. In medical education and professional training, VR provides safe training platforms for radiation oncologists, medical physicists, and radiation therapists through the construction of high-fidelity virtual environments. These platforms can be used for training skills in target volume delineation, treatment plan evaluation, and equipment quality control, thereby significantly improving training efficiency and quality. Furthermore, VR technology enables three-dimensional visualization and intuitive evaluation of treatment plans, facilitates multidisciplinary team (MDT) collaboration, and provides new paradigms for the development of real-time guidance technologies that integrate with augmented reality. This article systematically reviews the current applications of VR technology in the clinical practice, medical education, radiation protection, and scientific research of radiotherapy. It also discusses future prospects for integrating VR with artificial intelligence (AI), augmented reality (AR), and haptic feedback technologies. This review provides a valuable reference for the standardized application of VR technology in radiotherapy and radiation protection in China.

Objective To observe the difference of iron deposition in brain deep nuclei of systemic lupus erythematosus(SLE)patients with different disease activity levels using quantitative susceptibility mapping(QSM).Methods Fifty-four SLE patients were retrospectively enrolled and divided into moderate to severe activity group(score＞9,n=25)and mild activity group(score≤9,n=29)according to SLE disease activity index(SLEDAI)score.Patients' general clinical data,as well as the mean QSM of bilateral head of caudate nuclei,putamina,globi pallidi,red nuclei,substantiae nigrae,dentate nuclei and thalami were compared between groups.Pearson or Spearman coefficients were performed to analyze correlations of the mean QSM of nuclei being significant different between groups and conventional clinical indicators.Then receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of the mean QSM for distinguishing moderate to severe and mild activity SLE.Results Compared with mild activity group,moderate to severe activity group had higher serum ferritin,erythrocyte sedimentation rate(ESR),C reactive protein(CRP),anti-double stranded DNA(anti-dsDNA)antibody levels,also higher proportion of positive anti-ribosomal P protein(anti-P)antibodies,but lower complement C3 and C4 levels(all P＜0.05).The mean QSM of bilateral putamina and thalami were significantly higher in moderate to severe activity group than in mild activity group(both P＜0.05).The mean QSM of bilateral putamina was positively correlated with SLEDAI scores,ferritin levels and positivity of anti-P antibodies in SLE patients(with r or rs of 0.447,0.526 and 0.473,respectively,and all P＜0.05).The AUC for distinguishing moderate to severe SLE and mild activity SLE based on the mean bilateral putamina QSM was 0.810.Conclusion There were significant differences of iron deposition in deep brain nuclei of SLE patients with moderate to severe and mild activity.The mean QSM of bilateral putamina could be used to distinguish SLE with moderate to severe activity and mild activity.

[Objective] To compare the diagnostic accuracy of three imaging modalities, inlducing CT urography (CTU), contrast-enhanced MRI (CE-MRI), and contrast-enhanced ultrasound (CEUS) in the qualitative diagnosis of renal space-occupying lesions. [Methods] A retrospective analysis was performed on 542 patients with renal lesions confirmed by surgical pathology in our hospital during Jan.2019 and May 2024.The diagnostic results of CTU, CE-MRI and CEUS were compared and analyzed based on the patients' clinical and pathological data. [Results] The diagnostic accuracy rate of CTU, CE-MRI and CEUS were 84.50%, 83.14% and 86.14%, respectively.For the 161 patients who underwent all three examinations, CEUS was significantly more accurate than CTU (84.16% vs. 77.02%, P=0.018), while there was no significant difference between CTU or CEUS and CE-MRI (79.81%) (P>0.05). Further analysis found that for lesions ≤4 cm, the accuracy of the three examinations was as follows: CEUS=CTU 79.55%, CE-MRI 76.14%, with no significant difference (P>0.05). However, for lesions >4 cm, CEUS ranked the first, followed by CE-MRI and CTU (89.73% vs. 84.25% vs. 73.97%), and CEUS and CE-MRI were better than CTU (P<0.05). Additionally, for the diagnosis of clear cell renal carcinoma and benign renal space-occupying lesions, there was no statistically significant difference among the three imaging modalities (P>0.05), while for the qualitative diagnosis of non-clear cell renal carcinoma, CEUS ranked the first, followed by CE-MRI and CTU (83.87% vs. 74.19% vs. 56.45%), and CE-MRI and CEUS were better than CTU (P<0.05). [Conclusion] All of them have important diagnostic value, and the appropriate selection should be based on patients' specifc conditions.CEUS and CE-MRI are more accurate in the qualitative diagnosis of renal space-occupying lesions than CTU, especially for large lesions and non-clear cell carcinoma.

OBJECTIVE: An intelligent moxibustion instrument based on electromyography was designed to evaluate the real-time therapeutic effect of moxibustion. METHODS: Taking Shenshu (BL23) as the subject, surface electromyography (sEMG) at the center and equidistant points of Shenshu (BL23) were collected. The characteristic parameters, integrated electromyography (iEMG) and root mean square (RMS) were calculated before and after moxibustion. After analyzing the effect of moxibustion, a function algorithm for the end-of-moxibustion was obtained. Using this algorithm and combined with STM32 technology, the control system of moxibustion instrument and the upper computer software were designed to achieve the precise control during moxibustion delivery. Finally, the function, stability and safety of the moxibustion instrument were verified through clinical trials to ensure its effectiveness in practical application. RESULTS: During one cycle of moxibustion at the center of Shenshu (BL23), the iEMG of sEMG decreased over time, meaning the decrease in muscle fatigue degree, and after one cycle of moxibustion, it elevated over time, showing the increase in muscle fatigue degree. RMS increased by 1.90% before and after moxibustion at the equidistant points of Shenshu (BL23), and the system indicated the end of moxibustion when RMS increased by 0.15%, and decreased by 0.13% at the center of Shenshu (BL23). The intelligent moxibustion instrument designed based on this algorithm can realize the function of mild moxibustion, and the effect of moxibustion can be evaluated by the real-time monitoring of RMS changes through the upper computer. During the operation of moxibustion instrument, moxa stick was fixed stably, remained a safe distance of 3 cm to 4 cm away from the skin surface. When the length of moxa stick was less than 5 cm left after ignited and the skin temperature exceeded the preset safety threshold of 48 ℃, the system was alarmed automatically. CONCLUSION The intelligent moxibustion instrument designed in the research can effectively evaluate the effect of moxibustion, and ensure the safety and stability during moxibustion delivery.
Humans ; Moxibustion/methods* ; Electromyography/instrumentation* ; Adult ; Male ; Female ; Young Adult ; Acupuncture Points ; Algorithms ; Middle Aged

Objective: To investigate the association between emotional intelligence and sleep problems at the symptom level among junior and senior high school students, so as to provide new insights for interventions targeting junior and senior high school students sleep disorders. Methods: From November 2023 to May 2024, a stratified cluster random sampling method was employed to select 3 531 first year junior high school and first year senior high school students from 6 schools in Guangyuan City and Liangshan Yi Autonomous Prefecture in Sichuan Province, as well as Lhasa City in Tibet Autonomous Region. The Insomnia Severity Index Scale and the Wong and Law Emotional Intelligence Scale(WLEIS) were used to assess sleep problems and emotional intelligence. A network analysis was performed to explore the relationship between emotional intelligence and sleep disorders, and a gender based network comparison analysis was conducted. Results: The reported rate of sleep problems among junior and senior high school students was 47.3%, with severe sleep problems of 2.2%. Difficulty maintaining sleep, worry about sleep, and emotional application were the core symptoms in the network (node strength values: 1.11, 0.98, and 0.82, respectively). Dissatisfaction with sleep and emotional application served as bridge symptoms connecting emotional intelligence and sleep problems (bridge strength values: 1.77 and 1.59, respectively). The edge weights of the emotional intelligence and sleep problems network differed significantly between genders (maximum difference in edge weight values was 0.13， P <0.05). Conclusions Emotional application ability and dissatisfaction with sleep are the key nodes in the network connecting emotional intelligence and sleep problems. Targeted efforts to enhance emotional application ability may effectively reduce the risk of sleep problems among junior and senior high school students.