Objective To explore the pathological diagnostic value of optical coherence tomography (OCT) in lung cancer. Methods This study selected patients who underwent general anesthesia and electronic bronchoscope biopsy at the Respiratory Endoscopy Center of Sichuan Provincial People’s Hospital from January 1, 2023, to December 1, 2023. White-light bronchoscopy (WLB), auto-fluorescence bronchoscopy (AFB), and OCT examinations were performed in all patients. Lesions were assessed for benign or malignant characteristics based on AFB and OCT before biopsy. The final pathological results were determined according to pathology report. Results A total of 124 patients were included in the study. The accuracy of OCT in differentiating the nature of lesions was 93.55%, significantly higher than AFB (accuracy 83.06%). The accuracy, sensitivity, and specificity of OCT were all higher than AFB. For squamous carcinoma, adenocarcinoma, and small cell lung cancer, the accuracy rates of OCT imaging characteristics were 91.94%, 94.35%, and 94.35%, respectively. Conclusion OCT can improve the accuracy of pre-bronchoscopic tissue pathology biopsy in determining the nature of lesions and provide rapid pathological typing basis, potentially further promoting the development of non-invasive histological biopsy.
Objective To evaluate medical students’ perceptions and attitudes toward artificial intelligence (AI)-assisted diagnosis of renal cell carcinoma (RCC), and to analyze their educational needs regarding AI in pathological diagnosis. Methods A questionnaire survey (including closed and open-ended questions) was conducted to assess medical students’ perceptions, attitudes, and educational needs concerning AI-assisted RCC diagnosis. Participants included medical students from different specialties and standardized training residents. The questionnaire covered demographic information, perceptions and attitudes toward AI, and AI-related educational needs. Results A total of 249 respondents completed the survey. The majority were standardized training residents, mostly aged 23-26 years, and 40.96% had practical experience in pathological diagnosis of RCC. The median scores for most closed-ended questions were 4. Respondents generally considered “efficiency” and “improved accuracy” as the most prominent advantages of AI, with timeliness, automated diagnosis, reduction of human error, and precise diagnosis being the most emphasized aspects. Analysis of AI-related educational needs revealed high-frequency keywords such as “expanding sample size” “balanced responsibility allocation” and “enhancing collaboration skills.” Conclusion Medical students hold a positive attitude toward AI and its application in RCC diagnosis, but there remains a lack of formal AI-related education.
