ObjectiveTo explore the efficiency of Ki-67 expression and CT imaging features in predicting the degree of invasion of lung adenocarcinoma. MethodsThe clinical data of 217 patients with pulmonary nodules, who were diagnosed as suspicious lung cancer by multi-disciplinary treatment clinic of pulmonary nodules in our hospital from September 2017 to August 2021, were retrospectively analyzed. There were 84 males and 133 females, aged 52 (25-84) years. The patients were divided into two groups according to the infiltration degree, including an adenocarcinoma in situ and microinvasive adenocarcinoma group (n=145) and an invasive adenocarcinoma group (n=72). ResultsThere was no statistical difference in the age and gender between the two groups (P>0.05). The univariate analysis showed that CK-7, P63, P40 and CK56 expressions were not different between the two groups (P=0.172, 0.468, 0.827, 0.313), while Napsin A, TTF-1 and Ki-67 expressions were statistically different (P=0.002, 0.020, <0.001). The multivariate analysis showed that Ki-67 expression was statistically different between the two groups (P<0.001). Ki-67 was positively correlated with malignant features of CT images and the degree of lung adenocarcinoma invasion (P<0.05). Ki-67 and CT imaging features alone could predict the degree of lung adenocarcinoma invasion, but their sensitivity and specificity were not high. Ki-67 combined with CT imaging features could achieve a higher prediction efficiency.ConclusionCompared with Ki-67 or CT imaging features alone, the combined prediction of Ki-67 and imaging features is more effective, which is of great significance for clinicians to select the appropriate operation occasion.
Following the rapid advancement of artificial intelligence technologies, especially the development of large language models like ChatGPT, the field of medical clinical practice is undergoing an unprecedented technological revolution. These advanced technologies, through efficient processing and analysis of large datasets, not only provide medical professionals with auxiliary diagnoses and treatment suggestions but also significantly enhance the quality and efficiency of medical education. This study conducts a comprehensive analysis and review of the applications of large language models in various aspects, including clinical inquiry, history collection, medical literature writing, clinical decision support, optimization of medical portal websites, patient health management, medical education, academic research, and scientific writing. However, the application of these technologies is not without flaws and presents several limitations and ethical challenges. This paper focuses on challenges related to technological errors, academic dishonesty, abuse risks, over-reliance, possibilities of misdiagnosis and treatment errors, and issues of accountability. In conclusion, large language models demonstrate tremendous potential in the integration and advancement of medical practices. Nevertheless, while fully harnessing the benefits brought by ChatGPT, it is essential to acknowledge and address these ethical challenges to ensure that the application of ChatGPT in the medical field is responsible and effective.
Objective To discuss the main pathological types and imaging characteristics of pulmonary nodules that are highly suspected to be malignant in clinical practice but are pathologically confirmed to be benign. Methods A retrospective analysis was performed on the clinical data of patients with pulmonary nodules who were initially highly suspected of malignancy but were subsequently pathologically confirmed to be benign. These patients were treated at the First Affiliated Hospital of Xiamen University from December 2020 to April 2023. Based on the outcomes of preoperative discussions, the patients were categorized into a benign group and a suspicious malignancy group. The clinical data and imaging characteristics of both groups were compared. Results A total of 232 patients were included in the study, comprising 112 males and 120 females, with a mean age of (50.7±12.0) years. Among these, 127 patients were classified into the benign group, while 105 patients were categorized into the suspicious malignancy group. No statistically significant differences were observed between the two groups regarding age, gender, symptoms, smoking history, or tumor history (P>0.05). However, significant differences were noted in nodule density, CT values, margins, shapes, and malignant signs (P＜0.05). Further analysis revealed that in the suspicious malignancy group, solid nodules were predominantly characterized by collagen nodules and fibrous tissue hyperplasia (33.3%), followed by tuberculosis (20.4%) and fungal infections (18.5%). In contrast, non-solid nodules were primarily composed of collagen nodules and fibrous tissue hyperplasia (41.2%) and atypical adenomatous hyperplasia (17.7%). ConclusionBenign pulmonary nodules that are suspected to be malignant are pathologically characterized by the presence of collagen nodules, fibrous tissue hyperplasia, tuberculosis, atypical adenomatous hyperplasia, and fungal infections. Radiologically, these nodules typically present as non-solid lesions and may exhibit features suggestive of malignancy, including spiculation, lobulation, cavitation, and pleural retraction.