Objective To discuss the main pathological types and imaging features of pulmonary nodules highly suspected to be malignant in clinical practice but pathologically confirmed to be benign. Method A retrospective analysis was conducted on the clinical data of the patients with pulmonary nodules, who were initially highly suspected of malignancy but were pathologically confirmed as benign, treated at the First Affiliated Hospital of Xiamen University from December 2020 to April 2023. Based on the results of preoperative discussions, the patients were divided into a benign group and a suspicious malignancy group. Results Finally, 232 patients were collected, including 112 males and 120 females, with an average age of 51 years. There were 127 patients in the benign group, and 105 patients in the suspicious malignancy group. There was no statistical difference in age, gender, symptoms, smoking history, and tumor history between the two groups (P>0.05). However, there were statistical differences in nodule density, CT values, margins, shapes, andmalignant signs between the two groups (P<0.05). The analysis showed that in suspiciously malignant pulmonary nodules, the solid group was mainly characterized by collagen nodules and fibrous tissue hyperplasia (33.3%), tuberculosis (20.3%), and fungal infection (18.5%), while the non-solid group was primarily composed of collagen nodules and fibrous tissue hyperplasia (39.2%) and atypical adenomatous hyperplasia (17.6%). ConclusionThe benign pulmonary nodules suspected of malignancy are pathologically characterized by the presence of collagen nodules and fibrous tissue hyperplasia, tuberculosis, atypical adenomatous hyperplasia, and fungal infections. In terms of imaging features, they typically present as non-solid nodules, accompanied by signs of malignancy such as spiculation, lobulation, cavitation, and pleural retraction.
Objective To explore the value of preoperative detection of soluble fragments of cytokeratin-19 (CYFRA21-1), carcinoembryonic antigen (CEA), and postoperative detection of nuclear proliferation associated antigen Ki67 in prognostic evaluation of non-small cell lung cancer patients. Methods The clinicopathological data and follow-up results of patients with non-small cell lung cancer treated in the Department of Thoracic Surgery of the First Affiliated Hospital of Xiamen University in 2017 were collected. CYFRA21-1>3.39 ng/mL was defined as positive, and CEA>5 ng/mL was defined as positive. The receiver operating characteristic curve (ROC curve) of Ki67 expression level was drawn. The maximum area under the curve (AUC) was the cutoff value of Ki67 expression level, and the Ki67 expression level greater than its cutoff value was defined as positive. Cox regression analysis was used to determine the independent risk factors for poor prognosis in patients with non-small cell lung cancer. Results Finally 248 patients were collected, including 125 males and 123 females, with a median age of 61 years (ranging from 30 to 81 years) at the time of surgery. Univariate analysis showed that positive CYFRA21-1, high expression of Ki67, positive CEA, age≥60 years at operation, distant metastasis, lymph node metastasis, maximum tumor diameter>3 cm, and TNM stage Ⅲ were associated with poor prognosis in patients with non-small cell lung cancer. When combined detection of preoperative tumor markers and postoperative Ki67, the prognosis of all negative patients was the best, and that of all positive patients was the worst. Cox regression analysis showed that positive CEA+positive CYFRA21-1+high expression of Ki67 was an independent risk factor for poor prognosis in patients with non-small cell lung cancer (P<0.05). Conclusion The combined detection of preoperative serum CYFRA21-1, CEA, and postoperative Ki67 has important value in evaluating the prognosis of non-small cell lung cancer patients.
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.