ObjectiveTo explore the effect of preoperative glucocorticoid on systemic inflammatory indexes and pulmonary inflammation after radical esophagectomy.MethodsA total of 44 patients with esophageal cancer treated in the First Affiliated Hospital of Xiamen University from July 2019 to September 2020 were selected and randomly divided into an intervention group and an observation group by random number table. There were 22 patients in the intervention group, including 20 males and 2 females with an average age of 62.86±5.22 years and 22 patients in the observation group, including 19 males and 3 females with an average age of 63.00±6.19 years. Two groups were given thoracoscope-assisted incision via right chest, upper abdomen and left neck. The intervention group was given an intravenous infusion of methylprednisolone 500 mg before induction of anesthesia, and the observation group was given the same dose of normal saline. The second generation cephalosporins were routinely used to prevent infection in the two groups. The levels of interleukin-6 (IL-6) and C-reactive protein (CRP), lymphocyte and neutrophil count before operation and 1 day, 3 days and 5 days after operation were recorded and compared between the two groups. Utrecht Pneumonia Scoring System (UPSS) score 1 day after operation, the healing of the surgical incision and the anastomotic leakage within 2 weeks after the operation were evaluated.ResultsThe level of IL-6 in the intervention group was significantly lower than that in the observation group at 1 hour and 1 day after operation (both P<0.05). CRP showed significant difference between the two groups 2 days after operation (P=0.044). The white blood cell count in the intervention group was significantly less than that in the observation group 1 day and 3 days after operation (both P<0.05). There was no significant difference in lymphocyte or neutrophil count between the two groups 1 day after operation. There was no significant difference in the rate of non-grade A wound healing or the incidence of anastomotic leakage between the two groups within 2 weeks after operation. The pneumonia score of UPSS in the intervention group was lower than that in the observation group 1 day after operation (P=0.027).ConclusionThe use of glucocorticoid before radical esophagectomy can reduce the systemic inflammatory reaction and improve the short-term postoperative pulmonary inflammation. At the same time, no adverse effect on the healing of surgical incision and anastomotic stoma is found, which has certain safety.
The informatization construction in medical field not only brings convenience to clinical doctors, but also creates huge data for clinical research. Taking the application of information technology in thoracic surgery as an example, we decide to talk about the establishment and application of esophageal cancer database based on standardized and structured electronic medical records. The aim, through the construction of database, is to improve clinical doctors’ management ability of esophageal cancer, to provide reference of the information construction to medical colleagues, and to promote the application of information in medicine.
As an emerging technology, artificial intelligence (AI) uses human theory and technology for robots to study, develop, learn and identify human technologies. Thoracic surgeons should be aware of new opportunities that may affect their daily practice by the direct use of AI technology, or indirect use in the relevant medical fields (radiology, pathology, and respiratory medicine). The purpose of this paper is to review the application status and future development of AI associated with thoracic surgery, diagnosis of AI-related lung cancer, prognosis-assisted decision-making programs and robotic surgery. While AI technology has made rapid progress in many areas, the medical industry only accounts for a small part of AI use, and AI technology is gradually becoming widespread in the diagnosis, treatment, rehabilitation, and care of diseases. The future of AI is bright and full of innovative perspectives. The field of thoracic surgery has conducted valuable exploration and practice on AI, and will receive more and more influence and promotion from AI.
ObjectiveTo analyze the value of structured electronic medical records for pulmonary nodules in increasing the ability of outpatient service and hospital management by resident physicians.MethodsWe included 40 trainees [94 males and 26 females aged 22-31 (26.45±2.81) years] who were trained in the standardized training base for surgical residents in our hospital from January 2018 to January 2021. The trainees were randomly divided into two groups including a structured group using the structured electronic medical record for pulmonary nodule and an unstructured group using unstructured electronic medical record designed by our department. The time of completing hospitalization records and first-time course records, the quality of course records, the accuracy of issuing admission orders, the quality of teaching rounds, and patient’s satisfaction between the two groups were analyzed and compared.Results(1) The average time in the structured group to complete inpatient medical records was significantly shorter than that of the unstructured group (53.61±8.12 min vs. 84.25±16.09 min, P<0.010); the average time in the structured group to complete the first-time course record was shorter than that of the unstructured group (13.20±5.43 min vs. 27.51±8.62 min, P<0.010), and there was a significant statistical difference between the two groups. (2) The overall teaching round quality score of the students in the structured group was significantly higher than that in the unstructured group (84.21±15.61 vs. 70.91±12.28, P<0.010). (3) The score of the medical record writing quality of the structured group was significantly higher than that of the unstructured group (80.25±9.22 vs. 74.22±5.40, P<0.010).ConclusionThe structured electronic medical record specific for pulmonary nodules can effectively improve the training efficiency in the standardized training of surgical residents, improve the clinical ability to deal with pulmonary nodules, improve the integrity and accuracy of key clinical data collected by students, and improve doctor-patient relationship.
Lung cancer is a disease with high incidence rate and high mortality rate worldwide. Its diagnosis and treatment mode is developing constantly. Among them, multi-disciplinary team (MDT) can provide more accurate diagnosis and more individualized treatment, which can not only benefit more early patients, but also prolong the survival time of late patients. However, MDT diagnosis and treatment mode is only carried out more in provincial and municipal tertiary hospitals and has not been popularized. This article intends to introduce MDT mode and its advantages, hoping that MDT mode can be popularized and applied.
Non-small cell lung cancer is the main cause of cancer death in the world, and its incidence is increasing year by year, seriously endangering human health. Early non-small cell lung cancer is generally difficult to be detected based on symptoms and signs. Therefore, accurate pathological diagnosis and accurate prediction of prognosis are crucial for formulating the best treatment plan for non-small cell lung cancer patients and improving their survival. The application of artificial intelligence in the diagnosis and treatment of non-small cell lung cancer has shown good performance and great potential effect. This paper introduces the research progress of artificial intelligence in predicting the classification, staging, genomics and prognosis of non-small cell lung cancer.
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.
MicroRNAs (miRNAs) are a class of short, highly conserved, non-coding RNA molecules that regulate gene expression by specific binding to the messenger RNAs (mRNAs). At present, the researches on miRNAs have caused immense global concern, and expression of miR-139-5p plays a significant role in tumorigenesis, metastasis and recurrence, through regulating proliferation, migration, and invasion of cancer cells in lung cancer, esophageal cancer, breast cancer, tongue squamous cell carcinoma, hepatocellular carcinoma, etc. MiR-139-5p has a positive impact on the prognosis of cancer, and it can combine with some chemotherapeutic drugs to reverse resistance and enhance the sensitivity of radiotherapy. It also works in the cells and tissues of other diseases, including nerve cells, and inflammation. This article reviewed the progress of miR-139-5p.
ObjectiveTo investigate the relationship between primary pulmonary mucinous adenocarcinoma (PPMA) mass type and pneumonia type and the difference of malignant degree, and to analyze the role of clinical manifestations and CT features in the diagnosis of this disease. MethodsThe clinical data of PPMA patients admitted in the First Affiliated Hospital of Xiamen University from May 2011 to March 2022 were retrospectively analyzed. According to CT features, they were divided into a mass type group and a pneumonia type group. The clinical manifestations, CT features and the relationship between Ki-67 index and the degree of malignancy between the two groups were analyzed and compared, and the diagnostic ability of PPMA was enhanced. ResultsA total of 57 PPMA patients were enrolled. There were 17 males and 40 females, with an average age of 53.82±10.65 years, and 28 (49%) patients had reversed hato-like sign. There were statistically significant differences between the two groups in the maximum diameter of tumor, boundary condition and pleural indentation sign (P<0.05). There was no significant difference between the two groups in Ki-67% index and its high and low value-added groups (P>0.05). ConclusionThere is no significant difference in the degree of malignancy between the two types of PPMA, which supports that the pneumonia type is only the manifestation of the disease progression of the lump type. PPMA is more common in elderly women and often occurs in both lower lungs. The clinical manifestations are mainly cough and white mucinous sputum. CT scans can show various manifestations, among which the reversed hato-like sign is expected to become an important imaging feature. Combined with high solid fraction, pleural indentation sign, and vacuole sign, it can play a significant auxiliary role in its diagnosis.
ObjectiveTo evaluate the effectiveness of the artificial intelligence-assisted diagnosis and treatment system in distinguishing benign and malignant lung nodules and the infiltration degree.MethodsClinical data of 87 patients with pulmonary nodules admitted to the First Affiliated Hospital of Xiamen University from January 2019 to August 2020 were retrospectively analyzed, including 33 males aged 55.1±10.4 years, and 54 females aged 54.5±14.1 years. A total of 90 nodules were included, which were divided into a malignant tumor group (n=80) and a benign lesion group (n=10), and the malignant tumor group was subdivided into an invasive adenocarcinoma group (n=60) and a non-invasive adenocarcinoma group (n=20). The malignant probability and doubling time of each group were compared and its ability to predict the benign and malignant nodules and the invasion degree was analyzed.ResultsBetween the malignant tumor group and the benign lesion group, the malignant probability was significantly different, and the malignant probability could better distinguish malignant nodules and benign lesions (87.2%±9.1% vs. 28.8%±29.0%, P=0.000). The area under the curve (AUC) was 0.949. The maximum diameter of nodules in the benign lesion group was significantly longer than that in the malignant tumor group (1.270±0.481 cm vs. 0.990±0.361 cm, P=0.026); the doubling time of benign lesions was significantly longer than that of malignant nodules (1 083.600±258.180 d vs. 527.025±173.176 d, P=0.000), and the AUC was 0.975. The maximum diameter of the nodule in the invasive adenocarcinoma group was longer than that of the non-invasive adenocarcinoma group (1.350±0.355 cm vs. 0.863±0.271 cm, P=0.000), and there was no statistical difference in the probability of malignancy between the invasive adenocarcinoma group and the non-invasive adenocarcinoma group (89.7%±5.7% vs. 86.4%±9.9%, P=0.082). The AUC was 0.630. The doubling time of the invasive adenocarcinoma group was significantly shorter than that of the non-invasive adenocarcinoma group (392.200±138.050 d vs. 571.967±160.633 d, P=0.000), and the AUC was 0.829.ConclusionThe malignant probability and doubling time of lung nodules calculated by the artificial intelligence-assisted diagnosis and treatment system can be used in the assessment of the preoperative benign and malignant lung nodules and the infiltration degree.