Objective To explore the independent risk factors for tumor invasiveness of ground-glass nodules and establish a tumor invasiveness prediction model. Methods A retrospective analysis was performed in 389 patients with ground-glass nodules admitted to the Department of Thoracic Surgery in the First Hospital of Lanzhou University from June 2018 to May 2021 with definite pathological findings, including clinical data, imaging features and tumor markers. A total of 242 patients were included in the study according to inclusion criteria, including 107 males and 135 females, with an average age of 57.98±9.57 years. CT data of included patients were imported into the artificial intelligence system in DICOM format. The artificial intelligence system recognized, automatically calculated and output the characteristics of pulmonary nodules, such as standard diameter, solid component size, volume, average CT value, maximum CT value, minimum CT value, central CT value, and whether there were lobulation, burr sign, pleural depression and blood vessel passing. The patients were divided into two groups: a preinvasive lesions group (atypical adenomatoid hyperplasia/adenocarcinoma in situ) and an invasive lesions group (minimally invasive adenocarcinoma/invasive adenocarcinoma). Univariate and multivariate analyses were used to screen the independent risk factors for tumor invasiveness of ground-glass nodules and then a prediction model was established. The receiver operating characteristic (ROC) curve was drawn, and the critical value was calculated. The sensitivity and specificity were obtained according to the Yorden index. Results Univariate and multivariate analyses showed that central CT value, Cyfra21-1, solid component size, nodular nature and burr of the nodules were independent risk factors for the diagnosis of tumor invasiveness of ground-glass nodules. The optimum critical value of the above indicators between preinvasive lesions and invasive lesions were –309.00 Hu, 3.23 ng/mL, 8.65 mm, respectively. The prediction model formula for tumor invasiveness probability was logit (P)=0.982–(3.369×nodular nature)+(0.921×solid component size)+(0.002×central CT value)+(0.526×Cyfra21-1)–(0.0953×burr). The areas under the curve obtained by plotting the ROC curve using the regression probabilities of regression model was 0.908. The accuracy rate was 91.3%. Conclusion The logistic regression model established in this study can well predict the tumor invasiveness of ground-glass nodules by CT and tumor markers with high predictive value.
ObjectiveTo investigate the relationship between the nodule manifestation of malignant pleural lesions under medical thoracoscopy and pleural fluid biochemistry and tumor marker levels. MethodsA total of 110 patients with malignant pleura, including 90 cases of lung cancer, 18 cases of malignant mesothelioma, 1 case of diffuse large B-cell lymphoma, and 1 case of ovarian serous carcinoma, who were hospitalized in the Department of Respiratory and Critical Care Medicine, East Hospital of Shandong Provincial Hospital from February 2011 to January 2022 were selected as the study subjects. The pleural nodule manifestation was divided into 6 layers were according to the number of pleural nodules in the medical thoracoscopic field, they were divided into 6 layers: non-nodular group, nodular group (pleural nodules of different sizes were distributed); The nodular group was further divided into nodular scattered group (total number of pleural nodules in all fields under thoracoscopy ≤10) and nodular diffuse group (total number of pleural nodules in all fields under thoracoscopy >10); The nodular diffuse group was further divided into the multiple nodules diffused group (the total number of pleural nodules >10 under thoracoscopy and ≤10 nodules in a single microscopic field) and the nodular diffuse patchwork group (the total number of pleural nodules >10 under thoracoscopy and >10 nodules in a single microscopic field). Four biochemical items of pleural fluid, pleural fluid lactate dehydrogenase (LDH), adenosine deaminase (ADA), glucose (GLU), protein quantification (TP) levels and pleural fluid carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) levels, serum CEA, and serum cytokeratin fragment 19 (CYFRA21-1) levels were measured to compare the expression levels of indicators between the non-nodular group and the nodular group, the nodular scattered group and the nodular diffuse group, the multiple nodules diffused group and the nodular diffuse patchwork group.ResultsThe LDH level in pleural fluid of nodular group was significantly higher than that of non-nodular group (P<0.01). The LDH level in pleural fluid of diffuse nodular group was higher than that of scattered nodular group (P<0.05). Compared to those in multiple nodules diffused group, the levels of LDH and ADA in pleural fluid of nodules patchy diffused group were significantly increased (P<0.01), and the GLU level was decreased (P<0.05). However, there were no statistically significant differences in the length of disease, smoking index, TP in pleural fluid, CEA in pleural fluid, CA125 in pleural fluid, CEA in serum and CYFRA21-1 in serum between the paired groups.ConclusionsThere were differences in the expression levels of LDH, ADA and GLU in pleural fluid of different degrees of malignant pleural lesions. The higher the degree of pleural lesions, the higher the levels of LDH and ADA in pleural fluid and the lower the levels of GLU in pleural fluid.
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.
ObjectiveTo establish and validate a predictive model for solid and partially solid lung nodules as poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. MethodsPatients who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patient CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operator characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. ResultsA total of 299 patients were included, with 103 males and 196 females, with a median age of 57 (51.00, 67.25) years; 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average CT value [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.988)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.936, and specificity of 0.750. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. ConclusionFor patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.