Objective To develop an innovative recognition algorithm that aids physicians in the identification of pulmonary nodules. MethodsPatients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, Affiliated Drum Tower Hospital of Nanjing University Medical School in December 2023, were enrolled in the study. Chest surface exploration data were collected at a rate of 60 frames per second and a resolution of 1 920×1 080. Frame images were saved at regular intervals for subsequent block processing. An algorithm database for lung nodule recognition was developed using the collected data. ResultsA total of 16 patients were enrolled, including 9 males and 7 females, with an average age of (54.9±14.9) years. In the optimized multi-topology convolutional network model, the test results demonstrated an accuracy rate of 94.39% for recognition tasks. Furthermore, the integration of micro-variation amplification technology into the convolutional network model enhanced the accuracy of lung nodule identification to 96.90%. A comprehensive evaluation of the performance of these two models yielded an overall recognition accuracy of 95.59%. Based on these findings, we conclude that the proposed network model is well-suited for the task of lung nodule recognition, with the convolutional network incorporating micro-variation amplification technology exhibiting superior accuracy. Conclusion Compared to traditional methods, our proposed technique significantly enhances the accuracy of lung nodule identification and localization, aiding surgeons in locating lung nodules during thoracoscopic surgery.
Objective To investigate the risk factors, diagnosis and treatment of solitary pulmonary nodule （diameter≤3cm）. Methods From Jan. 2001 to Dec. 2002, the clinical data of 297 patients with solitary pulmonary nodule were reviewed. Chi-square or t-test were used in univariate analysis of age, gender, symptom, smoking history, the size, location and radiological characteristics of nodule, and logistic regression in multivariate analysis. Results Univariate analysis revealed that malignancy was significantly associated with age （P=0. 000）, smoking history （P=0. 001）, the size （P=0. 000） and radiological characteristics （P=0. 000） of nodule. In multivariate analysis （logistic regression）, it was significantly associated with age （OR = 1. 096）, the size （OR = 2. 329） and radiological characteristics （OR=0. 167） of nodule. Conclusion Age and the size of nodule could be risk factors. Radiological findings could help distinguish from malignant nodules.
ObjectiveTo study the therapeutic efficacy of stereoelectroencephalography (SEEG)-guided radiofrequency thermo-coagulation ablation (RF-TC) in the treatment of tuberous sclerosis (TSC) related epilepsy and to investigate the prediction of the therapeutic response to SEEG-guided RF-TC for the efficacy of the subsequent surgical treatment. MethodsWe retrospectively analyze TSC patients who underwent SEEG phase II evaluation from January 2014 to January 2023, and to select patients who underwent RF-TC after completion of SEEG monitoring, study the seizure control of patients after RF-TC, and classify patients into effective and ineffective groups for RF-TC treatment according to the results of RF-TC treatment, compare the surgical outcomes of patients in the two groups after SEEG, to explore the prediction of surgical outcome by RF-TC treatment. Results59 patients with TSC were enrolled, 53 patients (89.83%) were genetic detection, of which 28 (52.83%) were TSC1-positive, 21 (39.62%) were TSC2-positive, and 4 (7.54%) were negative, with 33 (67.34%) de novo mutations. The side of the SEEG electrode placement: left hemisphere in 9 cases, right hemisphere in 13 cases, and bilateral hemisphere in 37 cases. 37 patients (62.71%) were seizure-free at 3 months, 31 patients (52.54%) were seizure-free at 6 months, 29 patients (49.15%) were seizure-free at 12 months, and 20 patients (39.21%) were seizure-free at 24 months or more. 11 patients had a seizure reduction of more than 75% after RF-TC, and the remaining 11 patients showed no significant change after RF-TC. There were 48 patients (81.35%) in the effective group and 11 patients (18.65%) in the ineffective group. In the effective group, 22 patients were performed focal tuber resection laser ablation, 19 cases were seizure-free (86.36%). In the ineffective group, 10 patients were performed focal tuber resection laser ablation, only 5 cases were seizure-free (50%), which was a significant difference between the two groups (P<0.05). ConclusionsOur data suggest that SEEG guided RF-TC is a safe and effective both diagnostic and therapeutic treatment for TSC-related epilepsy, and can assist in guiding the development of future resective surgical strategies and determining prognosis.
Abstract： Sarcoidosis is a common systemic disease with noncaseating granulomatous epithelioid nodule and coexisting granulomatous inflammation. Although sarcoidosis can affect any organ of the body， more than 90% of the patients demonstrate thoracic involvement， which is often confusing with lung cancer and other diseases. Therefore, thoracic surgeons must have a clear understanding of sarcoidosis. Moreover， due to the special role of surgery in obtaining pathological specimens， thoracic surgeon plays an important role in the diagnosis and treatment of sarcoidosis. It is not difficult to make diagnosis for patients with typical clinical features of sarcoidosis. However， the majority of patients do not have specific manifestations of sarcoidosis. The cause of sarcoidosis remains unknown， and there is also no specific treatment strategy for it. But recent research has shown that annexin A11 gene may be involved in the pathogenesis of sarcoidosis， and tumor necrosis factor （TNF） inhibitor is effective in the treatwent of sarcoidosis.
Objective To investigate the accuracy of 18F-FDG positron emission tomography/computed tomography (PET/CT) combined with CT three-dimensional reconstruction (CT-3D) in the differential diagnosis of benign and malignant pulmonary nodules. Methods The clinical data of patients who underwent pulmonary nodule surgery in the Department of Thoracic Surgery, Northern Jiangsu People's Hospital from July 2020 to August 2021 were retrospectively analyzed. The preoperative 18F-FDG PET/CT and chest enhanced CT-3D and other imaging data were extracted. The parameters with diagnostic significance were screened by the area under the receiver operating characteristic (ROC) curve (AUC). Three prediction models, including PET/CT prediction model (MOD PET), CT-3D prediction model (MOD CT-3D), and PET/CT combined CT-3D prediction model (MOD combination), were established through binary logistic regression, and the diagnostic performance of the models were validated by ROC curve. Results A total of 125 patients were enrolled, including 57 males and 68 females, with an average age of 61.16±8.57 years. There were 46 patients with benign nodules, and 79 patients with malignant nodules. A total of 2 PET/CT parameters and 5 CT-3D parameters were extracted. Two PET/CT parameters, SUVmax≥1.5 (AUC=0.688) and abnormal uptake of hilar/mediastinal lymph node metabolism (AUC=0.671), were included in the regression model. Among the CT-3D parameters, CT value histogram peaks (AUC=0.694) and CT-3D morphology (AUC=0.652) were included in the regression model. Finally, the AUC of the MOD PET was verified to be 0.738 [95%CI (0.651, 0.824)], the sensitivity was 74.7%, and the specificity was 60.9%; the AUC of the MOD CT-3D was 0.762 [95%CI (0.677, 0.848)], the sensitivity was 51.9%, and the specificity was 87.0%; the AUC of the MOD combination was 0.857 [95%CI (0.789, 0.925)], the sensitivity was 77.2%, the specificity was 82.6%, and the differences were statistically significant (P<0.001). Conclusion 18F-FDG PET/CT combined with CT-3D can improve the diagnostic performance of pulmonary nodules, and its specificity and sensitivity are better than those of single imaging diagnosis method. The combined prediction model is of great significance for the selection of surgical timing and surgical methods for pulmonary nodules, and provides a theoretical basis for the application of artificial intelligence in the pulmonary nodule diagnosis.