The possibility of solitary pulmonary nodules tending to lung cancer is very high in the middle and late stage. In order to detect the middle and late solitary pulmonary nodules, we present a new computer-aided diagnosis method based on the geometric features. The new algorithm can overcome the disadvantage of the traditional algorithm which can't eliminate the interference of vascular cross section. The proposed algorithm was implemented by multiple clustering of the extracted geometric features of region of interest (ROI) through K-means algorithm, including degree of slenderness, similar degree of circle, degree of compactness and discrete degree. The 232 lung CT images were selected from Lung Image Database Consortium (LIDC) database to do contrast experiment. Compared with the traditional algorithm, the detection rate of the new algorithm was 92.3%, and the error rate was 14.8%. At the same time, the detection rate of the traditional algorithm was only 83.9%, and the error rate was 78.2%. The results show that the proposed algorithm can mark the solitary pulmonary nodules more accurately and reduce the error rate due to precluding the disturbance of vessel section.
Objective To explore the diagnostic and treatment value of computed tomography (CT)-guided embolization coil localization of pulmonary nodules accurately resected under the thoracoscope. Methods Between October 2015 and October 2016, 40 patients with undiagnosed nodules of 15 mm or less were randomly divided into a no localization group (n=20, 11 males and 9 females with an average age of 60.50±8.27 years) or preoperative coil localization group (n=20, 12 males and 8 females with an average age of 61.35±8.47 years). Coils were placed with the distal end deep to the nodule and the superficial end coiled on the visceral pleural surface with subsequent visualization by video-assisted thoracoscopic (VATS). Nodules were removed by VATS wedge excision using endo staplers. The tissue was sent for rapid pathological examination, and the pulmonary nodules with definitive pathology found at the first time could be defined as the exact excision. Results The age, sex, forced expiratory volume in the first second of expiration, nodule size/depth were similar between two groups. The coil group had a higher rate of accurate resection (100.00% vs. 70.00%, P=0.008), less operation time to nodule excision (35.65±3.38 minvs. 44.38±11.53 min,P=0.003), and reduced stapler firings (3.25±0.85vs. 4.44±1.26,P=0.002) with no difference in total costs. Conclusion Preoperative CT-guided coil localization increases the rate of accurate resection.
ObjectiveTo investigate the preoperative psychological state of patients with pulmonary nodules in order to make the content of the education more "individualized and humanized".MethodsWe conducted a consecutive questionnaire study for 107 patients who were planning to undergo pulmonary resection surgery from May 2018 to July 2018 in our department. There were 54 males and 53 females with an average age of 56.8±11.2 years. The questionnaire content included two parts: personal basic information and 20 questions about surgery, complications, follow-up and hospitalization expense.ResultsThere were 60.7% of the patients diagnosed with pulmonary nodules by CT scan during physical examination, and 52.3% of the patients had strong will to undergo pulmonary surgery to resect nodules; 64.5% of patients wanted doctors to tell them the extent of the disease and whether the tumor could be cured by surgery, and 30.0% of patients concerned whether chief surgeon would complete the whole surgery. The surgery risk and postoperative complications were ignored by patients easily (5.6% and 14.9% respectively). The hospital expenses were not the primary concern of patients. Only 1.9% of patients believed that doctors used nonessentials which deliberately led to increased costs. Network follow-up was accepted by most patients (94.4%).ConclusionIt will contribute to improve preoperative education rationality and effectiveness by understanding true psychological state of patients.
Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.
Accurate segmentation of pulmonary nodules is an important basis for doctors to determine lung cancer. Aiming at the problem of incorrect segmentation of pulmonary nodules, especially the problem that it is difficult to separate adhesive pulmonary nodules connected with chest wall or blood vessels, an improved random walk method is proposed to segment difficult pulmonary nodules accurately in this paper. The innovation of this paper is to introduce geodesic distance to redefine the weights in random walk combining the coordinates of the nodes and seed points in the image with the space distance. The improved algorithm is used to achieve the accurate segmentation of pulmonary nodules. The computed tomography (CT) images of 17 patients with different types of pulmonary nodules were selected for segmentation experiments. The experimental results are compared with the traditional random walk method and those of several literatures. Experiments show that the proposed method has good accuracy in the segmentation of pulmonary nodule, and the accuracy can reach more than 88% with segmentation time is less than 4 seconds. The results could be used to assist doctors in the diagnosis of benign and malignant pulmonary nodules and improve clinical efficiency.
Lung cancer has the highest mortality rate among all malignant tumors. The key to reducing lung cancer mortality is the accurate diagnosis of pulmonary nodules in early-stage lung cancer. Computer-aided diagnostic techniques are considered to have potential beyond human experts for accurate diagnosis of early pulmonary nodules. The detection and classification of pulmonary nodules based on deep learning technology can continuously improve the accuracy of diagnosis through self-learning, and is an important means to achieve computer-aided diagnosis. First, we systematically introduced the application of two dimension convolutional neural network (2D-CNN), three dimension convolutional neural network (3D-CNN) and faster regions convolutional neural network (Faster R-CNN) techniques in the detection of pulmonary nodules. Then we introduced the application of 2D-CNN, 3D-CNN, multi-stream multi-scale convolutional neural network (MMCNN), deep convolutional generative adversarial networks (DCGAN) and transfer learning technology in classification of pulmonary nodules. Finally, we conducted a comprehensive comparative analysis of different deep learning methods in the detection and classification of pulmonary nodules.
ObjectiveTo investigate the diagnostic value of tuberculosis T cell spot test (T-SPOT.TB) and erythrocyte sedimentation rate (ESR) test in the diagnosis of simple pulmonary nodules in Xinjiang.MethodsA retrospective analysis of 72 patients with asymptomatic simple pulmonary nodules in the Department of Thoracic Surgery, the First Affiliated Hospital of Xinjiang Medical University from October 2017 to July 2019 was performed. According to the pathological results, the patients were divided into a tuberculoma group [n=23, including 14 males and 9 females, aged 37-84 (56.91±12.73) years] and a lung cancer group [n=49, including 31 males and 18 females, aged 34-83 (61.71±10.15) years]. The predictive value of T-SPOT.TB and ESR results for the diagnosis of simple pulmonary nodules was evaluated.ResultsThe positive rate of T-SPOT.TB in the tuberculoma group (69.60%) was higher than that in the lung cancer group (42.90%) (χ2=5.324, P=0.021), with a sensitivity of 69.56% and specificity of 57.14%; the positive ESR was 47.80% in the tuberculoma group and 59.20% in the lung cancer group, and no statistical difference was found between the two groups (χ2=0.981, P=0.322), with a sensitivity of 47.82% and specificity of 40.81%; the area under receiver operating characteristic curve (AUC) was 0.618, the 95% confidence interval of AUC was (0.479, 0.758), and the Youden’s index was 0.267 with a sensitivity of 69.60% and specificity of 57.10%. Difference in the T-SPOT.TB and ESR test results was statistically significant (χ2=4.986, P=0.026), but the correlation between the tests was weak with a Pearson contingency coefficient of 0.199. ESR results in patients with different ages were statistically different (χ2=7.343, P=0.025), but the correlation between age and ESR results was weak with a Pearson contingency coefficient of 0.239; T-SPOT.TB results in patients with different ages were not statistically different (χ2=0.865, P=0.649), and the correlation between age and ESR results was weak with a Pearson contingency coefficient of 0.084.ConclusionThe diagnostic value of T-SPOT.TB and ESR tests is small in the diagnosis of simple pulmonary nodules.
Increasing peripheral pulmonary nodules are detected given the growing adoption of chest CT screening for lung cancer. The invention of electromagnetic navigation bronchoscope provides a new diagnosis and treatment method for pulmonary nodules, which has been demonstrated to be feasible and safe, and the technique of microwave ablation through bronchus is gradually maturing. The one-stop diagnosis and treatment of pulmonary nodules can be completed by the combination of electromagnetic navigation bronchoscopy and microwave ablation, which will help achieve local treatment through the natural cavity without trace.
ObjectiveTo explore the safety and effectiveness of a precise marking method based on body surface mesh and three-dimensional (3D) image reconstruction.MethodsWe retrospectively analyzed the clinical data of 22 patients in our hospital from October 2018 to October 2019. There were 13 males and 9 females aged 58.5 (37-72) years. All patients underwent a precise marking of pulmonary nodules based on body surface mesh and 3D image reconstruction. Then, video-assisted thoracoscopic surgery (VATS) was performed to resect the nodules. The clinical data, including positioning success rate and operation time were analyzed.ResultsA total of 22 small pulmonary nodules were removed. The average diameter of small nodules was 12±3 mm, and the average distance from the visceral pleura was 17±6 mm. The localization success rate was 86.4%. The operation time was 110±43 min, and there was no surgery-related complication.ConclusionThe method of marking pulmonary nodules based on body surface mesh and 3D image reconstruction is a safe and reliable technology, which reduces the risk of hemopneumothorax caused by CT-guided lung puncture.
The widespread use of low-dose computed tomography (LDCT) in lung cancer screening has enabled more and more lung nodules to get identified of which more than 20% are multiple pulmonary nodules. At present, there is no guideline or consensus for multiple pulmonary nodules whose management is based primarily on the pulmonary imaging characteristics and associated risk factors. Herein, this review covers the imaging methods, CT appearances and management of multiple pulmonary nodules.