Objective To improve the knowledge of pulmonary sclerosing hemangioma ( PSH)especially with bilateral multiple lesions of the lung. Methods The clinical data of 3 cases of PSH ( 1 case with bilateral multiple lesions in the lung) were analyzed, and the related literatures were reviewed. Results All of the 3 cases were females. A 22-year-old female patientwith bilateral multiple nodules in the lungs was complicated with thyroid multiple nodular goiter ( with hypothyroidism) , dysfunctional uterine bleeding ( with anemia) , nodular hyperplasia of the breast, and arteriovenous malformation over forearm. Thoracoscopicbiopsy of left lung and resection of the right pulmonary mass were performed and both the lesions were confirmed as PSH. The clinical manifestations of multiorgan diseases and the presence of PSH suggested Cowden syndrome in this patient. The other 2 cases aged 50 and 53 were asymptomatic with solitary pulmonary nodules identified incidentally. The accessory examinations for malignancies, infections, and autoimmune diseases showed no specific findings. Resection of the lesions were performed by thoracoscopic surgery and thoracotomy respectively, and the histopathological results proved to be PSH. Literature review showed that PSH typically occurred in middle-aged women without clinical symptoms and signs, often presenting as a pulmonary solitary nodule/mass identified incidentally. The differential diagnosis should include peripheral carcinoma, hamartoma, inflammatory pseudotumor and tuberculoma. Multiple PSH, which mainly presented as multiple well-defined nodules /masses of different size in the lungs, was rather rare, but easily confused with metastatic neoplasm. Lung biopsy by surgical operation was a common way to confirm the diagnosis, while FDP-PET and fine needle aspiration biopsy showed some defects. Surgical resection was an effective method of treatment, the residual lesions of multiple PSH should be monitored. Cowden syndrome may be considered if a PSH coexisting with abnormity of multiple organs such as thyoid, breast and vessels. Conclusions PSH should be considered during the differential diagnosis for solitary or multiple nodules /masses in the lung. Surgical biopsy is a common way to confirm the diagnosis. Local excision andnecessary follow-up are effective methods of treatment.
ObjectiveTo systematically review the diagnostic accuracy of C-arm cone-beam CT (CBCT)-guided percutaneous transthoracic needle biopsy (PTNB) for lung nodules. MethodsWe electronically searched databases including PubMed, EMbase, EBSCO, Ovid, CBM, VIP, WanFang Data and CNKI from inception to Feb 28th, 2015, to collect diagnostic studies of CBCT-guided PTNB for lung nodules. Two reviewers independently screened literature, extracted data and assessed the methodological quality of included studies by QUADAS-1 tool. Then, meta-analysis was performed by Stata 12.0 and Meta-DiSc 1.4 softwares for calculating pooled sensitivity (Sen), specificity (Spe), positive likelihood ration (+LR), negative likelihood ration (-LR), and diagnostic odds ratio (DOR), drawing summary receiver operating characteristic (SROC) curve and estimating area under the curve (AUC). ResultsA total of 9 studies involving 1 815 patients were included. The results of meta-analysis showed that the pooled Sen, Spe, +LR,-LR, and DOR were 0.95 (95%CI 0.92 to 0.96), 1.00 (95%CI 0.66 to 1.00), 2 076.58 (95%CI 1.8 to 2.3e+0.6), 0.05 (95%CI 0.04 to 0.08), and 39 443.88 (95%CI 30.53 to 5.1e+0.7), respectively. The AUC of SROC was 0.97 (95%CI 0.95 to 0.98). ConclusionCBCT-guided PTNB can be used as one of the primary examination approaches for lung nodules with relatively high diagnostic accuracy. Due to limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveTo analyze the difference of location identification of pulmonary nodules in two dimensional (2D) and three dimensional (3D) images, and to discuss the identification methods and clinical significance of pulmonary nodules location in 3D space.MethodsThe clinical data of 105 patients undergoing sublobectomy in the Department of Thoracic Surgery, the First Affiliated Hospital with Nanjing Medical University from December 2018 to December 2019 were analyzed retrospectively. There were 28 males and 77 females, with an average age of 57.21±13.19 years. The nodule location was determined by traditional 2D method and 3D depth ratio method respectively, and the differences were compared.ResultsA total of 30 nodules had different position identification between the two methods, among which 25 nodules in the inner or middle zone of 2D image were located in the peripheral region of 3D image. The overall differences between the two methods were statistically significant (P<0.05). The diagnostic consistency rates of two methods were 66.67% in the right upper lung, 83.33% in the right middle lung, 73.68% in the right lower lung, 75.76% in the left upper lung, and 64.71% in the left lower lung. In each lung lobe, the difference between the two methods in the right upper lung (P=0.014) and the left upper lung (P=0.019) was statistically significant, while in the right middle lung (P=1.000), right lower lung (P=0.460) and left lower lung (P=0.162) were not statistically significant.ConclusionThe 3D position definition of lung nodules based on depth ratio is more accurate than the traditional 2D definition, which is helpful for preoperative planning of sublobectomy.
ObjectiveTo assess the accuracy of CT features of lung nodules (≤3 cm) in predicting the accuracy of the pathological subtype and degree of infiltration of adenocarcinoma. Methods We retrospectively analyzed the clinical data of 333 patients with non-cavitary pulmonary nodules diagnosed as adenocarcinoma by surgery and pathology in the China-Japan Friendship Hospital from 2011 to 2018, including 108 males and 225 females, aged 16-82 (59.57±10.16) years. The basic clinical data and CT characteristics of the patients were recorded. ResultsWhen the average CT value was ≥−507 Hu, the maximum diameter of the lung window was ≥14.5 mm, and the solid component ratio was ≥5.0%, it indicated more likely the invasive adenocarcinoma (IAC). The higher the average CT value of the nodule, the larger the maximum diameter of the lung window, and the more solid components, the higher the degree of infiltration. CT morphological features (including burrs, lobes, vascular signs, bronchial signs, pleural stretch or depression signs) were more common in IAC. Among them, burrs were more common in acinar adenocarcinoma and papillary adenocarcinoma. In invasive adenocarcinoma, the higher the risk of recurrence of the pathological subtype, the greater the average CT value. When the average CT value of IAC was >−106 Hu, and the proportion of solid components was ≥70.5%, the histological subtypes were more inclined to micropapillary/solid predominant adenocarcinoma. Conclusion The evaluation of CT features of lung nodules can improve the predictive value of histopathological types of lung adeno- carcinoma, thereby optimizing clinical treatment decisions and obtaining more ideal therapeutic effects.
Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional (3D) multi-view convolutional neural network (MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network (MVSECNN) model is constructed by introducing the squeeze-and-excitation (SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.