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1.
Int. braz. j. urol ; 48(2): 294-302, March-Apr. 2022. tab, graf
Article in English | LILACS | ID: biblio-1364942

ABSTRACT

ABSTRACT Objective: To compare enhancement patterns of typical adrenal adenomas, lipid-poor adenomas, and non-adenomas on magnetic resonance imaging (MRI). Materials and Methods: Evaluation of adrenal nodules larger than 1.0 cm, with at least 2-year follow-up, evaluated on MRI in January 2007 and December 2016. Two different protocols were included - upper abdomen MRI (delayed phase after 3 minutes) and abdomen and pelvis MRI (delayed phase after 7 minutes) - and nodules were divided in typical adenomas (characterized on out-of-phase MRI sequence), lipid-poor adenomas (based on follow-up imaging stability) and non-adenomas (based on pathological finding or follow-up imaging). T2-weighted and enhancement features were analyzed (absolute and relative washout and enhancement curve pattern), similarly to classic computed tomography equations. Results: Final cohort was composed of 123 nodules in 116 patients (mean diameter of 1.8 cm and mean follow up time of 4 years and 3 months). Of them, 98 (79%) nodules had features of typical adenomas by quantitative chemical shift imaging, and demonstrated type 3 curve pattern in 77%, mean absolute and relative washout of 29% and 16%, respectively. Size, oncologic history and T2-weighted features showed statistically significant differences among groups. Also, a threshold greater than 11.75% for absolute washout on MRI achieved sensitivity of 71.4% and specificity of 70.0%, in differentiating typical adenomas from non-adenomas. Conclusion: Calculating absolute washout of adrenal nodules on MRI may help identifying proportion of non-adenomas.


Subject(s)
Humans , Adrenal Gland Neoplasms/pathology , Adrenal Gland Neoplasms/diagnostic imaging , Magnetic Resonance Imaging/methods , Tomography, X-Ray Computed/methods , Retrospective Studies , Sensitivity and Specificity , Contrast Media , Diagnosis, Differential
2.
Article in Chinese | WPRIM | ID: wpr-936369

ABSTRACT

OBJECTIVE@#To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images.@*METHODS@#We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method.@*RESULTS@#The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the datamodel coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P < 0.001).@*CONCLUSION@#The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.


Subject(s)
Algorithms , Humans , Image Processing, Computer-Assisted/methods , Phantoms, Imaging , Signal-To-Noise Ratio , Tomography, X-Ray Computed/methods , Water
3.
Article in Chinese | WPRIM | ID: wpr-936305

ABSTRACT

OBJECTIVE@#To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage.@*METHODS@#Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images.@*RESULTS@#At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively.@*CONCLUSION@#The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.


Subject(s)
Algorithms , Cerebral Hemorrhage/diagnostic imaging , Humans , Image Processing, Computer-Assisted/methods , Least-Squares Analysis , Tomography, X-Ray Computed/methods
4.
Chinese Journal of Lung Cancer ; (12): 147-155, 2022.
Article in Chinese | WPRIM | ID: wpr-928792

ABSTRACT

BACKGROUND@#At present, the research progress of targeted therapy for epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) gene mutations in lung adenocarcinoma is very rapid, which brings new hope for the treatment of advanced lung adenocarcinoma patients. However, the specific imaging and pathological features of EGFR and ALK gene mutations in adenocarcinoma are still controversial. This study will further explore the correlation between EGFR, ALK gene mutations and imaging and pathological features in invasive lung adenocarcinoma.@*METHODS@#A total of 525 patients with lung adenocarcinoma who underwent surgery in our center from January 2018 to December 2019 were included. According to the results of postoperative gene detection, the patients were divided into EGFR gene mutation group, ALK gene mutation group and wild group, and the EGFR gene mutation group was divided into exon 19 and exon 21 subtypes. The pathological features of the mutation group and wild group, such as histological subtype, lymph node metastasis, visceral pleural invasion (VPI) and imaging features such as tumor diameter, consolidation tumor ratio (CTR), lobulation sign, spiculation sign, pleural retraction sign, air bronchus sign and vacuole sign were analyzed by univariate analysis and multivariate Logistic regression analysis to explore whether the gene mutation group had specific manifestations.@*RESULTS@#EGFR gene mutation group was common in women (OR=2.041, P=0.001), with more pleural traction sign (OR=1.506, P=0.042), and had little correlation with lymph node metastasis and VPI (P>0.05). Among them, exon 21 subtype was more common in older (OR=1.022, P=0.036), women (OR=2.010, P=0.007), and was associated with larger tumor diameter (OR=1.360, P=0.039) and pleural traction sign (OR=1.754, P=0.029). Exon 19 subtype was common in women (OR=2.230, P=0.009), with a high proportion of solid components (OR=1.589, P=0.047) and more lobulation sign (OR=2.762, P=0.026). ALK gene mutations were likely to occur in younger patients (OR=2.950, P=0.045), with somking history (OR=1.070, P=0.002), and there were more micropapillary components (OR=4.184, P=0.019) and VPI (OR=2.986, P=0.034) in pathology.@*CONCLUSIONS@#The EGFR and ALK genes mutated adenocarcinomas have specific imaging and clinicopathological features, and the mutations in exon 19 or exon 21 subtype have different imaging features, which is of great significance in guiding the clinical diagnosis and treatment of pulmonary nodules.


Subject(s)
Adenocarcinoma of Lung/genetics , Aged , Anaplastic Lymphoma Kinase/genetics , ErbB Receptors/genetics , Female , Genes, erbB-1 , Humans , Lung Neoplasms/pathology , Mutation , Tomography, X-Ray Computed/methods
5.
Article in Chinese | WPRIM | ID: wpr-941013

ABSTRACT

OBJECTIVE@#To build a helical CT projection data restoration model at random low-dose levels.@*METHODS@#We used a noise estimation module to achieve noise estimation and obtained a low-dose projection noise variance map, which was used to guide projection data recovery by the projection data restoration module. A filtering back-projection algorithm (FBP) was finally used to reconstruct the images. The 3D wavelet group residual dense network (3DWGRDN) was adopted to build the network architecture of the noise estimation and projection data restoration module using asymmetric loss and total variational regularization. For validation of the model, 1/10 and 1/15 of normal dose helical CT images were restored using the proposed model and 3 other restoration models (IRLNet, REDCNN and MWResNet), and the results were visually and quantitatively compared.@*RESULTS@#Quantitative comparisons of the restored images showed that the proposed helical CT projection data restoration model increased the structural similarity index by 5.79% to 17.46% compared with the other restoration algorithms (P < 0.05). The image quality scores of the proposed method rated by clinical radiologists ranged from 7.19% to 17.38%, significantly higher than the other restoration algorithms (P < 0.05).@*CONCLUSION@#The proposed method can effectively suppress noises and reduce artifacts in the projection data at different low-dose levels while preserving the integrity of the edges and fine details of the reconstructed CT images.


Subject(s)
Algorithms , Artifacts , Tomography, Spiral Computed , Tomography, X-Ray Computed/methods
6.
Article in Chinese | WPRIM | ID: wpr-941011

ABSTRACT

OBJECTIVE@#To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction.@*METHODS@#A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods.@*RESULTS@#For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001).@*CONCLUSION@#The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.


Subject(s)
Algorithms , Artifacts , Image Processing, Computer-Assisted/methods , Metals , Phantoms, Imaging , Tomography, X-Ray Computed/methods
7.
Chinese Journal of Oncology ; (12): 767-775, 2022.
Article in Chinese | WPRIM | ID: wpr-940937

ABSTRACT

Objective: To investigate the value of predicting the degree of differentiation of pulmonary invasive adenocarcinoma (IAC) based on CT image radiomics model and the expression difference of immunohistochemical factors between different degrees of differentiation of lesions. Methods: The clinicopathological data of patients with pulmonary IAC confirmed by surgical pathology in the Affiliated Huai'an First People's Hospital to Nanjing Medical University from December 2017 to September 2018 were collected. High-throughput feature acquisition was performed for all outlined regions of interest, and prediction models were constructed after dimensionality reduction by the minimum absolute shrinkage operator. Receiver operating characteristic curve was used to assess the predictive efficacy of clinical characteristic model, radiomics model and individualized prediction model combined with both to identify the degree of pulmonary IAC differentiation, and immunohistochemical expressions of Ki-67, NapsinA and TTF-1 were compared between groups with different degrees of IAC differentiation using rank sum test. Results: A total of 396 high-throughput features were extracted from all IAC lesions, and 10 features with high generalization ability and correlation with the degree of IAC differentiation were screened. The mean radiomics score of poorly differentiated IAC in the training group (1.206) was higher than that of patients with high and medium differentiation (0.969, P=0.001), and the mean radiomics score of poorly differentiated IAC in the test group (1.545) was higher than that of patients with high and medium differentiation (-0.815, P<0.001). The differences in gender (P<0.001), pleural stretch sign (P=0.005), and burr sign (P=0.033) were statistically significant between patients in the well and poorly differentiated IAC groups. Multifactorial logistic regression analysis showed that gender and pleural stretch sign were related to the degree of IAC differentiation (P<0.05). The clinical feature model consisted of age, gender, pleural stretch sign, burr sign, tumor vessel sign, and vacuolar sign, and the individualized prediction model consisted of gender, pleural stretch sign, and radiomic score, and was represented by a nomogram. The Akaike information standard values of the radiomics model, clinical feature model and individualized prediction model were 54.756, 82.214 and 53.282, respectively. The individualized prediction model was most effective in identifying the degree of differentiation of pulmonary IAC, and the area under the curves (AUC) of the individualized prediction model in the training group and the test group were 0.92 (95% CI: 0.86-0.99) and 0.88 (95% CI: 0.74-1.00, respectively). The AUCs of the radiomics group model for predicting the degree of differentiation of pulmonary IAC in the training group and the test group were 0.91 (95% CI: 0.83-0.98) and 0.87 (95% CI: 0.72-1.00), respectively. The AUCs of the clinical characteristics model for predicting the degree of differentiation of pulmonary IACs in the training and test groups were 0.75 (95% CI: 0.63-0.86) and 0.76 (95% CI: 0.59-0.94), respectively. The expression level of Ki-67 in poorly differentiated IAC was higher than that in well-differentiated IAC (P<0.001). The expression levels of NapsinA, TTF-1 in poorly differentiated IAC were higher than those in well-differentiated IAC (P<0.05). Conclusions: Individualized prediction model consisted of gender, pleural stretch sign and radiomics score can discriminate the differentiation degree of IAC with the best performance in comparison with clinical feature model and radiomics model. Ki-67, NapsinA and TTF-1 express differently in different degrees of differentiation of IAC.


Subject(s)
Adenocarcinoma of Lung/pathology , Humans , Ki-67 Antigen , Lung Neoplasms/pathology , Retrospective Studies , Tomography, X-Ray Computed/methods
8.
Chinese Journal of Oncology ; (12): 555-561, 2022.
Article in Chinese | WPRIM | ID: wpr-940922

ABSTRACT

Objective: Solid and micropapillary pattern are highly invasive histologic subtypes in lung adenocarcinoma and are associated with poor prognosis while the biopsy sample is not enough for the accurate histological diagnosis. This study aims to assess the correlation and predictive efficacy between metabolic parameters in (18)F-fluorodeoxy glucose positron emission tomography/computed tomography ((18)F-FDG PET-CT), including the maximum SUV (SUV(max)), metabolic tumor volume (MTV), total lesion glycolysis (TLG) and solid and micropapillary histological subtypes in lung adenocarcinoma. Methods: A total of 145 resected lung adenocarcinomas were included. The clinical data and preoperative (18)F-FDG PET-CT data were retrospectively analyzed. Mann-Whitney U test was used for the comparison of the metabolic parameters between solid and micropapillary subtype group and other subtypes group. Receiver operating characteristic (ROC) curve and areas under curve (AUC) were used for evaluating the prediction efficacy of metabolic parameters for solid or micropapillary patterns. Univariate and multivariate analyses were conducted to determine the prediction factors of the presence of solid or micropapillary subtypes. Results: Median SUV(max) and TLG in solid and papillary predominant subtypes group (15.07 and 34.98, respectively) were significantly higher than those in other subtypes predominant group (6.03 and 10.16, respectively, P<0.05). ROC curve revealed that SUV(max) and TLG had good efficacy for prediction of solid and micropapillary predominant subtypes [AUC=0.811(95% CI: 0.715~0.907) and 0.725(95% CI: 0.610~0.840), P<0.05]. Median SUV(max) and TLG in lung adenocarcinoma with the solid or micropapillary patterns (11.58 and 22.81, respectively) were significantly higher than those in tumors without solid and micropapillary patterns (4.27 and 6.33, respectively, P<0.05). ROC curve revealed that SUV(max) and TLG had good efficacy for predicting the presence of solid or micropapillary patterns [AUC=0.757(95% CI: 0.679~0.834) and 0.681(95% CI: 0.595~0.768), P<0.005]. Multivariate logistic analysis showed that the clinical stage (Stage Ⅲ-Ⅳ), SUV(max) ≥10.27 and TLG≥7.12 were the independent predictive factors of the presence of solid or micropapillary patterns (P<0.05). Conclusions: Preoperative SUV(max) and TLG of lung adenocarcinoma have good prediction efficacy for the presence of solid or micropapillary patterns, especially for the solid and micropapillary predominant subtypes and are independent factors of the presence of solid or micropapillary patterns.


Subject(s)
Adenocarcinoma of Lung/diagnostic imaging , Fluorodeoxyglucose F18/metabolism , Humans , Lung Neoplasms/pathology , Multimodal Imaging/methods , Positron Emission Tomography Computed Tomography , Positron-Emission Tomography/methods , Prognosis , Radiopharmaceuticals , Retrospective Studies , Tomography, X-Ray Computed/methods , Tumor Burden
9.
Article in Chinese | WPRIM | ID: wpr-939612

ABSTRACT

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.


Subject(s)
Humans , Lung/pathology , Lung Neoplasms/pathology , Neural Networks, Computer , Tomography, X-Ray Computed/methods
10.
Article in Chinese | WPRIM | ID: wpr-939611

ABSTRACT

Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.


Subject(s)
Algorithms , China , Disease Progression , Humans , Multiple Pulmonary Nodules , Neural Networks, Computer , Tomography, X-Ray Computed/methods
11.
Article in Chinese | WPRIM | ID: wpr-935732

ABSTRACT

Objective: To analyze the radiological characteristics of chest high-resolution computed tomography (HRCT) of patients with asbestosis, and to investigate the signs of predicting the disease progression of asbestosis. Methods: A prospective method was used to enroll 68 patients with asbestosis who were regularly followed up from 2013 to 2016. The radiological characteristics of patients with asbestosis were described by the International Classification of HRCT for Occupational and Environmental Respiratory Diseases (ICOERD) , and the differences between patients with and without progression were compared during the observation period. The Cox proportional hazards regression model was used to analyze the chest HRCT radiological signs predicting the progression of asbestosis. Results: The study included 68 patients with asbestosis aged (65.5±7.8) years old, of which 64.7% (44/68) were female, 29.4% (20/68) had a history of smoking. There was no significant difference in age, sex, smoking and asbestos exposure between patients with progressive asbestosis (20.6%, 14/68) and patients without progressive asbestosis (79.4%, 54/68) (P>0.05) . Chest HRCT of patients with asbestosis showed irregular and/or linear opacities, of which 5.9% (4/68) were accompanied by honeycombing. Irregular and/or linear opacities were mainly lower lung preponderant, often accompanied with ground glass opacity and mosaic perfusion. 98.5% (67/68) had pleural abnormalities, of which 39.7% (27/68) had diffuse pleural thickening with parenchymal bands and/or rounded atelectasis. The analysis of multivariable Cox proportional hazard regression showed that the risk of the progression of asbestosis was increased with higher irregular and/or linears opacities cores (HR=1.184, 95%CI: 1.012-1.384, P=0.034) and the appearance of honeycombing (HR=6.488, 95%CI: 1.447-29.097, P=0.015) . Conclusion: The irregular and/or linear opacities scores and honeycombing on chest HRCT are independent influencing factors for predicting the disease progression of asbestosis.


Subject(s)
Aged , Asbestos/adverse effects , Asbestosis/diagnostic imaging , Female , Humans , Lung , Middle Aged , Pleural Diseases/chemically induced , Tomography, X-Ray Computed/methods
12.
Article in English | WPRIM | ID: wpr-929028

ABSTRACT

OBJECTIVES@#Low dose computed tomography (LDCT) is the best method for early diagnosis of lung cancer. Even though it has been widely used in clinic, the selection of screening objects and the management scheme of pulmonary nodules are still not unified among research institutions. This study aims to evaluate the effect of LDCT in detection effect and follow-up process for pulmonary nodules in asymptomatic participants.@*METHODS@#A total of 1 600 asymptomatic participants (37 to 82 years old), who came from Yantian District People's Hospital, Southern University of Science and Technology, received LDCT. The lung nodules were categorized into positive nodules and semi-positive nodules, and according to the density of positive nodules they were categorized into 4 types: solid nodules (SN), partial solid nodules (pSN), pure ground glass nodules (pGGN), and pleural nodules (PN). The number, detection rate, imaging findings, follow-up change of lung nodules, and the postoperative pathological results of positive nodules were recorded and analyzed.@*RESULTS@#Lung nodules were found in 221 cases by LDCT. The total detection rate of lung nodule was 13.8% (221/1 600), and the detection rate in positive nodules was 4.9% (79/1 600). The detected nodules were mainly single (173 cases), solid (133 cases) and semi-positive nodules (142 cases). Most of nodules (177 cases) had no change in the follow-up process. The enlargement and/or increased density of nodules (5 cases) were lung cancer. Pathological results were obtained in 10 cases, 8 cases were malignant (1 small cell lung cancer and 7 adenocarcinomas), 2 cases were benign (cryptococcal infection and alveolar epithelial dysplasia). The detection rate of lung cancer was 0.5% (8/1 600), and the proportion of early lung cancer was 75% (6/8).@*CONCLUSIONS@#LDCT screening can identify and increase the detection rate in the early lung cancer, which is an effective screening method. It is safe and feasible to take regular follow-up and re-examination for nodules with diameter less than 5 mm. When the size and or density of nodule increases, it indicates the malignant prognosis of the nodule and timely clinical intervention is needed.


Subject(s)
Adenocarcinoma , Adult , Aged , Aged, 80 and over , Early Detection of Cancer/methods , Humans , Lung Neoplasms/pathology , Mass Screening/methods , Middle Aged , Tomography, X-Ray Computed/methods
13.
In. Soeiro, Alexandre de Matos; Leal, Tatiana de Carvalho Andreucci Torres; Accorsi, Tarso Augusto Duenhas; Gualandro, Danielle Menosi; Oliveira Junior, Múcio Tavares de; Caramelli, Bruno; Kalil Filho, Roberto. Manual da residência em cardiologia / Manual residence in cardiology. Santana de Parnaíba, Manole, 2 ed; 2022. p.788-792, tab.
Monography in Portuguese | LILACS | ID: biblio-1353341
18.
Rev. bras. oftalmol ; 81: e0042, 2022. graf
Article in English | LILACS | ID: biblio-1387970

ABSTRACT

ABSTRACT Introduction: The use of tridimensional (3D) printing in healthcare has contributed to the development of instruments and implants. The 3D printing has also been used for teaching future professionals. In order to have a good 3D printed piece, it is necessary to have high quality images, such as the ones from Computerized Tomography (CT scan) exam, which shows the anatomy from different cuts and allows for a good image reconstruction. Purpose: To propose a protocol for creating digital files from computerized tomography images to be printed in 3D and used as didactic material in the ophthalmology field, using open-source software, InVesalius®, Blender® and Repetier-Host©. Methods: Two orbit CT scan exam images in the DICOM format were used to create the virtual file to be printed in 3D. To edit the images, the software InVesalius® (Version 3.1.1) was used to delimit and clean the structure of interest, and also to convert to STL format. The software Blender® (Version 2.80) was used to refine the image. The STL image was then sent to the Repetier-Host© (Version 2.1.3) software, which splits the image in layers and generates the instructions to print the piece in the 3D printer using the polymer polylactic acid (PLA). Results: The printed anatomical pieces printed reproduced most structures, both bone and soft structures, satisfactorily. However, there were some problems during printing, such as the loss of small bone structures, that are naturally surrounded by muscles due to the lack of support. Conclusion: Despite the difficulties faced during the production of the pieces, it was also possible to reproduce the anatomical structures adequately, which indicates that this protocol of 3D printing from medical images is viable.


RESUMO Introdução: O uso de impressão em 3-D na área da saúde tem contribuído para o desenvolvimento de instrumentos e próteses. A impressão 3-D tem sido usada para o ensino de futuros profissionais. Para se alcançar uma boa peça em 3-D, é necessário ter imagens de alta qualidade, como aquelas geradas pelo exame de Tomografia Computadorizada (TC), que mostra a anatomia sob diferentes cortes e permite uma boa reconstrução de imagem. Objetivo: Propor um protocolo para a criação de arquivos digitais a partir de imagens de tomografia computadorizada a serem impressas em 3-D e usadas como modelo de material didático oftalmológico usando software de código aberto, InVesalius®, Bender® e Repetier-Host©. Métodos: Foram utilizadas imagens em formato DICOM provenientes de dois exames de tomografia computadorizada de órbitas para a impressão tridimensional. Para manuseio das imagens, foram utilizados o InVesalius®, versão 3.1.1, para delimitar e limpar a estrutura de interesse e também para converter em formato STL. O Blender®, versão 2.80 foi usado para refinamento. A imagem em STL foi então enviada para o programa Repetier-Host, versão 2.1.3, que divide a imagem em camadas e gera as instruções para impressão da peça em ácido polilático na impressora tridimensional. Resultados: As peças anatômicas impressas reproduziram de forma satisfatória a maioria das estruturas ósseas e musculares. No entanto, houve dificuldade durante a impressão das estruturas ósseas menores, como perda de estrutura óssea pequena, que não possuíam sustentação, por serem envoltas pelo músculo. Conclusão: Apesar das dificuldades encontradas na produção dessas peças de estudo, foi possível reproduzir estruturas com fidelidade, indicando que o protocolo proposto viabiliza a impressão de imagens oriundas da tomografia computadorizada para impressão tridimensional.


Subject(s)
Humans , Ophthalmology/education , Orbit/anatomy & histology , Orbit/diagnostic imaging , Tomography, X-Ray Computed/methods , Imaging, Three-Dimensional/instrumentation , Printing, Three-Dimensional/instrumentation , Students, Medical , Teaching , Software , Education, Medical/methods , Anatomy/education , Models, Anatomic
19.
Chinese Journal of Traumatology ; (6): 170-176, 2022.
Article in English | WPRIM | ID: wpr-928495

ABSTRACT

PROPOSE@#In this study, we re-assessed the criteria defined by the radiological society of North America (RSNA) to determine novel radiological findings helping the physicians differentiating COVID-19 from pulmonary contusion.@*METHODS@#All trauma patients with blunt chest wall trauma and subsequent pulmonary contusion, COVID-19-related signs and symptoms before the trauma were enrolled in this retrospective study from February to May 2020. Included patients (Group P) were then classified into two groups based on polymerase chain reaction tests (Group Pa for positive patients and Pb for negative ones). Moreover, 44 patients from the pre-pandemic period (Group PP) were enrolled. They were matched to Group P regarding age, sex, and trauma-related scores. Two radiologists blindly reviewed the CT images of all enrolled patients according to criteria defined by the RSNA criteria. The radiological findings were compared between Group P and Group PP; statistically significant ones were re-evaluated between Group Pa and Group Pb thereafter. Finally, the sensitivity and specificity of each significant findings were calculated. The Chi-square test was used to compare the radiological findings between Group P and Group PP.@*RESULTS@#In the Group PP, 73.7% of all ground-glass opacities (GGOs) and 80% of all multiple bilateral GGOs were detected (p < 0.001 and p = 0.25, respectively). Single bilateral GGOs were only seen among the Group PP. The Chi-square tests showed that the prevalence of diffused GGOs, multiple unilateral GGOs, multiple consolidations, and multiple bilateral consolidations were significantly higher in the Group P (p = 0.001, 0.01, 0.003, and 0.003, respectively). However, GGOs with irregular borders and single consolidations were more significant among the Group PP (p = 0.01 and 0.003, respectively). Of note, reticular distortions and subpleural spares were exclusively detected in the Group PP.@*CONCLUSION@#We concluded that the criteria set by RSNA for the diagnosis of COVID-19 are not appropriate in trauma patients. The clinical signs and symptoms are not always useful either. The presence of multiple unilateral GGOs, diffused GGOs, and multiple bilateral consolidations favor COVID-19 with 88%, 97.62%, and 77.7% diagnostic accuracy.


Subject(s)
COVID-19 , Contusions/diagnostic imaging , Humans , Lead , Lung/diagnostic imaging , Lung Injury/etiology , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
20.
Article in Chinese | WPRIM | ID: wpr-928228

ABSTRACT

Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.


Subject(s)
Algorithms , Humans , Lung Neoplasms/diagnostic imaging , Neural Networks, Computer , Radiographic Image Interpretation, Computer-Assisted/methods , Tomography, X-Ray Computed/methods
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