Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping.

A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored for interpreting localization and classification tasks while they ignored fine-grained features. Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN. In this paper, we first develop a soft activation mapping (SAM) to enable fine-grained lung nodule shape & margin (LNSM) feature analysis with a CNN so that it can access rich discrete features. Secondly, by combining high-level convolutional features with SAM, we further propose a high-level feature enhancement scheme (HESAM) to localize LNSM features. Experiments on the LIDC-IDRI dataset indicate that 1) SAM captures more fine-grained and discrete attention regions than existing methods, 2) HESAM localizes more accurately on LNSM features and obtains the state-of-the-art predictive performance, reducing the false positive rate, and 3) we design and conduct a visually matching experiment which incorporates radiologists study to increase the confidence level of applying our method to clinical diagnosis.

Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks.

PURPOSE: Early detection and treatment of lung cancer holds great importance. However, pulmonary-nodule classification using CT images alone is difficult to realize. To address this concern, a method for pulmonary-nodule classification based on a deep convolutional neural network (DCNN) and generative adversarial networks (GAN) has previously been proposed by the authors. In that method, the said classification was performed exclusively using axial cross sections of pulmonary nodules. During actual medical-examination procedures, however, a comprehensive judgment can only be made via observation of various pulmonary-nodule cross sections. In the present study, a comprehensive analysis was performed by extending the application of the previously proposed DCNN- and GAN-based automatic classification method to multiple cross sections of pulmonary nodules. METHODS: Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. Firstly, multiplanar images of the pulmonary nodule are generated. Classification training was performed for three DCNNs. A certain pretraining was initially performed using GAN-generated nodule images. This was followed by fine-tuning of each pretrained DCNN using original nodule images provided as input. RESULTS: As a result of the evaluation, the specificity was 77.8% and the sensitivity was 93.9%. Additionally, the specificity was observed to have improved by 11.1% without any reduction in the sensitivity, compared to our previous report. CONCLUSION: This study reports development of a comprehensive analysis method to classify pulmonary nodules at multiple sections using GAN and DCNN. The effectiveness of the proposed discrimination method based on use of multiplanar images has been demonstrated to be improved compared to that realized in a previous study reported by the authors. In addition, the possibility of enhancing classification accuracy via application of GAN-generated images, instead of data augmentation, for pretraining even for medical datasets that contain relatively few images has also been demonstrated.

Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning.

BACKGROUND: Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images. OBJECTIVE: Aiming to classify malignant and benign pulmonary nodules using CT images, we explore different strategies to utilize the state-of-the-art deep convolutional neural networks (CNN). METHODS: Experiments are conducted using the Lung Image Database Consortium image collection (LIDC-IDRI), which is a public database containing 1018 cases. Three strategies are implemented including to 1) modify some state-of-the-art CNN architectures, 2) integrate different CNNs and 3) adopt transfer learning. Totally, 11 deep CNN models are compared using the same dataset. RESULTS: Study demonstrates that, for the model modification scheme, a concise CifarNet performs better than the other modified CNNs with more complex architectures, achieving an area under ROC curve of AUC = 0.90. Integrated CNN models do not significantly improve the classification performance, but the model complexity is reduced. Transfer learning outperforms the other two schemes and ResNet with fine-tuning leads to the best performance with an AUC = 0.94, as well as the sensitivity of 91% and an overall accuracy of 88%. CONCLUSIONS: Model modification, model integration, and transfer learning can play important roles to identify and generate optimal deep CNN models in classifying pulmonary nodules based on CT images efficiently. Transfer learning is preferred when applying deep learning to medical imaging applications.

Classification of benign and malignant lung nodules from CT images based on hybrid features.

The classification of benign and malignant lung nodules has great significance for the early detection of lung cancer, since early diagnosis of nodules can greatly increase patient survival. In this paper, we propose a novel classification method for lung nodules based on hybrid features from computed tomography (CT) images. The method fused 3D deep dual path network (DPN) features, local binary pattern (LBP)-based texture features and histogram of oriented gradients (HOG)-based shape features to characterize lung nodules. DPN is a convolutional neural network which integrates the advantages of aggregated residual transformations (ResNeXt) for feature reuse and a densely convolutional network (DenseNet) for exploring new features. LBP is a prominent feature descriptor for texture classification, when combining with the HOG descriptor, it can improve the classification performance considerably. To differentiate malignant nodules from benign ones, a gradient boosting machine (GBM) algorithm is employed. We evaluated the proposed method on the publicly available LUng Nodule Analysis 2016 (LUNA16) dataset with 1004 nodules, achieving an area under the receiver operating characteristic curve (AUC) of 0.9687 and accuracy of 93.78%. The promising results demonstrate that our method has strong robustness on the classification of nodule patterns by virtue of the joint use of texture features, shape features and 3D deep DPN features. The method has the potential to help radiologists to interpret diagnostic data and make decisions in clinical practice.

Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.

Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans. Our method uses a novel 15-layer 2D deep convolutional neural network architecture for automatic feature extraction and classification of pulmonary candidates as nodule or nonnodule. Focal loss function is then applied to the training process to boost classification accuracy of the model. We evaluated our method on the LIDC/IDRI dataset extracted by the LUNA16 challenge. The experiments showed that our deep learning method with focal loss is a high-quality classifier with an accuracy of 97.2%, sensitivity of 96.0%, and specificity of 97.3%.

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.

The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous models that proposed computationally intensive deep ensemble models or three-dimensional CNN models, we propose a lightweight, multiple view sampling based multi-section CNN architecture. The model obtains a nodule's cross sections from multiple view angles and encodes the nodule's volumetric information into a compact representation by aggregating information from its different cross sections via a view pooling layer. The compact feature is subsequently used for the task of nodule classification. The method does not require the nodule's spatial annotation and works directly on the cross sections generated from volume enclosing the nodule. We evaluated the proposed method on lung image database consortium (LIDC) and image database resource initiative (IDRI) dataset. It achieved the state-of-the-art performance with a mean 93.18% classification accuracy. The architecture could also be used to select the representative cross sections determining the nodule's malignancy that facilitates in the interpretation of results. Because of being lightweight, the model could be ported to mobile devices, which brings the power of artificial intelligence (AI) driven application directly into the practitioner's hand.

Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification.

Existing deep-learning-based pulmonary nodule classification models usually use images and benign-malignant labels as inputs for training. Image attributes of the nodules, as human-nameable high-level semantic labels, are rarely used to build a convolutional neural network (CNN). In this paper, a new method is proposed to combine the advantages of two classifications, which are pulmonary nodule benign-malignant classification and pulmonary nodule image attributes classification, into a deep learning network to improve the accuracy of pulmonary nodule classification. For this purpose, a unique 3D CNN is built to learn image attribute and benign-malignant classification simultaneously. A novel loss function is designed to balance the influence of two different kinds of classifications. The CNN is trained by a publicly available lung image database consortium (LIDC) dataset and is tested by a cross-validation method to predict the risk of a pulmonary nodule being malignant. This proposed method generates the accuracy of 91.47%, which is better than many existing models. Experimental findings show that if the CNN is built properly, the nodule attributes classification and benign-malignant classification can benefit from each other. By using nodule attribute learning as a control factor in a deep learning scheme, the accuracy of pulmonary nodule classification can be significantly improved by using a deep learning scheme.

Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.

BACKGROUND AND OBJECTIVE: In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive reduction, such false positives are reliably reduced. Note that this task is challenging due to 1) the imbalance between the numbers of nodules and non-nodules and 2) the intra-class diversity of non-nodules. Although techniques using 3D convolutional neural networks (CNNs) have shown promising performance, they suffer from high computational complexity which hinders constructing deep networks. To efficiently address these problems, we propose a novel framework using the ensemble of 2D CNNs using single views, which outperforms existing 3D CNN-based methods. METHODS: Our ensemble of 2D CNNs utilizes single-view 2D patches to improve both computational and memory efficiency compared to previous techniques exploiting 3D CNNs. We first categorize non-nodules on the basis of features encoded by an autoencoder. Then, all 2D CNNs are trained by using the same nodule samples, but with different types of non-nodules. By extending the learning capability, this training scheme resolves difficulties of extracting representative features from non-nodules with large appearance variations. Note that, instead of manual categorization requiring the heavy workload of radiologists, we propose to automatically categorize non-nodules based on the autoencoder and k-means clustering. RESULTS: We performed extensive experiments to validate the effectiveness of our framework based on the database of the lung nodule analysis 2016 challenge. The superiority of our framework is demonstrated through comparing the performance of five frameworks trained with differently constructed training sets. Our proposed framework achieved state-of-the-art performance (0.922 of the competition performance metric score) with low computational demands (789K of parameters and 1024M of floating point operations per second). CONCLUSION: We presented a novel false positive reduction framework, the ensemble of single-view 2D CNNs with fully automatic non-nodule categorization, for pulmonary nodule detection. Unlike previous 3D CNN-based frameworks, we utilized 2D CNNs using 2D single views to improve computational efficiency. Also, our training scheme using categorized non-nodules, extends the learning capability of representative features of different non-nodules. Our framework achieved state-of-the-art performance with low computational complexity.

Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature.

BACKGROUND AND OBJECTIVES: To improve lung nodule classification efficiency, we propose a lung nodule CT image characterization method. We propose a multi-directional feature extraction method to effectively represent nodules of different risk levels. The proposed feature combined with pattern recognition model to classify lung adenocarcinomas risk to four categories: Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA). METHODS: First, we constructed the reference map using an integral image and labelled this map using a K-means approach. The density distribution map of the lung nodule image was generated after scanning all pixels in the nodule image. An exponential function was designed to weight the angular histogram for each component of the distribution map, and the features of the image were described. Then, quantitative measurement was performed using a Random Forest classifier. The evaluation data were obtained from the LIDC-IDRI database and the CT database which provided by Shanghai Zhongshan hospital (ZSDB). In the LIDC-IDRI, the nodules are categorized into three configurations with five ranks of malignancy ("1" to "5"). In the ZSDB, the nodule categories are AAH, AIS, MIA, and IA. RESULTS: The average of Student's t-test p-values were less than 0.02. The AUCs for the LIDC-IDRI database were 0.9568, 0.9320, and 0.8288 for Configurations 1, 2, and 3, respectively. The AUCs for the ZSDB were 0.9771, 0.9917, 0.9590, and 0.9971 for AAH, AIS, MIA and IA, respectively. CONCLUSION: The experimental results demonstrate that the proposed method outperforms the state-of-the-art and is robust for different lung CT image datasets.

Agile convolutional neural network for pulmonary nodule classification using CT images.

OBJECTIVE: To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images. METHODS: A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally. RESULTS: After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to [Formula: see text], the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used. CONCLUSIONS: This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

Feature fusion for lung nodule classification.

PURPOSE: This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning. METHODS: Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized to provide feature descriptors of malignant and benign nodules and non-nodule regions of interest. Support vector machines (SVMs) and k-nearest neighbor (kNN) classifiers in serial and two-tier cascade frameworks are optimized and analyzed for optimal classification results of nodules. RESULTS: A total of 1191 nodule and non-nodule samples from the Lung Image Data Consortium database is used for analysis. Classification using SVM and kNN classifiers is examined. The classification results from the two-tier cascade SVM using Gabor features showed overall better results for identifying non-nodules, malignant and benign nodules with average area under the receiver operating characteristics (AUC-ROC) curves of 0.99 and average f1-score of 0.975 over the two tiers. CONCLUSION: In the results, higher overall AUCs and f1-scores were obtained for the non-nodules cases using any of the three features, showing the greatest distinguishability over nodules (benign/malignant). SVM and kNN classifiers were used for benign, malignant and non-nodule classification, where Gabor proved to be the most effective of the features for classification. The cascaded framework showed the greatest distinguishability between benign and malignant nodules.

Automatic classification of lung nodules on MDCT images with the temporal subtraction technique.

PURPOSE: A temporal subtraction (TS) image is obtained by subtracting a previous image, which is warped to match the structures of the previous image and the related current image. The TS technique removes normal structures and enhances interval changes such as new lesions and substitutes in existing abnormalities from a medical image. However, many artifacts remaining on the TS image can be detected as false positives. METHOD: This paper presents a novel automatic segmentation of lung nodules using the Watershed method, multiscale gradient vector flow snakes and a detection method using the extracted features and classifiers for small lung nodules (20 mm or less). RESULT: Using the proposed method, we conduct an experiment on 30 thoracic multiple-detector computed tomography cases including 31 small lung nodules. CONCLUSION: The experimental results indicate the efficiency of our segmentation method.

Pulmonary nodule classification with deep residual networks.

PURPOSE  : Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules. METHODS: We evaluate the effectiveness of very deep convolutional neural networks at the task of expert-level lung nodule malignancy classification. Using the state-of-the-art ResNet architecture as our basis, we explore the effect of curriculum learning, transfer learning, and varying network depth on the accuracy of malignancy classification. RESULTS: Due to a lack of public datasets with standardized problem definitions and train/test splits, studies in this area tend to not compare directly against other existing work. This makes it hard to know the relative improvement in the new solution. In contrast, we directly compare our system against two state-of-the-art deep learning systems for nodule classification on the LIDC/IDRI dataset using the same experimental setup and data set. The results show that our system achieves the highest performance in terms of all metrics measured including sensitivity, specificity, precision, AUROC, and accuracy. CONCLUSIONS: The proposed method of combining deep residual learning, curriculum learning, and transfer learning translates to high nodule classification accuracy. This reveals a promising new direction for effective pulmonary nodule CAD systems that mirrors the success of recent deep learning advances in other image-based application domains.

Selección de un algoritmo para la clasificación de Nódulos Pulmonares Solitarios / Algorithm selection for classifying Solitary Pulmonary Nodules

En los últimos años la comunidad científica internacional ha dedicado considerables recursos a la investigación y desarrollo de sistemas de diagnóstico asistidos por ordenador, utilizados por los médicos en el proceso de diagnóstico. Se ha prestado especial atención en algunas áreas médicas, como las especialidades oncológicas, por los altos índices de mortalidad provocados por algunas enfermedades como el cáncer de pulmón. El diagnóstico temprano de este padecimiento puede reducir en gran medida estos indicadores y mejorar la calidad de vida de los pacientes. El objetivo que se pretende con el desarrollo de esta investigación, es la selección adecuada de un algoritmo de clasificación, para ser utilizado en la fase que lleva el mismo nombre como parte de un sistema de diagnóstico asistido por ordenador para la clasificación de nódulos pulmonares solitarios. Para la selección adecuada del algoritmo de clasificación, se realiza un experimento utilizando las herramientas Weka v3.7.10 y Matlab 2013. Para determinar cuál de las técnicas estudiadas arroja mejores resultados de rendimiento, se utilizó el mismo conjunto de datos para las fases de entrenamiento, prueba y validación del clasificador, disponible en la base de datos internacional The Lung Image Database Consortium Image Collection(AU)

In recent years the international scientific community has devoted considerable resources to research and development of systems for computer-aided diagnosis used by physicians in the diagnostic process. Special attention has been provided in some medical areas, such as oncology specialties, by high mortality rates caused by some diseases like lung cancer. Early diagnosis of this condition can greatly reduce these indicators and improve quality of life of patients.The objective pursued with the development of this research is the proper selection of a classification algorithm, to be used in the phase that has the same name, as part of a system of computer-aided diagnosis for classification of solitary pulmonary nodules. For the selection of the appropriate classification algorithm, an experiment was performed using the tools Weka v3.7.10 and Matlab 2013. To determine which of the techniques studied produces better performance results, the same data set was used for the phases of training, testing and validation of the classifier, available in the international database The Lung Image Database Consortium Image Collection(AU)

Lung adenocarcinomas: correlation of computed tomography and pathology findings.

Adenocarcinoma is the most common histologic type of lung cancer. Recent lung adenocarcinoma classifications from the International Association for the Study of Lung cancer, the American Thoracic Society and the European Respiratory Society (IASLC/ETS/ERS, 2011) and World Health Organization (WHO, 2015) define a wide range of adenocarcinoma types and subtypes featuring different prognosis and management. This spectrum of lesions translates into various CT presentations and features, which generally show good correlation with histopathology, stressing the key role of the radiologist in the diagnosis and management of those patients. This review aims at helping radiologists to understand the basics of the up-to-date adenocarcinoma pathological classifications, radio-pathological correlations and how to use them in the clinical setting, as well as other imaging-related correlations (radiogenomics, quantitative analysis, PET-CT).

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.

BACKGROUND: Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. METHODS: The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. RESULTS: The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. CONCLUSIONS: The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

Clinical application of CT and CT-guided percutaneous transthoracic needle biopsy in patients with indeterminate pulmonary nodules.

OBJECTIVE: To investigate the clinical application of CT and CT-guided percutaneous transthoracic needle biopsy (CT-PTNB) in patients with indeterminate pulmonary nodules (IPNs). METHODS: We retrospectively studied 113 patients with PNs undergoing CT and CT-PTNB. Variables such as gender, age at diagnosis, smoking status, CT findings, and CT-PTNB techniques were analyzed. Data analysis was performed with the Student's t-test for independent samples the chi-square test, and normal approximation test for comparison of two proportions. RESULTS: Of the 113 patients studied, 68 (60.2%) were male and 78 (69%) were smokers. The diameter of malignant lesions ranged from 2.6 cm to 10.0 cm. Most of the IPNs (85%) were located in the peripheral region. The biopsied IPNs were found to be malignant in 88 patients (77.8%) and benign in 25 (22.2%). Adenocarcinoma was the most common malignant tumor, affecting older patients. The IPN diameter was significantly greater in patients with malignant PNs than in those with benign IPNs (p < 0.001). Having regular contour correlated significantly with an IPN being benign (p = 0.022), whereas spiculated IPNs and bosselated IPNs were more often malignant (in 50.7% and 28.7%, respectively). Homogeneous attenuation and necrosis were more common in patients with malignant lesions (51.9% and 26.9%, respectively) CONCLUSIONS: In our sample, CT and CT-PTNB were useful in distinguishing between malignant and benign IPNs. Advanced age and smoking were significantly associated with malignancy. Certain CT findings related to IPNs (larger diameter, spiculated borders, homogeneous attenuation, and necrosis) were associated with malignancy.

Clinical application of CT and CT-guided percutaneous transthoracic needle biopsy in patients with indeterminate pulmonary nodules / Aplicação clínica da TC e biópsia transtorácica percutânea guiada por TC em pacientes com nódulos pulmonares indeterminados

OBJECTIVE: To investigate the clinical application of CT and CT-guided percutaneous transthoracic needle biopsy (CT-PTNB) in patients with indeterminate pulmonary nodules (IPNs). METHODS: We retrospectively studied 113 patients with PNs undergoing CT and CT-PTNB. Variables such as gender, age at diagnosis, smoking status, CT findings, and CT-PTNB techniques were analyzed. Data analysis was performed with the Student's t-test for independent samples the chi-square test, and normal approximation test for comparison of two proportions. RESULTS: Of the 113 patients studied, 68 (60.2%) were male and 78 (69%) were smokers. The diameter of malignant lesions ranged from 2.6 cm to 10.0 cm. Most of the IPNs (85%) were located in the peripheral region. The biopsied IPNs were found to be malignant in 88 patients (77.8%) and benign in 25 (22.2%). Adenocarcinoma was the most common malignant tumor, affecting older patients. The IPN diameter was significantly greater in patients with malignant PNs than in those with benign IPNs (p < 0.001). Having regular contour correlated significantly with an IPN being benign (p = 0.022), whereas spiculated IPNs and bosselated IPNs were more often malignant (in 50.7% and 28.7%, respectively). Homogeneous attenuation and necrosis were more common in patients with malignant lesions (51.9% and 26.9%, respectively) CONCLUSIONS: In our sample, CT and CT-PTNB were useful in distinguishing between malignant and benign IPNs. Advanced age and smoking were significantly associated with malignancy. Certain CT findings related to IPNs (larger diameter, spiculated borders, homogeneous attenuation, and necrosis) were associated with malignancy. .

OBJETIVO: Investigar a aplicação clínica da TC e da biópsia transtorácica percutânea guiada por TC (BTP-TC) em pacientes com nódulos pulmonares indeterminados (NPIs). MÉTODOS: Foram estudados retrospectivamente 113 pacientes portadores de NPIs submetidos a TC e BTP-TC. Foram analisadas variáveis como sexo, idade ao diagnóstico, tabagismo, achados tomográficos e técnicas de BTP-TC. A análise dos dados foi efetuada por meio do teste t de Student para amostras independentes, teste do qui-quadrado e teste de comparação de duas proporções por aproximação normal. RESULTADOS: Dos 113 pacientes estudados, 68 (60,2%) eram do sexo masculino e 78 (69%) eram tabagistas. O diâmetro das lesões malignas variou de 2,6 a 10,0 cm. A maioria dos NPIs estava localizada na região periférica (85%). O resultado da biópsia foi maligno em 88 pacientes (77,8%) e benigno em 25 (22,2%). O adenocarcinoma foi o tumor maligno mais frequente, acometendo pacientes com idade mais avançada. O diâmetro dos NPIs foi significativamente maior nos pacientes com malignidade (p < 0,001). Houve uma associação significativa entre NPIs com contorno regular e lesões benignas (p = 0,022), enquanto os de tipo espiculado e bocelado foram mais frequentes em pacientes com lesões malignas (50,7% e 28,7%, respectivamente). Atenuação homogênea e necrose foram mais frequentes em pacientes com lesões malignas (51,9% e 26,9%, respectivamente). CONCLUSÕES: A TC e a BTP-TC foram úteis no diagnóstico diferencial entre lesões malignas e benignas nos pacientes com NPIs nesta amostra. Idade mais avançada e tabagismo associaram-se significativamente com malignidade. Houve associações de achados tomográficos (diâmetro maior, contorno espiculado, atenuação homogênea ...