Tumor-node-metastasis (TNM) classification for lung cancer is essential for appropriate treatment strategies and has been used widely in the investigation and treatment of this cancer. In TNM classification, N descriptors are one of the most important prognostic indicators and are determined by the metastatic lymph node stations. Therefore, accurate classification of lymph nodes is crucial. Thoracic contrast-enhanced Computed Tomography (CT) images represent the gold-standard modality. However, manual segmentation and classification of lymph nodes are challenges that arise from the relatively similar attenuation between lymph nodes and surrounding structures. Recent progress of convolutional neural network (CNN) has spawned research on mediastinal lymph nodes segmentation on chest CT images using CNNs. However, the previous CNN-based method did not consider the relationship between airways and lymph node locations for segmenting the thoracic N1 lymph nodes group. In this study, we investigate whether distance maps based on tracheobronchial labeling can represent the anatomy properties of the N1 lymph nodes group in volumetric CT images using the NIH open-source dataset.
Noninvasive biomarkers that capture the adenocarcinoma aggressiveness could provide crucial quantitative information for precision medicine to aid clinical decision making. Texture features are known to measure tumor heterogeneity and have been identified as the features having a potential correlation to outcomes in lung adenocarcinomas. Nevertheless, current methods for analyzing texture patterns that arise from local intensity variation are limited to reveal a spatial configuration of the texture structures in 3D thoracic CT images. This lack of an intuitive visualization of the texture of nodules makes understanding the meanings underlying the tumor heterogeneity a challenging problem. In this study, we propose an approach combining a structure-texture image decomposition with a topological data analysis to represent a spatial configuration of the texture of lung adenocarcinoma. The image decomposition aims to split the 3D thoracic CT image into two components, namely, the structure component with the piecewise-smooth part having the global structural information of nodule and the texture component with the locally-patterned oscillating part. We demonstrate the use of topological data analysis to capture architectural features that arise from the texture component. Specifically, using persistent homology of texture components, we compute topological representations of lung adenocarcinomas with the appearance of consolidation on CT images. Applying the method to an example of early-stage lung adenocarcinomas graded with texture features based on the popular algorithm such as gray-level co-occurrence matrix (GLCM), we present that the structure-texture image decomposition model with topological data analysis might be a promising tool in analyzing the tumor heterogeneity in 3D thoracic CT images.
Lung cancer CT screening has been carried out. Unnecessary biopsy is performed in 20-55% of cancer candidate cases. Several malignant risk models have been published to reduce the false positive rate of lung cancer. In this study, we develop a high-performance malignant risk model. This risk model consists of Generalized Additive Model (GAM) using diameter, pleural attachment area rate, CT kurtosis, GLCM_Inertia, GLCM_IDM and GLCM_Energy_in_marginal_region. This model shows effectiveness by showing AUC 0.918 compared to the current Pancan model.
Lung adenocarcinomas are the most prevalent subtype of non-small cell lung cancers which are found as the most common true-positive finding in a lung cancer screening population. The ability to preoperatively identify patients with a high rate of relapse becomes crucial to guide treatment decisions and to develop risk-adapted treatment strategies. Considerable research efforts have been performed to enable the stratification of adenocarcinoma aggressiveness based on preoperative CT image analyses for optimal therapeutic management to maximize patient survival and preserve lung function. It is currently a major focus to quantitatively evaluate adenocarcinoma aggressiveness according to computerextracted imaging features (radiomics) in three-dimensional (3D) thoracic CT images. Texture features are known to measure tumor heterogeneity and have been identified as the features having a potential correlation to outcomes in lung cancer. Nevertheless, a spatial configuration of texture caused by the tumor heterogeneity remains elusive. In this study, we present a visualization method to reveal a spatial configuration of the texture of pulmonary nodules in 3D thoracic CT images through a structure-texture image decomposition. Applying the method to an example of early-stage lung adenocarcinomas graded with texture features based on the popular algorithm such as gray-level co-occurrence matrix (GLCM), we present that the preliminary results reveal the presence of intensity structure caused by tumor heterogeneity.
Screening for lung cancer with low-dose computed tomography (CT) has led to increased recognition of small lung cancers and is expected to increase the rate of detection of early-stage lung cancer. Major concerns in the implementation of the CT screening of large populations include determining the appropriate management of pulmonary nodules found on a scan. The identification of patients with early-stage lung cancer who have a higher risk for relapse and who require more aggressive surveillance has been a target of the intense investigation. This study was performed to investigate whether the computer-aided CT image features could improve the discrimination ability of lung cancer prediction models for nodules in whom malignancy was suspected.
Screening for lung cancer with low-dose computed tomography (CT) has led to increased recognition of small lung cancers and is expected to increase the rate of detection of early-stage lung cancer. Major concerns in the implementation of the CT screening of large populations include determining the appropriate management of pulmonary nodules found on a scan. The identification of patients with early-stage lung cancer who have a higher risk for relapse and who require more aggressive surveillance has been a target of intense investigation. This study was performed to investigate whether image features of internal intensity in combination with surrounding structure characteristics are associated with an increased risk of relapse in patients with stage IA lung adenocarcinoma. We focused on pleural attachment status which is one of morphological characteristics associated with prognosis in three-dimensional thoracic CT images.
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