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https://www.readbyqxmd.com/read/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#1
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Xinyang Feng, Jie Yang, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226734/prediction-of-malignant-and-benign-of-lung-tumor-using-a-quantitative-radiomic-method
#2
Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian, Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian, Min Xu, Jiangdian Song, Di Dong, Jie Tian, Jun Wang, Yali Zang, Xia Liu
Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features has the ability to capture intra-tumoural heterogeneity in a non-invasive way. Here we carry out a radiomic analysis of 150 features quantifying lung tumour image intensity, shape and texture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226733/association-between-tumor-heterogeneity-and-progression-free-survival-in-non-small-cell-lung-cancer-patients-with-egfr-mutations-undergoing-tyrosine-kinase-inhibitors-therapy
#3
Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian, Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian, Zaiyi Liu, Yanqi Huang, Yali Zang, Jiangdian Song, Jie Tian, Di Dong
For non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, current staging methods do not accurately predict the risk of disease recurrence after tyrosine kinase inhibitors (TKI) therapy. Developing a noninvasive method to predict whether individual could benefit from TKI therapy has great clinical significance. In this research, a radiomics approach was proposed to determine whether the tumor heterogeneity of NSCLC, which was measured by the texture on computed tomography (CT), could make an independent prediction of progression-free survival (PFS)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28130689/comparison-of-machine-learning-methods-for-classifying-mediastinal-lymph-node-metastasis-of-non-small-cell-lung-cancer-from-18-f-fdg-pet-ct-images
#4
Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu
BACKGROUND: This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from (18)F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network...
December 2017: EJNMMI Research
https://www.readbyqxmd.com/read/28114048/multi-source-transfer-learning-with-convolutional-neural-networks-for-lung-pattern-analysis
#5
Stergios Christodoulidis, Marios Anthimopoulos, Lukas Ebner, Andreas Christe, Stavroula Mougiakakou
Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem...
December 7, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28113928/automatic-scoring-of-multiple-semantic-attributes-with-multi-task-feature-leverage-a-study-on-pulmonary-nodules-in-ct-images
#6
Sihong Chen, Jing Qin, Xing Ji, Baiying Lei, Tianfu Wang, Dong Ni, Jie-Zhi Cheng
The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images...
November 16, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28068363/quantitative-computed-tomography-features-for-predicting-tumor-recurrence-in-patients-with-surgically-resected-adenocarcinoma-of-the-lung
#7
Hyun Jung Koo, Yu Sub Sung, Woo Hyun Shim, Hai Xu, Chang-Min Choi, Hyeong Ryul Kim, Jung Bok Lee, Mi Young Kim
PURPOSE: The purpose of this study was to determine if preoperative quantitative computed tomography (CT) features including texture and histogram analysis measurements are associated with tumor recurrence in patients with surgically resected adenocarcinoma of the lung. METHODS: The study included 194 patients with surgically resected lung adenocarcinoma who underwent preoperative CT between January 2013 and December 2013. Quantitative CT feature analysis of the lung adenocarcinomas were performed using in-house software based on plug-in package for ImageJ...
2017: PloS One
https://www.readbyqxmd.com/read/28048907/su-f-r-46-predicting-distant-failure-in-lung-sbrt-using-multi-objective-radiomics-model
#8
Z Zhou, M Folkert, P Iyengar, Y Zhang, J Wang
PURPOSE: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. METHODS: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048213/mo-de-207a-09-low-dose-ct-image-reconstruction-via-learning-from-different-patient-normal-dose-images
#9
H Han, L Xing, Z Liang
PURPOSE: To investigate a novel low-dose CT (LdCT) image reconstruction strategy for lung CT imaging in radiation therapy. METHODS: The proposed approach consists of four steps: (1) use the traditional filtered back-projection (FBP) method to reconstruct the LdCT image; (2) calculate structure similarity (SSIM) index between the FBP-reconstructed LdCT image and a set of normal-dose CT (NdCT) images, and select the NdCT image with the highest SSIM as the learning source; (3) segment the NdCT source image into lung and outside tissue regions via simple thresholding, and adopt multiple linear regression to learn high-order Markov random field (MRF) pattern for each tissue region in the NdCT source image; (4) segment the FBP-reconstructed LdCT image into lung and outside regions as well, and apply the learnt MRF prior in each tissue region for statistical iterative reconstruction of the LdCT image following the penalized weighted least squares (PWLS) framework...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048179/su-f-r-54-ct-texture-based-early-tumor-treatment-response-assessment-during-radiation-therapy-delivery-small-cell-versus-non-small-cell-lung-cancers
#10
J Paul, E Gore, X Li
PURPOSE: Tumor treatment response may potentially be assessed during radiation therapy (RT) by analyzing changes in CT-textures. We investigated the different early RT-responses between small cell (SCLC) and non-small cell lung cancer (NSCLC) as assessed by CT-texture. METHODS: Daily diagnostic-quality CT acquired during routine CT-guided RT using a CT-on-Rails for 13-NSCLC and 5-SCLC patients were analyzed. These patient had ages ranging from 45-78 and 38-63 years, respectively, for NSCLC and SCLC groups, and tumor-stages ranging from T2-T4, and were treated with either RT or chemotherapy and RT with 45-66Gy/ 20-34 fractions...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048125/su-f-r-31-identification-of-robust-normal-lung-ct-texture-features-for-the-prediction-of-radiation-induced-lung-disease
#11
W Choi, S Riyahi, W Lu
PURPOSE: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume. METHODS: The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres placed at the upper lobe and lower lobe respectively in the normal lung (contralateral to tumor)...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047655/su-f-r-24-identifying-prognostic-imaging-biomarkers-in-early-stage-lung-cancer-using-radiomics
#12
X Zeng, J Wu, Y Cui, H Gao, R Li
PURPOSE: Patients diagnosed with early stage lung cancer have favorable outcomes when treated with surgery or stereotactic radiotherapy. However, a significant proportion (∼20%) of patients will develop metastatic disease and eventually die of the disease. The purpose of this work is to identify quantitative imaging biomarkers from CT for predicting overall survival in early stage lung cancer. METHODS: In this institutional review board-approved HIPPA-compliant retrospective study, we retrospectively analyzed the diagnostic CT scans of 110 patients with early stage lung cancer...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047623/su-f-r-13-decoding-18f-fdg-uptake-heterogeneity-for-primary-and-lymphoma-tumors-by-using-texture-analysis-in-pet-images
#13
C Ma, Y Yin
PURPOSE: To explore 18F-FDG uptake heterogeneity of primary tumor and lymphoma tumor by texture features of PET image and quantify the heterogeneity difference between primary tumor and lymphoma tumor. METHODS: 18 patients with primary tumor and lymphoma tumor in lung cancer were enrolled. All patients underwent whole-body 18F-FDG PET/CT scans before treatment. Texture features, based on Gray-level Co-occurrence Matrix, second and high order matrices are extracted from code using MATLAB software to quantify 18F-FDG uptake heterogeneity...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047538/su-f-r-20-image-texture-features-correlate-with-time-to-local-failure-in-lung-sbrt-patients
#14
M Andrews, M Abazeed, N Woody, K Stephans, G Videtic, P Xia, T Zhuang
PURPOSE: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT). METHODS AND MATERIALS: From an IRB-approved lung SBRT registry for patients treated between 2009-2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047431/su-f-r-51-radiomics-in-ct-perfusion-maps-of-head-and-neck-cancer
#15
M Nesteruk, O Riesterer, R Bundschuh, P Veit-Haibach, M Huellner, G Studer, S Stieb, S Glatz, M Pruschy, M Guckenberger, S Tanadini-Lang
PURPOSE: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. METHODS: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047413/su-f-r-40-robustness-test-of-computed-tomography-textures-of-lung-tissues-to-varying-scanning-protocols-using-a-realistic-phantom-environment
#16
S Lee, D Markel, G Hegyi, I El Naqa
PURPOSE: The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting. METHODS: We investigated 11 CT texture features for radiation-induced lung disease from 3 categories (first-order, grey level co-ocurrence matrix (GLCM), and Law's filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28047061/su-f-r-07-radiomics-of-ct-features-and-associations-and-correlation-with-outcomes-following-lung-sbrt
#17
E Schreibmann, A Iwinski Sutter, D Whitaker, J Switchenko, E Elder, K Higgins, P Patel
OBJECTIVE: To investigate the prognostic significance of image gradients and in predicting clinical outcomes in a patients with non-small cell lung cancer treated with stereotactic body radiotherapy (SBRT) on 71 patients with 83 treated lesions. METHODS: The records of patients treated with lung SBRT were retrospectively reviewed. When applicable, SBRT target volumes were modified to exclude any overlap with pleura, chestwall, or mediastinum. The ITK software package was utilized to generate quantitative measures of image intensity, inhomogeneity, shape morphology and first and second-order CT textures...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28046883/su-f-r-21-the-stability-of-radiomics-features-on-4d-fdg-pet-ct-images
#18
C Ma
PURPOSE: The aim of our study was to perform a stability analysis of 4D PET-derived features in non-small cell lung carcinoma (NSCLC) based on six different respiratory phases. METHODS: The 4D FDG-PET/CT respiratory phases were labeled as T0%, T17%, T33%,T50%, T67%, T83% phases, with the T0% phase approximately corresponding to the normal end-inspiration. Lesions were manually delineated based on fused PET-CT, using a standardized clinical delineation protocol. Six texture parameters were analyzed...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28046857/mo-de-207b-08-radiomic-ct-features-complement-semantic-annotations-to-predict-egfr-mutations-in-lung-adenocarcinomas
#19
E Rios Velazquez, Y Liu, C Parmar, V Narayan, R Gillies, H Aerts
PURPOSE: To compare the complementary value of quantitative radiomic features to that of radiologist-annotated semantic features in predicting EGFR mutations in lung adenocarcinomas. METHODS: Pre-operative CT images of 258 lung adenocarcinoma patients were available. Tumors were segmented using the sing-click ensemble segmentation algorithm. A set of radiomic features was extracted using 3D-Slicer. Test-retest reproducibility and unsupervised dimensionality reduction were applied to select a subset of reproducible and independent radiomic features...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28046844/su-f-r-45-the-prognostic-value-of-radiotherapy-based-on-the-changes-of-texture-features-between-pre-treatment-and-post-treatment-fdg-pet-image-for-nsclc-patients
#20
C Ma, Y Yin
PURPOSE: The purpose of this research is investigating which texture features extracted from FDG-PET images by gray-level co-occurrence matrix(GLCM) have a higher prognostic value than the other texture features. METHODS: 21 non-small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F-FDG PET/CT scans with both pre-treatment and post-treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB...
June 2016: Medical Physics
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