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https://www.readbyqxmd.com/read/28433412/quantitative-imaging-correlating-image-features-with-the-segmentation-accuracy-of-pet-based-tumor-contours-in-the-lung
#1
Perry B Johnson, Lori A Young, Narottam Lamichhane, Vivek Patel, Felix M Chinea, Fei Yang
The purpose of this study was to investigate the correlation between image features extracted from PET images and the accuracy of manually drawn lesion contours in the lung. Such correlations are interesting in that they could potentially be used in predictive models to help guide physician contouring. In this work, 26 synthetic PET datasets were created using an anthropomorphic phantom and Monte Carlo simulation. Manual contours of simulated lesions were provided by 10 physicians. Contour accuracy was quantified using five commonly used similarity metrics which were then correlated with several features extracted from the images...
April 19, 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
https://www.readbyqxmd.com/read/28430603/tumor-heterogeneity-assessed-by-texture-analysis-on-contrast-enhanced-ct-in-lung-adenocarcinoma-association-with-pathologic-grade
#2
Ying Liu, Shichang Liu, Fangyuan Qu, Qian Li, Runfen Cheng, Zhaoxiang Ye
Objectives To investigate whether texture features on contrast-enhanced computed tomography (CECT) images of lung adenocarcinoma have association with pathologic grade.Methods A cohort of 148 patients with surgically operated adenocarcinoma was retrospectively reviewed. Fifty-four CT features of the primary lung tumor were extracted from CECT images using open-source 3D Slicer software; meanwhile, enhancement homogeneity was evaluated by two radiologists using visual assessment. Multivariate logistic regression analysis was performed to determine significant image indicator of pathologic grade...
February 16, 2017: Oncotarget
https://www.readbyqxmd.com/read/28422299/incorporation-of-pre-therapy-18-f-fdg-uptake-data-with-ct-texture-features-into-a-radiomics-model-for-radiation-pneumonitis-diagnosis
#3
Gregory J Anthony, Alexandra Cunliffe, Richard Castillo, Ngoc Pham, Thomas Guerrero, Samuel G Armato, Hania Al-Hallaq
PURPOSE: To determine whether the addition of standardized uptake value (SUV) from PET scans to CT lung texture features could improve a radiomics-based model of radiation pneumonitis (RP) diagnosis in patients undergoing radiotherapy. METHODS AND MATERIALS: Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were collected including pre-therapy PET/CT scans, pre-/post-therapy diagnostic CT scans and RP status. Twenty texture features (first-order, fractal, Laws' filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung...
April 19, 2017: Medical Physics
https://www.readbyqxmd.com/read/28409834/fully-automatic-and-accurate-detection-of-lung-nodules-in-ct-images-using-a-hybrid-feature-set
#4
Furqan Shaukat, Gulistan Raja, Ali Gooya, Alejandro F Frangi
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly. METHOD: The proposed method starts with pre-processing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multi scale dot enhancement filtering prior to nodule detection and feature extraction...
April 13, 2017: Medical Physics
https://www.readbyqxmd.com/read/28392615/ultra-high-spatial-resolution-multi-energy-ct-using-photon-counting-detector-technology
#5
S Leng, R Gutjahr, A Ferrero, S Kappler, A Henning, A Halaweish, W Zhou, J Montoya, C McCollough
Two ultra-high-resolution (UHR) imaging modes, each with two energy thresholds, were implemented on a research, whole-body photon-counting-detector (PCD) CT scanner, referred to as sharp and UHR, respectively. The UHR mode has a pixel size of 0.25 mm at iso-center for both energy thresholds, with a collimation of 32 × 0.25 mm. The sharp mode has a 0.25 mm pixel for the low-energy threshold and 0.5 mm for the high-energy threshold, with a collimation of 48 × 0.25 mm. Kidney stones with mixed mineral composition and lung nodules with different shapes were scanned using both modes, and with the standard imaging mode, referred to as macro mode (0...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28373718/delta-radiomics-features-for-the-prediction-of-patient-outcomes-in-non-small-cell-lung-cancer
#6
Xenia Fave, Lifei Zhang, Jinzhong Yang, Dennis Mackin, Peter Balter, Daniel Gomez, David Followill, Aaron Kyle Jones, Francesco Stingo, Zhongxing Liao, Radhe Mohan, Laurence Court
Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non-small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC...
April 3, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28342715/prediction-of-neonatal-respiratory-morbidity-by-quantitative-ultrasound-lung-texture-analysis-a-multicenter-study
#7
Montse Palacio, Elisenda Bonet-Carne, Teresa Cobo, Alvaro Perez-Moreno, Joan Sabrià, Jute Richter, Marian Kacerovsky, Bo Jacobsson, Raúl A García-Posada, Fernando Bugatto, Ramon Santisteve, Àngels Vives, Mauro Parra-Cordero, Edgar Hernandez-Andrade, José Luis Bartha, Pilar Carretero-Lucena, Kai Lit Tan, Rogelio Cruz-Martínez, Minke Burke, Suseela Vavilala, Igor Iruretagoyena, Juan Luis Delgado, Mauro Schenone, Josep Vilanova, Francesc Botet, George S H Yeo, Jon Hyett, Jan Deprest, Roberto Romero, Eduard Gratacos
BACKGROUND: Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure limits the use of fetal lung maturity assessment. OBJECTIVE: The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (<39...
March 23, 2017: American Journal of Obstetrics and Gynecology
https://www.readbyqxmd.com/read/28339127/quantitative-ultrasound-texture-analysis-for-differentiating-preterm-from-term-fetal-lungs
#8
Sleiman R Ghorayeb, Luis A Bracero, Matthew J Blitz, Zara Rahman, Martin L Lesser
OBJECTIVES: To differentiate preterm (<37 weeks' gestation) from term (≥37 weeks' gestation) fetal lungs by using quantitative texture analysis of ultrasound images. METHODS: This study retrospectively evaluated singleton gestations with valid dating at 20 weeks' gestational age (GA) or later between January 2015 and December 2015. Images were obtained from Voluson E8 ultrasound systems (GE Healthcare, Milwaukee, WI). A region of interest was selected in each fetal lung image at the level of the 4 heart chambers from an area that appeared most representative of the overall lung tissue and had the least shadow...
March 24, 2017: Journal of Ultrasound in Medicine: Official Journal of the American Institute of Ultrasound in Medicine
https://www.readbyqxmd.com/read/28333649/multi-scale-rotation-invariant-convolutional-neural-networks-for-lung-texture-classification
#9
Qiangchang Wang, Yuanjie Zheng, Gongping Yang, Weidong Jin, Xinjian Chen, Yilong Yin
We propose a new Multi-scale Rotation-invariant Convolutional Neural Network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography (HRCT). MRCNN employs Gabor-local binary pattern (Gabor-LBP) which introduces a good property in image analysis - invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches...
March 21, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28325447/statistical-tools-for-the-temporal-analysis-and-classification-of-lung-lesions
#10
Stelmo Magalhães Barros Netto, Aristófanes Corrêa Silva, Hélio Lopes, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass
BACKGROUND AND OBJECTIVE: Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions. In addition, we extract textural features for the classification of lesions as benign or malignant. METHODS: We employ the statistical concept of uncertainty to associate each voxel of a lesion to a probability that changes occur in the lesion over time...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28297322/evaluation-of-the-tool-reg-refine-for-user-guided-deformable-image-registration
#11
Perry B Johnson, Kyle R Padgett, Kuan L Chen, Nesrin Dogan
"Reg Refine" is a tool available in the MIM Maestro v6.4.5 platform (www.mimsoftware.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground-truth models, the Pixel-based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck...
May 2016: Journal of Applied Clinical Medical Physics
https://www.readbyqxmd.com/read/28295386/an-integrated-segmentation-and-shape-based-classification-scheme-for-distinguishing-adenocarcinomas-from-granulomas-on-lung-ct
#12
Mehdi Alilou, Niha Beig, Mahdi Orooji, Anant Madabhushi, Prabhakar Rajiah, Michael Yang, Robert Gilkeson, Philip Linden, Vamsidhar Velcheti, Sagar Rakshit, Niyoti Reddy, Frank Jacono
PURPOSE: Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans...
March 14, 2017: Medical Physics
https://www.readbyqxmd.com/read/28268558/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#13
Jie Yang, Xinyang Feng, 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/28268557/prediction-of-malignant-and-benign-of-lung-tumor-using-a-quantitative-radiomic-method
#14
Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian
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/28268556/association-between-tumor-heterogeneity-and-progression-free-survival-in-non-small-cell-lung-cancer-patients-with-egfr-mutations-undergoing-tyrosine-kinase-inhibitors-therapy
#15
Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian
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/28258739/analysis-of-ct-features-and-quantitative-texture-analysis-in-patients-with-lung-adenocarcinoma-a-correlation-with-egfr-mutations-and-survival-rates
#16
B Sacconi, M Anzidei, A Leonardi, F Boni, L Saba, R Scipione, M Anile, M Rengo, F Longo, M Bezzi, F Venuta, A Napoli, A Laghi, C Catalano
AIM: To investigate the correlation between conventional computed tomography (CT) features, quantitative texture analysis (QTA), epidermal growth factor receptor (EGFR) mutations, and survival rates in patients with lung adenocarcinoma. MATERIALS AND METHODS: Sixty-eight patients were evaluated for conventional CT features and QTA in this retrospective study. A multiple logistic regression analysis and receiver operating characteristics (ROC) curve analysis versus death and EGFR status was performed for CT features and QTA in order to assess correlation between CT features, QTA, EGFR mutations, and survival rates...
February 28, 2017: Clinical Radiology
https://www.readbyqxmd.com/read/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#17
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
#18
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
#19
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/28169141/measuring-interobserver-disagreement-in-rating-diagnostic-characteristics-of-pulmonary-nodule-using-the-lung-imaging-database-consortium-and-image-database-resource-initiative
#20
Hongli Lin, Changxing Huang, Weisheng Wang, Jiawei Luo, Xuedong Yang, Yuling Liu
RATIONALE AND OBJECTIVES: The purpose of this study was to measure and analyze interobserver disagreement in rating diagnostic characteristics of pulmonary nodules on computed tomography scans using the Lung Imaging Database Consortium and Image Database Resource Initiative (LIDC/IDRI) database, and then to provide investigators with understanding the variability in rating diagnostic characteristics among radiologists. MATERIALS AND METHODS: A histogram-based accumulated nodule-level approach is proposed to measure interobserver disagreement in rating diagnostic characteristics of pulmonary nodules among radiologists...
February 3, 2017: Academic Radiology
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