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https://www.readbyqxmd.com/read/29325682/computer-based-quantitative-computed-tomography-image-analysis-in-idiopathic-pulmonary-fibrosis-a-mini-review
#1
REVIEW
Hirotsugu Ohkubo, Hiroaki Nakagawa, Akio Niimi
Idiopathic pulmonary fibrosis (IPF) is the most common type of progressive idiopathic interstitial pneumonia in adults. Many computer-based image analysis methods of chest computed tomography (CT) used in patients with IPF include the mean CT value of the whole lungs, density histogram analysis, density mask technique, and texture classification methods. Most of these methods offer good assessment of pulmonary functions, disease progression, and mortality. Each method has merits that can be used in clinical practice...
January 2018: Respiratory Investigation
https://www.readbyqxmd.com/read/29312866/quantitative-texture-analysis-on-pre-treatment-computed-tomography-predicts-local-recurrence-in-stage-i-non-small-cell-lung-cancer-following-stereotactic-radiation-therapy
#2
Carole Dennie, Rebecca Thornhill, Carolina A Souza, Cecilia Odonkor, Jason R Pantarotto, Robert MacRae, Graham Cook
Background: The prediction of local recurrence (LR) of stage I non-small cell lung cancer (NSCLC) after definitive stereotactic body radiotherapy (SBRT) remains elusive. The purpose of this study was to assess whether quantitative imaging features on pre-treatment computed tomography (CT) can predict LR beyond 18 (18F) fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT maximum standard uptake value (SUVmax). Methods: This retrospective study evaluated 36 patients with 37 stage I NSCLC who had local tumor control (LC; n=19) and (LR; n=18)...
December 2017: Quantitative Imaging in Medicine and Surgery
https://www.readbyqxmd.com/read/29290259/radiomics-and-radiogenomics-in-lung-cancer-a-review-for-the-clinician
#3
REVIEW
Rajat Thawani, Michael McLane, Niha Beig, Soumya Ghose, Prateek Prasanna, Vamsidhar Velcheti, Anant Madabhushi
Lung cancer is responsible for a large proportion of cancer-related deaths across the globe, with delayed detection being perhaps the most significant factor for its high mortality rate. Though the National Lung Screening Trial argues for screening of certain at-risk populations, the practical implementation of these screening efforts has not yet been successful and remains in high demand. Radiomics refers to the computerized extraction of data from radiologic images, and provides unique potential for making lung cancer screening more rapid and accurate using machine learning algorithms...
January 2018: Lung Cancer: Journal of the International Association for the Study of Lung Cancer
https://www.readbyqxmd.com/read/29275617/-treatment-of-patients-with-different-degree-of-acute-respiratory-distress-syndrome-caused-by-inhalation-of-white-smoke
#4
F W Yang, H M Xin, J H Zhu, X Y Feng, X C Jiang, Z Y Gong, Y L Tong
Objective: To summarize the treatment experience of patients with different degree of acute respiratory distress syndrome (ARDS) caused by inhalation of white smoke from burning smoke bomb. Methods: A batch of 13 patients with different degree of ARDS caused by inhalation of white smoke from burning smoke bomb, including 2 patients complicated by pulmonary fibrosis at the late stage, were admitted to our unit in February 2016. Patients were divided into mild (9 cases), moderate (2 cases), and serious (2 cases) degree according to the ARDS Berlin diagnostic criteria...
December 20, 2017: Zhonghua Shao Shang za Zhi, Zhonghua Shaoshang Zazhi, Chinese Journal of Burns
https://www.readbyqxmd.com/read/29247171/computer-aided-nodule-assessment-and-risk-yield-canary-may-facilitate-non-invasive-prediction-of-egfr-mutation-status-in-lung-adenocarcinomas
#5
Ryan Clay, Benjamin R Kipp, Sarah Jenkins, Ron A Karwoski, Fabien Maldonado, Srinivasan Rajagopalan, Jesse S Voss, Brian J Bartholmai, Marie Christine Aubry, Tobias Peikert
Computer-Aided Nodule Assessment and Risk Yield (CANARY) is quantitative imaging analysis software that predicts the histopathological classification and post-treatment disease-free survival of patients with adenocarcinoma of the lung. CANARY characterizes nodules by the distribution of nine color-coded texture-based exemplars. We hypothesize that quantitative computed tomography (CT) analysis of the tumor and tumor-free surrounding lung facilitates non-invasive identification of clinically-relevant mutations in lung adenocarcinoma...
December 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29221270/analysis-of-the-clinical-differentiation-of-pulmonary-sclerosing-pneumocytoma-and-lung-cancer
#6
Jun Zhu
Background: Pulmonary sclerosing pneumocytoma (PSP) is a rare benign lung tumor. This study investigated the diagnostic experience of PSP and lung cancer. Methods: This study is a retrospective study. We observed the locations of lung lesions, imaging form and clinical symptoms, and recorded the surgical complications through comparing patients with PSP and lung cancer. Results: From December 2012 to February 2017, 187 PSP cases and 197 lung cancer cases were collected...
September 2017: Journal of Thoracic Disease
https://www.readbyqxmd.com/read/29202136/explaining-radiological-emphysema-subtypes-with-unsupervised-texture-prototypes-mesa-copd-study
#7
Jie Yang, Elsa D Angelini, Benjamin M Smith, John H M Austin, Eric A Hoffman, David A Bluemke, R Graham Barr, Andrew F Laine
Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes...
2017: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging: MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers
https://www.readbyqxmd.com/read/29185058/content-based-image-retrieval-for-lung-nodule-classification-using-texture-features-and-learned-distance-metric
#8
Guohui Wei, Hui Cao, He Ma, Shouliang Qi, Wei Qian, Zhiqing Ma
Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR scheme (TSCBIR) for computer-aided diagnosis of lung nodules. Two similarity metrics, semantic relevance and visual similarity, are introduced to measure the similarity of different nodules. The first step is to search for K most similar reference ROIs for each queried ROI with the semantic relevance metric...
November 29, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29172671/pulmonary-quantitative-ct-imaging-in-focal-and-diffuse-disease-current-research-and-clinical-applications
#9
Mario Silva, Gianluca Milanese, Valeria Seletti, Alarico Ariani, Nicola Sverzellati
The frenetic development of imaging technology - both hardware and software - provides exceptional potential for investigation of the lung. In the last two decades, computed tomography (CT) was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative computed tomography (QCT). Hundreds of thousands of CTs have been analyzed for characterization of focal and diffuse disease of the lung...
November 27, 2017: British Journal of Radiology
https://www.readbyqxmd.com/read/29060752/content-based-retrieval-for-lung-nodule-diagnosis-using-learned-distance-metric
#10
Guohui Wei, He Ma, Wei Qian, Hongyang Jiang, Xinzhuo Zhao
Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29036692/clinical-utility-of-texture-analysis-of-18f-fdg-pet-ct-in-patients-with-stage-i-lung-cancer-treated-with-stereotactic-body-radiotherapy
#11
Kazuya Takeda, Kentaro Takanami, Yuko Shirata, Takaya Yamamoto, Noriyoshi Takahashi, Kengo Ito, Kei Takase, Keiichi Jingu
We evaluated the reproducibility and predictive value of texture parameters and existing parameters of 18F-FDG PET/CT images in Stage I non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT). Twenty-six patients with Stage I NSCLC (T1-2N0M0) were retrospectively analyzed. All of the patients underwent an 18F-FDG PET/CT scan before treatment and were treated with SBRT. Each tumor was delineated using PET Edge (MIM Software Inc., Cleveland, OH), and texture parameters were calculated using open-source code CGITA...
September 21, 2017: Journal of Radiation Research
https://www.readbyqxmd.com/read/28961108/low-dose-lung-ct-image-restoration-using-adaptive-prior-features-from-full-dose-training-database
#12
Yuanke Zhang, Junyan Rong, Hongbing Lu, Yuxiang Xing, Jing Meng
The valuable structure features in full-dose CT (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This study aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm (APFA), for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively...
September 27, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28944403/prediction-of-disease-free-survival-by-the-pet-ct-radiomic-signature-in-non-small-cell-lung-cancer-patients-undergoing-surgery
#13
Margarita Kirienko, Luca Cozzi, Lidija Antunovic, Lisa Lozza, Antonella Fogliata, Emanuele Voulaz, Alexia Rossi, Arturo Chiti, Martina Sollini
PURPOSE: Radiomic features derived from the texture analysis of different imaging modalities e show promise in lesion characterisation, response prediction, and prognostication in lung cancer patients. The present study aimed to identify an images-based radiomic signature capable of predicting disease-free survival (DFS) in non-small cell lung cancer (NSCLC) patients undergoing surgery. METHODS: A cohort of 295 patients was selected. Clinical parameters (age, sex, histological type, tumour grade, and stage) were recorded for all patients...
September 24, 2017: European Journal of Nuclear Medicine and Molecular Imaging
https://www.readbyqxmd.com/read/28934225/harmonizing-the-pixel-size-in-retrospective-computed-tomography-radiomics-studies
#14
Dennis Mackin, Xenia Fave, Lifei Zhang, Jinzhong Yang, A Kyle Jones, Chaan S Ng, Laurence Court
Consistent pixel sizes are of fundamental importance for assessing texture features that relate intensity and spatial information in radiomics studies. To correct for the effects of variable pixel sizes, we combined image resampling with Butterworth filtering in the frequency domain and tested the correction on computed tomography (CT) scans of lung cancer patients reconstructed 5 times with pixel sizes varying from 0.59 to 0.98 mm. One hundred fifty radiomics features were calculated for each preprocessing and field-of-view combination...
2017: PloS One
https://www.readbyqxmd.com/read/28929225/prediction-of-survival-by-texture-based-automated-quantitative-assessment-of-regional-disease-patterns-on-ct-in-idiopathic-pulmonary-fibrosis
#15
Sang Min Lee, Joon Beom Seo, Sang Young Oh, Tae Hoon Kim, Jin Woo Song, Sang Min Lee, Namkug Kim
OBJECTIVES: To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). METHODS: A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were included. The extents of five regional disease patterns were quantified using an in-house texture-based automated system. The fibrosis score was defined as the sum of the extent of honeycombing and reticular opacity...
September 19, 2017: European Radiology
https://www.readbyqxmd.com/read/28898189/ct-texture-analysis-definitions-applications-biologic-correlates-and-challenges
#16
Meghan G Lubner, Andrew D Smith, Kumar Sandrasegaran, Dushyant V Sahani, Perry J Pickhardt
This review discusses potential oncologic and nononcologic applications of CT texture analysis ( CTTA CT texture analysis ), an emerging area of "radiomics" that extracts, analyzes, and interprets quantitative imaging features. CTTA CT texture analysis allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation and may reflect information about the tissue microenvironment. CTTA CT texture analysis has shown promise in lesion characterization, such as differentiating benign from malignant or more biologically aggressive lesions...
September 2017: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/28881840/tumor-heterogeneity-assessed-by-texture-analysis-on-contrast-enhanced-ct-in-lung-adenocarcinoma-association-with-pathologic-grade
#17
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...
August 8, 2017: Oncotarget
https://www.readbyqxmd.com/read/28856247/quantitative-image-quality-comparison-of-reduced-and-standard-dose-dual-energy-multiphase-chest-abdomen-and-pelvis-ct
#18
Mario Buty, Ziyue Xu, Aaron Wu, Mingchen Gao, Chelyse Nelson, Georgios Z Papadakis, Uygar Teomete, Haydar Celik, Baris Turkbey, Peter Choyke, Daniel J Mollura, Ulas Bagci, Les R Folio
We present a new image quality assessment method for determining whether reducing radiation dose impairs the image quality of computed tomography (CT) in qualitative and quantitative clinical analyses tasks. In this Institutional Review Board-exempt study, we conducted a review of 50 patients (male, 22; female, 28) who underwent reduced-dose CT scanning on the first follow-up after standard-dose multiphase CT scanning. Scans were for surveillance of von Hippel-Lindau disease (N = 26) and renal cell carcinoma (N = 10)...
June 2017: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28844845/lepidic-predominant-pulmonary-lesions-lpl-ct-based-distinction-from-more-invasive-adenocarcinomas-using-3d-volumetric-density-and-first-order-ct-texture-analysis
#19
Jeffrey B Alpert, Henry Rusinek, Jane P Ko, Bari Dane, Harvey I Pass, Bernard K Crawford, Amy Rapkiewicz, David P Naidich
RATIONALE AND OBJECTIVES: This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas. MATERIALS AND METHODS: This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (nā€‰=ā€‰43) and INV (nā€‰=ā€‰21), were evaluated using high-resolution computed tomography...
August 24, 2017: Academic Radiology
https://www.readbyqxmd.com/read/28839156/diagnostic-classification-of-solitary-pulmonary-nodules-using-dual-time-18f-fdg-pet-ct-image-texture-features-in-granuloma-endemic-regions
#20
Song Chen, Stephanie Harmon, Timothy Perk, Xuena Li, Meijie Chen, Yaming Li, Robert Jeraj
Lung cancer, the most commonly diagnosed cancer worldwide, usually presents as solid pulmonary nodules (SPNs) on early diagnostic images. Classification of malignant disease at this early timepoint is critical for improving the success of surgical resection and increasing 5-year survival rates. 18F-fluorodeoxyglucose (18F-FDG) PET/CT has demonstrated value for SPNs diagnosis with high sensitivity to detect malignant SPNs, but lower specificity in diagnosing malignant SPNs in populations with endemic infectious lung disease...
August 24, 2017: Scientific Reports
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