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Radiomics

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https://www.readbyqxmd.com/read/28212290/defining-clinical-response-criteria-and-early-response-criteria-for-precision-oncology-current-state-of-the-art-and-future-perspectives
#1
Vivek Subbiah, Hubert H Chuang, Dhiraj Gambhire, Kalevi Kairemo
In this era of precision oncology, there has been an exponential growth in the armamentarium of genomically targeted therapies and immunotherapies. Evaluating early responses to precision therapy is essential for "go" versus "no go" decisions for these molecularly targeted drugs and agents that arm the immune system. Many different response assessment criteria exist for use in solid tumors and lymphomas. We reviewed the literature using the Medline/PubMed database for keywords "response assessment" and various known response assessment criteria published up to 2016...
February 15, 2017: Diagnostics
https://www.readbyqxmd.com/read/28211505/corrigendum-defining-a-radiomic-response-phenotype-a-pilot-study-using-targeted-therapy-in-nsclc
#2
Hugo J W L Aerts, Patrick Grossmann, Yongqiang Tan, Geoffrey R Oxnard, Naiyer Rizvi, Lawrence H Schwartz, Binsheng Zhao
No abstract text is available yet for this article.
February 17, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28205306/multi-site-quality-and-variability-analysis-of-3d-fdg-pet-segmentations-based-on-phantom-and-clinical-image-data
#3
Reinhard R Beichel, Brian J Smith, Christian Bauer, Ethan J Ulrich, Payam Ahmadvand, Mikalai M Budzevich, Robert J Gillies, Dmitry Goldgof, Milan Grkovski, Ghassan Hamarneh, Qiao Huang, Paul E Kinahan, Charles M Laymon, James M Mountz, John P Muzi, Mark Muzi, Sadek Nehmeh, Matthew J Oborski, Yongqiang Tan, Binsheng Zhao, John J Sunderland, John M Buatti
PURPOSE: Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics...
February 2017: Medical Physics
https://www.readbyqxmd.com/read/28199039/radiomics-assessment-of-bladder-cancer-grade-using-texture-features-from-diffusion-weighted-imaging
#4
Xi Zhang, Xiaopan Xu, Qiang Tian, Baojuan Li, Yuxia Wu, Zengyue Yang, Zhengrong Liang, Yang Liu, Guangbin Cui, Hongbing Lu
PURPOSE: To 1) describe textural features from diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low-grade bladder cancer from high-grade, and 2) propose a radiomics-based strategy for cancer grading using texture features. MATERIALS AND METHODS: In all, 61 patients with bladder cancer (29 in high- and 32 in low-grade groups) were enrolled in this retrospective study. Histogram- and gray-level co-occurrence matrix (GLCM)-based radiomics features were extracted from cancerous volumes of interest (VOIs) on DWI and corresponding ADC maps of each patient acquired from 3...
February 15, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28180924/ct-based-radiomics-signature-a-potential-biomarker-for-preoperative-prediction-of-early-recurrence-in-hepatocellular-carcinoma
#5
Ying Zhou, Lan He, Yanqi Huang, Shuting Chen, Penqi Wu, Weitao Ye, Zaiyi Liu, Changhong Liang
PURPOSE: To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC). METHODS: A total of 215 HCC patients who underwent partial hepatectomy were enrolled in this retrospective study, and all the patients were followed up at least within 1 year. Radiomics features were extracted from arterial- and portal venous-phase CT images, and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model...
February 8, 2017: Abdominal Radiology
https://www.readbyqxmd.com/read/28176411/diffusion-radiomics-analysis-of-intratumoral-heterogeneity-in-a-murine-prostate-cancer-model-following-radiotherapy-pixelwise-correlation-with-histology
#6
Yu-Chun Lin, Gigin Lin, Ji-Hong Hong, Yi-Ping Lin, Fang-Hsin Chen, Shu-Hang Ng, Chun-Chieh Wang
PURPOSE: To investigate the biological meaning of apparent diffusion coefficient (ADC) values in tumors following radiotherapy. MATERIALS AND METHODS: Five mice bearing TRAMP-C1 tumor were half-irradiated with a dose of 15 Gy. Diffusion-weighted images, using multiple b-values from 0 to 3000 s/mm(2) , were acquired at 7T on day 6. ADC values calculated by a two-point estimate and monoexponential fitting of signal decay were compared between the irradiated and nonirradiated regions of the tumor...
February 8, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28175417/139%C3%A2-clinically-applicable-and-biologically-validated-mri-radiomic-test-method-predicts-glioblastoma-genomic-landscape-and-survival
#7
Pascal O Zinn, Sanjay K Singh, Aikaterini Kotrotsou, Faramak Zandi, Ginu Thomas, Masumeh Hatami, Markus M Luedi, Ahmed Elakkad, Islam Hassan, Joy Gumin, Erik P Sulman, Frederick F Lang, Rivka R Colen
No abstract text is available yet for this article.
August 1, 2016: Neurosurgery
https://www.readbyqxmd.com/read/28168275/promises-and-challenges-for-the-implementation-of-computational-medical-imaging-radiomics-in-oncology
#8
E J Limkin, R Sun, L Dercle, E I Zacharaki, C Robert, S Reuzé, A Schernberg, N Paragios, E Deutsch, C Ferté
No abstract text is available yet for this article.
February 7, 2017: Annals of Oncology: Official Journal of the European Society for Medical Oncology
https://www.readbyqxmd.com/read/28166261/radiomic-analysis-reveals-dce-mri-features-for-prediction-of-molecular-subtypes-of-breast-cancer
#9
Ming Fan, Hui Li, Shijian Wang, Bin Zheng, Juan Zhang, Lihua Li
The purpose of this study was to investigate the role of features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and to incorporated clinical information to predict the molecular subtypes of breast cancer. In particular, 60 breast cancers with the following four molecular subtypes were analyzed: luminal A, luminal B, human epidermal growth factor receptor-2 (HER2)-over-expressing and basal-like. The breast region was segmented and the suspicious tumor was depicted on sequentially scanned MR images from each case...
2017: PloS One
https://www.readbyqxmd.com/read/28152264/prediction-of-malignancy-by-a-radiomic-signature-from-contrast-agent-free-diffusion-mri-in-suspicious-breast-lesions-found-on-screening-mammography
#10
Sebastian Bickelhaupt, Daniel Paech, Philipp Kickingereder, Franziska Steudle, Wolfgang Lederer, Heidi Daniel, Michael Götz, Nils Gählert, Diana Tichy, Manuel Wiesenfarth, Frederik B Laun, Klaus H Maier-Hein, Heinz-Peter Schlemmer, David Bonekamp
PURPOSE: To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences. MATERIALS AND METHODS: From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted...
February 2, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28149958/radiomics-of-lung-nodules-a-multi-institutional-study-of-robustness-and-agreement-of-quantitative-imaging-features
#11
Jayashree Kalpathy-Cramer, Artem Mamomov, Binsheng Zhao, Lin Lu, Dmitry Cherezov, Sandy Napel, Sebastian Echegaray, Daniel Rubin, Michael McNitt-Gray, Pechin Lo, Jessica C Sieren, Johanna Uthoff, Samantha K N Dilger, Brandan Driscoll, Ivan Yeung, Lubomir Hadjiiski, Kenny Cha, Yoganand Balagurunathan, Robert Gillies, Dmitry Goldgof
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28112418/intrinsic-dependencies-of-ct-radiomic-features-on-voxel-size-and-number-of-gray-levels
#12
Muhammad Shafiq-Ul-Hassan, Geoffrey G Zhang, Kujtim Latifi, Ghanim Ullah, Dylan C Hunt, Yoganand Balagurunathan, Mahmoud Abrahem Abdalah, Matthew B Schabath, Dmitry G Goldgof, Dennis Mackin, Laurence Edward Court, Robert James Gillies, Eduardo Gerardo Moros
PURPOSE: Many radiomics features were originally developed for non-medical imaging applications and therefore original assumptions may need to be reexamined. In this study, we investigated the impact of slice thickness and pixel spacing (or pixel size) on radiomics features extracted from Computed Tomography (CT) phantom images acquired with different scanners as well as different acquisition and reconstruction parameters. The dependence of CT texture features on gray level discretization was also evaluated...
January 23, 2017: Medical Physics
https://www.readbyqxmd.com/read/28079714/radiomic-phenotyping-in-brain-cancer-to-unravel-hidden-information-in-medical-images
#13
Srishti Abrol, Aikaterini Kotrotsou, Ahmed Salem, Pascal O Zinn, Rivka R Colen
Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information...
February 2017: Topics in Magnetic Resonance Imaging: TMRI
https://www.readbyqxmd.com/read/28079702/radiomic-analysis-reveals-prognostic-information-in-t1-weighted-baseline-magnetic-resonance-imaging-in-patients-with-glioblastoma
#14
Michael Ingrisch, Moritz Jörg Schneider, Dominik Nörenberg, Giovanna Negrao de Figueiredo, Klaus Maier-Hein, Bogdana Suchorska, Ulrich Schüller, Nathalie Albert, Hartmut Brückmann, Maximilian Reiser, Jörg-Christian Tonn, Birgit Ertl-Wagner
OBJECTIVES: The aim of this study was to investigate whether radiomic analysis with random survival forests (RSFs) can predict overall survival from T1-weighted contrast-enhanced baseline magnetic resonance imaging (MRI) scans in a cohort of glioblastoma multiforme (GBM) patients with uniform treatment. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board and informed consent was waived. The MRI scans from 66 patients with newly diagnosed GBM from a previous prospective study were analyzed...
January 9, 2017: Investigative Radiology
https://www.readbyqxmd.com/read/28056889/dimensionality-reduction-based-fusion-approaches-for-imaging-and-non-imaging-biomedical-data-concepts-workflow-and-use-cases
#15
Satish E Viswanath, Pallavi Tiwari, George Lee, Anant Madabhushi
BACKGROUND: With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data being routinely acquired for disease characterization, there is a pressing need for quantitative tools to combine these varied channels of information. The goal of these integrated predictors is to combine these varied sources of information, while improving on the predictive ability of any individual modality. A number of application-specific data fusion methods have been previously proposed in the literature which have attempted to reconcile the differences in dimensionalities and length scales across different modalities...
January 5, 2017: BMC Medical Imaging
https://www.readbyqxmd.com/read/28048922/mo-de-207b-09-a-consistent-test-for-radiomics-softwares
#16
J Gan, J Wang, H Zhong, R Luo, Z Zhou, P Hu, L Shen, Z Zhang
PURPOSE: The purpose of this study is to investigate the consistency of the features extracted by different radiomics software. METHODS: CT sets of 212 patients with rectum tumor and three existing radiomics feature extraction tools (IBEX from MD_Anderson, RADIOMICS from Maastro, and a MATLAB based code-set from our institution) were enrolled in this study. Among thousands of features that were extracted by three softwares, 273 (between our codes and RADIOMICS), 51 (between IBEX and RADIOMICS) and 34 (between our codes and IBEX) feature pairs were proven to be conjugated according to feature definition...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048907/su-f-r-46-predicting-distant-failure-in-lung-sbrt-using-multi-objective-radiomics-model
#17
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/28048895/mo-a-207b-01-radiomics-segmentation-feature-extraction-techniques
#18
H Veeraraghavan
: Image segmentation is one of the core problems for applying radiomics-based analysis to images. However, achieving repeatable and accurate segmentations for large datasets is challenging. The choice of segmentation method, the metrics used to evaluate the quality of such segmentations all depend on the specific clinical problem. This course will introduce three approaches, namely, fully automatic, interactive, and semi-automatic methods for generating segmentations. The pros and cons of each approach and when to choose a specific method will be discussed...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048862/th-e-202-02-the-use-of-hypoxia-pet-imaging-for-radiotherapy
#19
J Humm
: PET/CT is a very important imaging tool in the management of oncology patients. PET/CT has been applied for treatment planning and response evaluation in radiation therapy. This educational session will discuss: 1. Pitfalls and remedies in PET/CT imaging for RT planning 2. The use of hypoxia PET imaging for radiotherapy 3. PET for tumor response evaluation The first presentation will address the issue of mis-registration between the CT and PET images in the thorax and the abdomen. We will discuss the challenges of respiratory gating and introduce an average CT technique to improve the registration for dose calculation and image-guidance in radiation therapy...
June 2016: Medical Physics
https://www.readbyqxmd.com/read/28048842/su-f-r-18-updates-to-the-computational-environment-for-radiological-research-for-image-analysis
#20
Aditya P Apte, Joseph O Deasy
PURPOSE: To present new tools in CERR for Texture Analysis and Visualization. METHOD: (1) Quantitative Image Analysis: We added the ability to compute Haralick texture features based on local neighbourhood. The Texture features depend on many parameters used in their derivation. For example: (a) directionality, (b) quantization of image, (c) patch-size for the neighborhood, (d) handling of the edge voxels within the region of interest, (e) Averaging co-occurance matrix vs texture features for different directions etc...
June 2016: Medical Physics
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