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https://www.readbyqxmd.com/read/28629560/ct-based-radiomics-signature-for-differentiating-borrmann-type-iv-gastric-cancer-from-primary-gastric-lymphoma
#1
Zelan Ma, Mengjie Fang, Yanqi Huang, Lan He, Xin Chen, Cuishan Liang, Xiaomei Huang, Zixuan Cheng, Di Dong, Changhong Liang, Jiajun Xie, Jie Tian, Zaiyi Liu
PURPOSE: To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL). MATERIALS AND METHODS: 40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model...
June 2017: European Journal of Radiology
https://www.readbyqxmd.com/read/28623121/radiomics-based-assessment-of-radiation-induced-lung-injury-after-stereotactic-body-radiotherapy
#2
Angel Moran, Megan E Daly, Stephen S F Yip, Tokihiro Yamamoto
BACKGROUND: Over 50% of patients who receive stereotactic body radiotherapy (SBRT) develop radiographic evidence of radiation-induced lung injury. Radiomics is an emerging approach that extracts quantitative features from image data, which may provide greater value and a better understanding of pulmonary toxicity than conventional approaches. We aimed to investigate the potential of computed tomography-based radiomics in characterizing post-SBRT lung injury. METHODS: A total of 48 diagnostic thoracic computed tomography scans (acquired prior to SBRT and at 3, 6, and 9 months post-SBRT) from 14 patients were analyzed...
May 25, 2017: Clinical Lung Cancer
https://www.readbyqxmd.com/read/28620443/pattern-recognition-for-predictive-preventive-and-personalized-medicine-in-cancer
#3
REVIEW
Tingting Cheng, Xianquan Zhan
Predictive, preventive, and personalized medicine (PPPM) is the hot spot and future direction in the field of cancer. Cancer is a complex, whole-body disease that involved multi-factors, multi-processes, and multi-consequences. A series of molecular alterations at different levels of genes (genome), RNAs (transcriptome), proteins (proteome), peptides (peptidome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens are involved in tumorigenesis and mutually associate and function in a network system, thus determines the difficulty in the use of a single molecule as biomarker for personalized prediction, prevention, diagnosis, and treatment for cancer...
March 2017: EPMA Journal
https://www.readbyqxmd.com/read/28615677/associations-between-radiologist-defined-semantic-and-automatically-computed-radiomic-features-in-non-small-cell-lung-cancer
#4
Stephen S F Yip, Ying Liu, Chintan Parmar, Qian Li, Shichang Liu, Fangyuan Qu, Zhaoxiang Ye, Robert J Gillies, Hugo J W L Aerts
Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms...
June 14, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28612050/a-rapid-segmentation-insensitive-digital-biopsy-method-for-radiomic-feature-extraction-method-and-pilot-study-using-ct-images-of-non-small-cell-lung-cancer
#5
Sebastian Echegaray, Viswam Nair, Michael Kadoch, Ann Leung, Daniel Rubin, Olivier Gevaert, Sandy Napel
Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28610955/radiomic-machine-learning-classifiers-for-prognostic-biomarkers-of-advanced-nasopharyngeal-carcinoma
#6
Bin Zhang, Xin He, Fusheng Ouyang, Dongsheng Gu, Yuhao Dong, Lu Zhang, Xiaokai Mo, Wenhui Huang, Jie Tian, Shuixing Zhang
We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error...
June 10, 2017: Cancer Letters
https://www.readbyqxmd.com/read/28608160/erratum-to-radiomic-features-from-the-peritumoral-brain-parenchyma-on-treatment-na%C3%A3-ve-multi-parametric-mr-imaging-predict-long-versus-short-term-survival-in-glioblastoma-multiforme-preliminary-findings
#7
Prateek Prasanna, Jay Patel, Sasan Partovi, Anant Madabhushi, Pallavi Tiwari
No abstract text is available yet for this article.
June 12, 2017: European Radiology
https://www.readbyqxmd.com/read/28604368/predictive-modeling-of-outcomes-following-definitive-chemoradiotherapy-for-oropharyngeal-cancer-based-on-fdg-pet-image-characteristics
#8
Michael R Folkert, Jeremy Setton, Aditya P Apte, Milan Grkovski, Robert J Young, Heiko Schöder, Wade L Thorstad, Nancy Y Lee, Joseph O Deasy, Jung Hun Oh
In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified...
July 7, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28597119/highlights-lecture-eanm-2016-embracing-molecular-imaging-and-multi-modal-imaging-a-smart-move-for-nuclear-medicine-towards-personalized-medicine
#9
REVIEW
Eric O Aboagye, Françoise Kraeber-Bodéré
The 2016 EANM Congress took place in Barcelona, Spain, from 15 to 19 October under the leadership of Prof. Wim Oyen, chair of the EANM Scientific Committee. With more than 6,000 participants, this congress was the most important European event in nuclear medicine, bringing together a multidisciplinary community involved in the different fields of nuclear medicine. There were over 600 oral and 1,200 poster or e-Poster presentations with an overwhelming focus on development and application of imaging for personalized care, which is timely for the community...
June 8, 2017: European Journal of Nuclear Medicine and Molecular Imaging
https://www.readbyqxmd.com/read/28595812/beyond-imaging-the-promise-of-radiomics
#10
REVIEW
Michele Avanzo, Joseph Stancanello, Issam El Naqa
The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images...
June 2017: Physica Medica: PM
https://www.readbyqxmd.com/read/28587043/effects-of-motion-on-radiomics-analysis-of-thoracic-cancers
#11
F F Yin, K Lafata, J C Hong, C R Kelsey
No abstract text is available yet for this article.
May 1, 2017: International Journal of Radiation Oncology, Biology, Physics
https://www.readbyqxmd.com/read/28587041/using-pretreatment-radiomics-and-delta-radiomics-features-to-predict-non-small-cell-lung-cancer-patient-outcomes
#12
X Fave, L Zhang, J Yang, D Mackin, P Balter, D R Gomez, D Followill, A K Jones, F Stingo, R Mohan, Z Liao, L E Court
No abstract text is available yet for this article.
May 1, 2017: International Journal of Radiation Oncology, Biology, Physics
https://www.readbyqxmd.com/read/28586353/lack-of-robustness-of-textural-measures-obtained-from-3d-brain-tumor-mris-impose-a-need-for-standardization
#13
David Molina, Julián Pérez-Beteta, Alicia Martínez-González, Juan Martino, Carlos Velasquez, Estanislao Arana, Víctor M Pérez-García
PURPOSE: Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. MATERIALS AND METHODS: Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study...
2017: PloS One
https://www.readbyqxmd.com/read/28575105/assessment-of-treatment-response-during-chemoradiation-therapy-for-pancreatic-cancer-based-on-quantitative-radiomic-analysis-of-daily-cts-an-exploratory-study
#14
Xiaojian Chen, Kiyoko Oshima, Diane Schott, Hui Wu, William Hall, Yingqiu Song, Yalan Tao, Dingjie Li, Cheng Zheng, Paul Knechtges, Beth Erickson, X Allen Li
PURPOSE: In an effort for early assessment of treatment response, we investigate radiation induced changes in quantitative CT features of tumor during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. METHODS: Diagnostic-quality CT data acquired daily during routine CT-guided CRT using a CT-on-rails for 20 pancreatic head cancer patients were analyzed. On each daily CT, the pancreatic head, the spinal cord and the aorta were delineated and the histograms of CT number (CTN) in these contours were extracted...
2017: PloS One
https://www.readbyqxmd.com/read/28574816/prediction-of-cervical-cancer-recurrence-using-textural-features-extracted-from-18f-fdg-pet-images-acquired-with-different-scanners
#15
Sylvain Reuzé, Fanny Orlhac, Cyrus Chargari, Christophe Nioche, Elaine Limkin, François Riet, Alexandre Escande, Christine Haie-Meder, Laurent Dercle, Sébastien Gouy, Irène Buvat, Eric Deutsch, Charlotte Robert
OBJECTIVES: To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline 18F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study. METHODS: 118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values...
May 15, 2017: Oncotarget
https://www.readbyqxmd.com/read/28567548/the-impact-of-image-reconstruction-settings-on-18f-fdg-pet-radiomic-features-multi-scanner-phantom-and-patient-studies
#16
Isaac Shiri, Arman Rahmim, Pardis Ghaffarian, Parham Geramifar, Hamid Abdollahi, Ahmad Bitarafan-Rajabi
OBJECTIVES: The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. METHODS: Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied...
May 31, 2017: European Radiology
https://www.readbyqxmd.com/read/28566328/somatic-mutations-drive-distinct-imaging-phenotypes-in-lung-cancer
#17
Emmanuel Rios Velazquez, Chintan Parmar, Ying Liu, Thibaud P Coroller, Gisele Cruz, Olya Stringfield, Zhaoxiang Ye, G Mike Makrigiorgos, Fiona M M Fennessy, Raymond H Mak, Robert J Gillies, John Quackenbush, Hugo Aerts
Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence (AI) methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered computed tomography (CT) image analytics...
May 31, 2017: Cancer Research
https://www.readbyqxmd.com/read/28557792/the-approximate-entropy-concept-extended-to-three-dimensions-for-calibrated-single-parameter-structural-complexity-interrogation-of-volumetric-images
#18
Christopher J Moore, Tom Marchant
Reconstructive volumetric imaging permeates medical practice because of its apparently clear depiction of anatomy. However, the tell tale signs of abnormality and its delineation for treatment demand experts work at the threshold of visibility for hints of structure. Hitherto, a suitable assistive metric that chimes with clinical experience has been absent. This paper develops the complexity measure approximate entropy (ApEn) from its one-dimensional physiological origin into a three-dimensional (3D) algorithm to fill this gap...
May 30, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28538408/precision-of-quantitative-computed-tomography-texture-analysis-using-image-filtering-a-phantom-study-for-scanner-variability
#19
Koichiro Yasaka, Hiroyuki Akai, Dennis Mackin, Laurence Court, Eduardo Moros, Kuni Ohtomo, Shigeru Kiryu
Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors...
May 2017: Medicine (Baltimore)
https://www.readbyqxmd.com/read/28526813/radiomic-model-for-predicting-mutations-in-the-isocitrate-dehydrogenase-gene-in-glioblastomas
#20
Kevin Li-Chun Hsieh, Cheng-Yu Chen, Chung-Ming Lo
The present study proposed a computer-aided diagnosis system based on radiomic features extracted through magnetic resonance imaging to determine the isocitrate dehydrogenase status in glioblastomas. Magnetic resonance imaging data were obtained from 32 patients with wild-typeisocitrate dehydrogenase and 7 patients with mutant isocitrate dehydrogenase in glioblastomas. Radiomic features, namely morphological, intensity, and textural features, were extracted from the tumor area of each patient. The feature sets were evaluated using a logistic regression classifier to develop a prediction model...
May 3, 2017: Oncotarget
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