keyword
MENU ▼
Read by QxMD icon Read
search

Radiomic

keyword
https://www.readbyqxmd.com/read/28434856/can-ct-and-mr-shape-and-textural-features-differentiate-benign-versus-malignant-pleural-lesions
#1
Elena Pena, MacArinze Ojiaku, Joao R Inacio, Ashish Gupta, D Blair Macdonald, Wael Shabana, Jean M Seely, Frank J Rybicki, Carole Dennie, Rebecca E Thornhill
RATIONALE AND OBJECTIVES: The study aimed to identify a radiomic approach based on CT and or magnetic resonance (MR) features (shape and texture) that may help differentiate benign versus malignant pleural lesions, and to assess if the radiomic model may improve confidence and accuracy of radiologists with different subspecialty backgrounds. MATERIALS AND METHODS: Twenty-nine patients with pleural lesions studied on both contrast-enhanced CT and MR imaging were reviewed retrospectively...
April 20, 2017: Academic Radiology
https://www.readbyqxmd.com/read/28423406/the-rise-of-radiomics-and-implications-for-oncologic-management
#2
Vivek Verma, Charles B Simone, Sunil Krishnan, Steven H Lin, Jinzhong Yang, Stephen M Hahn
Clinical medicine, particularly oncology, is progressing toward personalized care. Whereas the terms genomics, proteomics, transcriptomics, and metabolomics have dominated personalized medicine for the past couple decades, the concept of radiomics was first described in 2012. This nascent concept has major implications for personalized cancer care and involves extracting hundreds of standardized and quantifiable imaging characteristics from diagnostic computed tomography/magnetic resonance imaging images. The central hypothesis of radiomics is that these libraries of quantitative individual voxel-based variables are more sensitively associated with various clinical endpoints compared with the more qualitative radiologic, histopathologic, and clinical data more commonly utilized today...
July 1, 2017: Journal of the National Cancer Institute
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/28421244/texture-analysis-as-a-radiomic-marker-for-differentiating-renal-tumors
#4
HeiShun Yu, Jonathan Scalera, Maria Khalid, Anne-Sophie Touret, Nicolas Bloch, Baojun Li, Muhammad M Qureshi, Jorge A Soto, Stephan W Anderson
PURPOSE: To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma. MATERIALS AND METHODS: Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed...
April 18, 2017: Abdominal Radiology
https://www.readbyqxmd.com/read/28418006/radiomics-based-prognosis-analysis-for-non-small-cell-lung-cancer
#5
Yucheng Zhang, Anastasia Oikonomou, Alexander Wong, Masoom A Haider, Farzad Khalvati
Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC)...
April 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28417933/radiogenomic-analysis-of-oncological-data-a-technical-survey
#6
REVIEW
Mariarosaria Incoronato, Marco Aiello, Teresa Infante, Carlo Cavaliere, Anna Maria Grimaldi, Peppino Mirabelli, Serena Monti, Marco Salvatore
In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment...
April 12, 2017: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28390536/integration-of-pet-mr-hybrid-imaging-into-radiation-therapy-treatment
#7
REVIEW
Tong Zhu, Shiva Das, Terence Z Wong
Hybrid PET/MR imaging is in early development for treatment planning. This article briefly reviews research and clinical applications of PET/MR imaging in radiation oncology. With improvements in workflow, more specific tracers, and fast and robust acquisition protocols, PET/MR imaging will play an increasingly important role in better target delineation for treatment planning and have clear advantages in the evaluation of tumor response and in a better understanding of tumor heterogeneity. With advances in treatment delivery and the potential of integrating PET/MR imaging with research on radiomics for radiation oncology, quantitative and physiologic information could lead to more precise and personalized RT...
May 2017: Magnetic Resonance Imaging Clinics of North America
https://www.readbyqxmd.com/read/28374077/machine-learning-based-analysis-of-mr-radiomics-can-help-to-improve-the-diagnostic-performance-of-pi-rads-v2-in-clinically-relevant-prostate-cancer
#8
Jing Wang, Chen-Jiang Wu, Mei-Ling Bao, Jing Zhang, Xiao-Ning Wang, Yu-Dong Zhang
OBJECTIVE: To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). METHODS: This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features...
April 3, 2017: European Radiology
https://www.readbyqxmd.com/read/28373718/delta-radiomics-features-for-the-prediction-of-patient-outcomes-in-non-small-cell-lung-cancer
#9
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/28361913/multi-scale-radiomic-analysis-of-sub-cortical-regions-in-mri-related-to-autism-gender-and-age
#10
Ahmad Chaddad, Christian Desrosiers, Matthew Toews
We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs...
March 31, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28349351/advanced-mri-techniques-in-the-monitoring-of-treatment-of-gliomas
#11
REVIEW
Harpreet Hyare, Steffi Thust, Jeremy Rees
With advances in treatments and survival of patients with glioblastoma (GBM), it has become apparent that conventional imaging sequences have significant limitations both in terms of assessing response to treatment and monitoring disease progression. Both 'pseudoprogression' after chemoradiation for newly diagnosed GBM and 'pseudoresponse' after anti-angiogenesis treatment for relapsed GBM are well-recognised radiological entities. This in turn has led to revision of response criteria away from the standard MacDonald criteria, which depend on the two-dimensional measurement of contrast-enhancing tumour, and which have been the primary measure of radiological response for over three decades...
March 2017: Current Treatment Options in Neurology
https://www.readbyqxmd.com/read/28346329/cardiac-computed-tomography-radiomics-a-comprehensive-review-on-radiomic-techniques
#12
Márton Kolossváry, Miklós Kellermayer, Béla Merkely, Pál Maurovich-Horvat
Radiologic images are vast three-dimensional data sets in which each voxel of the underlying volume represents distinct physical measurements of a tissue-dependent characteristic. Advances in technology allow radiologists to image pathologies with unforeseen detail, thereby further increasing the amount of information to be processed. Even though the imaging modalities have advanced greatly, our interpretation of the images has remained essentially unchanged for decades. We have arrived in the era of precision medicine where even slight differences in disease manifestation are seen as potential target points for new intervention strategies...
March 24, 2017: Journal of Thoracic Imaging
https://www.readbyqxmd.com/read/28339588/mri-features-predict-survival-and-molecular-markers-in-diffuse-lower-grade-gliomas
#13
Hao Zhou, Martin Vallières, Harrison X Bai, Chang Su, Haiyun Tang, Derek Oldridge, Zishu Zhang, Bo Xiao, Weihua Liao, Yongguang Tao, Jianhua Zhou, Paul Zhang, Li Yang
Background.: Previous studies have shown that MR imaging features can be used to predict survival and molecular profile of glioblastoma. However, no study of a similar type has been performed on lower-grade gliomas (LGGs). Methods.: Presurgical MRIs of 165 patients with diffuse low- and intermediate-grade gliomas (histological grades II and III) were scored according to the Visually Accessible Rembrandt Images (VASARI) annotations. Radiomic models using automated texture analysis and VASARI features were built to predict isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q codeletion status, histological grade, and tumor progression...
January 24, 2017: Neuro-oncology
https://www.readbyqxmd.com/read/28336974/pet-radiomics-in-nsclc-state-of-the-art-and-a-proposal-for-harmonization-of-methodology
#14
M Sollini, L Cozzi, L Antunovic, A Chiti, M Kirienko
Imaging with positron emission tomography (PET)/computed tomography (CT) is crucial in the management of cancer because of its value in tumor staging, response assessment, restaging, prognosis and treatment responsiveness prediction. In the last years, interest has grown in texture analysis which provides an "in-vivo" lesion characterization, and predictive information in several malignances including NSCLC; however several drawbacks and limitations affect these studies, especially because of lack of standardization in features calculation, definitions and methodology reporting...
March 23, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28331828/radiomics-of-pulmonary-nodules-and-lung-cancer
#15
REVIEW
Ryan Wilson, Anand Devaraj
The large number of indeterminate pulmonary nodules encountered incidentally or during CT-based lung screening provides considerable diagnostic and management challenges. Conventional nodule evaluation relies on visually identifiable discriminators such as size and speculation. These visible nodule features are however small in number and subject to considerable interpretation variability. With the development of novel targeted therapies for lung cancer the diagnosis and characterization of early stage lung tumours has never been more important...
February 2017: Translational Lung Cancer Research
https://www.readbyqxmd.com/read/28325604/early-prediction-of-radiotherapy-induced-parotid-shrinkage-and-toxicity-based-on-ct-radiomics-and-fuzzy-classification
#16
Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Alessia Farneti, Giovanni Mauro Cattaneo, Giovanna Rizzo, Massimo Esposito
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers...
March 18, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28325002/radiomic-analysis-of-multi-contrast-brain-mri-for-the-prediction-of-survival-in-patients-with-glioblastoma-multiforme
#17
Ahmad Chaddad, Christian Desrosiers, Matthew Toews
Image texture features are effective at characterizing the microstructure of cancerous tissues. This paper proposes predicting the survival times of glioblastoma multiforme (GBM) patients using texture features extracted in multi-contrast brain MRI images. Texture features are derived locally from contrast enhancement, necrosis and edema regions in T1-weighted post-contrast and fluid-attenuated inversion-recovery (FLAIR) MRIs, based on the gray-level co-occurrence matrix representation. A statistical analysis based on the Kaplan-Meier method and log-rank test is used to identify the texture features related with the overall survival of GBM patients...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28295386/an-integrated-segmentation-and-shape-based-classification-scheme-for-distinguishing-adenocarcinomas-from-granulomas-on-lung-ct
#18
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/28289945/a-novel-representation-of-inter-site-tumour-heterogeneity-from-pre-treatment-computed-tomography-textures-classifies-ovarian-cancers-by-clinical-outcome
#19
Hebert Alberto Vargas, Harini Veeraraghavan, Maura Micco, Stephanie Nougaret, Yulia Lakhman, Andreas A Meier, Ramon Sosa, Robert A Soslow, Douglas A Levine, Britta Weigelt, Carol Aghajanian, Hedvig Hricak, Joseph Deasy, Alexandra Snyder, Evis Sala
PURPOSE: To evaluate the associations between clinical outcomes and radiomics-derived inter-site spatial heterogeneity metrics across multiple metastatic lesions on CT in patients with high-grade serous ovarian cancer (HGSOC). METHODS: IRB-approved retrospective study of 38 HGSOC patients. All sites of suspected HGSOC involvement on preoperative CT were manually segmented. Gray-level correlation matrix-based textures were computed from each tumour site, and grouped into five clusters using a Gaussian Mixture Model...
March 13, 2017: European Radiology
https://www.readbyqxmd.com/read/28280088/radiomics-features-of-multiparametric-mri-as-novel-prognostic-factors-in-advanced-nasopharyngeal-carcinoma
#20
Shuixing Zhang, Bin Zhang, Jie Tian, Di Dong, Dong Sheng Gu, Yu Hao Dong, Lu Zhang, Zhou Yang Lian, Jing Liu, Xiao Ning Luo, Shu Fang Pei, Xiao Kai Mo, Wen Hui Huang, Fu Sheng Ouyang, Bao Liang Guo, Long Liang, Wenbo Chen, Chang H Liang
PURPOSE: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC). EXPERIMENTAL DESIGN: One-hundred and eighteen patients (training cohort: n = 88; validation cohort: n = 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms...
March 9, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
keyword
keyword
80326
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"