keyword
MENU ▼
Read by QxMD icon Read
search

Radiomics

keyword
https://www.readbyqxmd.com/read/28916879/a-score-combining-baseline-neutrophilia-and-primary-tumor-suvpeak-measured-from-fdg-pet-is-associated-with-outcome-in-locally-advanced-cervical-cancer
#1
Antoine Schernberg, Sylvain Reuze, Fanny Orlhac, Irène Buvat, Laurent Dercle, Roger Sun, Elaine Limkin, Alexandre Escande, Christine Haie-Meder, Eric Deutsch, Cyrus Chargari, Charlotte Robert
PURPOSE: We investigated whether a score combining baseline neutrophilia and a PET biomarker could predict outcome in patients with locally advanced cervical cancer (LACC). METHODS: Patients homogeneously treated with definitive chemoradiation plus image-guided adaptive brachytherapy (IGABT) between 2006 and 2013 were analyzed retrospectively. We divided patients into two groups depending on the PET device used: a training set (TS) and a validation set (VS). Primary tumors were semi-automatically delineated on PET images, and 11 radiomics features were calculated (LIFEx software)...
September 15, 2017: European Journal of Nuclear Medicine and Molecular Imaging
https://www.readbyqxmd.com/read/28915902/development-and-clinical-application-of-radiomics-in-lung-cancer
#2
REVIEW
Bojiang Chen, Rui Zhang, Yuncui Gan, Lan Yang, Weimin Li
Since the discovery of X-rays at the end of the 19(th) century, medical imageology has progressed for 100 years, and medical imaging has become an important auxiliary tool for clinical diagnosis. With the launch of the human genome project (HGP) and the development of various high-throughput detection techniques, disease exploration in the post-genome era has extended beyond investigations of structural changes to in-depth analyses of molecular abnormalities in tissues, organs and cells, on the basis of gene expression and epigenetics...
September 15, 2017: Radiation Oncology
https://www.readbyqxmd.com/read/28914549/age-groups-related-glioblastoma-study-based-on-radiomics-approach
#3
Zeju Li, Yuanyuan Wang, Jinhua Yu, Yi Guo, Qi Zhang
Glioblastoma is the most aggressive malignant brain tumor with poor prognosis. Radiomics is a newly emerging and promising technique to reveal the complex relationships between high-throughput medical image features and deep information of disease including pathology, biomarkers and genomics. An approach was developed to investigate the internal relationship between magnetic resonance imaging (MRI) features and the age-related origins of glioblastomas based on a quantitative radiomics method. A fully automatic image segmentation method was applied to segment the tumor regions from three dimensional MRI images...
September 15, 2017: Computer Assisted Surgery (Abingdon, England)
https://www.readbyqxmd.com/read/28900812/radiomic-features-predict-ki-67-expression-level-and-survival-in-lower-grade-gliomas
#4
Yiming Li, Zenghui Qian, Kaibin Xu, Kai Wang, Xing Fan, Shaowu Li, Xing Liu, Yinyan Wang, Tao Jiang
To investigate the radiomic features associated with Ki-67 expression in lower grade gliomas and assess the prognostic values of these features. Patients with lower grade gliomas (n = 117) were randomly assigned into the training (n = 78) and validation (n = 39) sets. A total of 431 radiological features were extracted from each patient. Differential radiological features between the low and high Ki-67 expression groups were screened by significance analysis of microarrays. Then, generalized linear analysis was performed to select features that could predict the Ki-67 expression level...
September 12, 2017: Journal of Neuro-oncology
https://www.readbyqxmd.com/read/28899058/distinct-radiomic-phenotypes-define-glioblastoma-tp53-pten-egfr-mutational-landscape
#5
Pascal O Zinn, Sanjay K Singh, Aikaterini Kotrotsou, Srishti Abrol, Ginu Thomas, Jennifer Mosley, Ahmed Elakkad, Islam Hassan, Ashok Kumar, Rivka R Colen
No abstract text is available yet for this article.
September 1, 2017: Neurosurgery
https://www.readbyqxmd.com/read/28898189/ct-texture-analysis-definitions-applications-biologic-correlates-and-challenges
#6
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/28891217/reproducibility-of-f18-fdg-pet-radiomic-features-for-different-cervical-tumor-segmentation-methods-gray-level-discretization-and-reconstruction-algorithms
#7
Baderaldeen A Altazi, Geoffrey G Zhang, Daniel C Fernandez, Michael E Montejo, Dylan Hunt, Joan Werner, Matthew C Biagioli, Eduardo G Moros
Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from (18) Flourine-fluorodeoxyglucose ((18) F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV1 and MTV2 ) for each patient...
September 11, 2017: Journal of Applied Clinical Medical Physics
https://www.readbyqxmd.com/read/28890183/radiomics-analysis-on-ultrasound-for-prediction-of-biologic-behavior-in-breast-invasive-ductal-carcinoma
#8
Yi Guo, Yuzhou Hu, Mengyun Qiao, Yuanyuan Wang, Jinhua Yu, Jiawei Li, Cai Chang
INTRODUCTION: In current clinical practice, invasive ductal carcinoma is always screened using medical imaging techniques and diagnosed using immunohistochemistry. Recent studies have illustrated that radiomics approaches provide a comprehensive characterization of entire tumors and can reveal predictive or prognostic associations between the images and medical outcomes. To better reveal the underlying biology, an improved understanding between objective image features and biologic characteristics is urgently required...
August 18, 2017: Clinical Breast Cancer
https://www.readbyqxmd.com/read/28885084/influence-of-gray-level-discretization-on-radiomic-feature-stability-for-different-ct-scanners-tube-currents-and-slice-thicknesses-a-comprehensive-phantom-study
#9
Ruben T H M Larue, Janna E van Timmeren, Evelyn E C de Jong, Giacomo Feliciani, Ralph T H Leijenaar, Wendy M J Schreurs, Meindert N Sosef, Frank H P J Raat, Frans H R van der Zande, Marco Das, Wouter van Elmpt, Philippe Lambin
BACKGROUND: Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity of acquisition- and reconstruction protocols influences robustness of radiomic analyses. The aim of this study was to investigate the influence of different CT-scanners, slice thicknesses, exposures and gray-level discretization on radiomic feature values and their stability. MATERIAL AND METHODS: A texture phantom with ten different inserts was scanned on nine different CT-scanners with varying tube currents...
September 8, 2017: Acta Oncologica
https://www.readbyqxmd.com/read/28876221/rethinking-the-role-of-clinical-imaging
#10
James Pb O'Connor
Radiomics has the potential to improve the management of cancer patients, but further research is required before it can be adopted into routine clinical practice.
September 6, 2017: ELife
https://www.readbyqxmd.com/read/28874676/linc-ing-circulating-long-non-coding-rnas-to-the-diagnosis-and-malignant-prediction-of-intraductal-papillary-mucinous-neoplasms-of-the-pancreas
#11
Jennifer B Permuth, Dung-Tsa Chen, Sean J Yoder, Jiannong Li, Andrew T Smith, Jung W Choi, Jongphil Kim, Yoganand Balagurunathan, Kun Jiang, Domenico Coppola, Barbara A Centeno, Jason Klapman, Pam Hodul, Florian A Karreth, Jose G Trevino, Nipun Merchant, Anthony Magliocco, Mokenge P Malafa, Robert Gillies
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease that lacks effective biomarkers for early detection. We hypothesized that circulating long non-coding RNAs (lncRNAs) may act as diagnostic markers of incidentally-detected cystic PDAC precursors known as intraductal papillary mucinous neoplasms (IPMNs) and predictors of their pathology/histological classification. Using NanoString nCounter® technology, we measured the abundance of 28 candidate lncRNAs in pre-operative plasma from a cohort of pathologically-confirmed IPMN cases of various grades of severity and non-diseased controls...
September 5, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28874414/a-radiomics-nomogram-for-the-preoperative-prediction-of-lymph-node-metastasis-in-bladder-cancer
#12
Shaoxu Wu, Junjiong Zheng, Yong Li, Hao Yu, Siya Shi, Weibin Xie, Hao Liu, Yangfan Su, Jian Huang, Tianxin Lin
PURPOSE: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in bladder cancer. EXPERIMENTAL DESIGN: One hundred and eighteen eligible bladder cancer patients were divided into a training set (n =80) and a validation set (n =38). Radiomics features were extracted from arterial-phase computed tomography (CT) images of each patient. A radiomics signature was then constructed with the least absolute shrinkage and selection operator algorithm in the training set...
September 5, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
https://www.readbyqxmd.com/read/28872634/advancing-the-cancer-genome-atlas-glioma-mri-collections-with-expert-segmentation-labels-and-radiomic-features
#13
Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin S Kirby, John B Freymann, Keyvan Farahani, Christos Davatzikos
Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA)...
September 5, 2017: Scientific Data
https://www.readbyqxmd.com/read/28872054/enhancement-of-multimodality-texture-based-prediction-models-via-optimization-of-pet-and-mr-image-acquisition-protocols-a-proof-of-concept
#14
Martin Vallières, Sébastien Laberge, André Diamant, Issam El Naqa
Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models...
September 5, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28871110/a-deep-learning-based-radiomics-model-for-prediction-of-survival-in-glioblastoma-multiforme
#15
Jiangwei Lao, Yinsheng Chen, Zhi-Cheng Li, Qihua Li, Ji Zhang, Jing Liu, Guangtao Zhai
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images...
September 4, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28869399/clinical-applications-of-textural-analysis-in-non-small-cell-lung-cancer
#16
Iain Phillips, Mazhar Ajaz, Veni Ezhil, Vineet Prakash, Sheaka Alobaidli, Sarah J McQuaid, Christopher South, James Scuffham, Andrew Nisbet, Philip Evans
Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed 'radiomics' and includes semantic and agnostic approaches. Texture Analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour...
September 4, 2017: British Journal of Radiology
https://www.readbyqxmd.com/read/28865968/-computational-medical-imaging-radiomics-and-potential-for-immuno-oncology
#17
R Sun, E J Limkin, L Dercle, S Reuzé, E I Zacharaki, C Chargari, A Schernberg, A S Dirand, A Alexis, N Paragios, É Deutsch, C Ferté, C Robert
The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes...
August 30, 2017: Cancer Radiothérapie: Journal de la Société Française de Radiothérapie Oncologique
https://www.readbyqxmd.com/read/28864046/-radiotherapy-for-head-and-neck-squamous-cell-carcinoma-state-of-the-art-and-future-directions
#18
U Schick, F Huguet, Y Pointreau, O Pradier
Therapeutic principles of radiation therapy in head and neck carcinomas will be discussed in this review. Intensity-modulated radiotherapy with concomitant cisplatin should be standard. In case of contraindication to chemotherapy, cetuximab is an option, while hyperfractionation should be considered in patients unfit for concomitant treatment. Concomitant chemotherapy should be administered in the presence of extracapsular extensions and positive margins in the postoperative setting. Current research areas such as desescalation in human papillomavirus-positive tumours, adaptive radiotherapy, radiomics and immunotherapy will also be addressed...
August 29, 2017: Cancer Radiothérapie: Journal de la Société Française de Radiothérapie Oncologique
https://www.readbyqxmd.com/read/28860628/radiomics-strategies-for-risk-assessment-of-tumour-failure-in-head-and-neck-cancer
#19
Martin Vallières, Emily Kay-Rivest, Léo Jean Perrin, Xavier Liem, Christophe Furstoss, Hugo J W L Aerts, Nader Khaouam, Phuc Felix Nguyen-Tan, Chang-Shu Wang, Khalil Sultanem, Jan Seuntjens, Issam El Naqa
Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer...
August 31, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28853237/pixel-classification-method-in-optical-coherence-tomography-for-tumor-segmentation-and-its-complementary-usage-with-oct-microangiography
#20
Alexander Moiseev, Ludmila Snopova, Sergey Kuznetsov, Natalia Buyanova, Vadim Elagin, Marina Sirotkina, Elena Kiseleva, Lev Matveev, Vladimir Zaytsev, Felix Feldchtein, Elena Zagaynova, Valentin Gelikonov, Natalia Gladkova, Alex Vitkin, Grigory Gelikonov
A novel machine-learning method to distinguish between tumor and normal tissue in optical coherence tomography (OCT) has been developed. Pre-clinical murine ear model implanted with mouse colon carcinoma CT-26 was used. Structural-image-based feature sets were defined for each pixel and machine learning classifiers were trained using "ground truth" OCT images manually segmented by comparison with histology. The accuracy of the OCT tumour segmentation method was then quantified by comparing with fluorescence imaging of tumors expressing genetically encoded fluorescent protein KillerRed that clearly delineates tumor borders...
August 29, 2017: Journal of Biophotonics
keyword
keyword
21935
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"