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Olena Weaver, Jessica W T Leung
OBJECTIVE: The goals of this review are to provide background information on the definitions and applications of the general term "biomarker" and to highlight the specific roles of breast imaging biomarkers in research and clinical breast cancer care. A search was conducted of the main electronic biomedical databases (PubMed, Cochrane, Embase, MEDLINE [Ovid], Scopus, and Web of Science). The search was focused on review literature in general radiology and biomedical sciences and on reviews and primary research articles on biomarkers in breast imaging over the 15 years ending in June 2017...
November 22, 2017: AJR. American Journal of Roentgenology
Huaiqiang Sun, Ying Chen, Qiang Huang, Su Lui, Xiaoqi Huang, Yan Shi, Xin Xu, John A Sweeney, Qiyong Gong
Purpose To identify cerebral radiomic features related to diagnosis and subtyping of attention deficit hyperactivity disorder (ADHD) and to build and evaluate classification models for ADHD diagnosis and subtyping on the basis of the identified features. Materials and Methods A consecutive cohort of 83 age- and sex-matched children with newly diagnosed and never-treated ADHD (mean age 10.83 years ± 2.30; range, 7-14 years; 71 boys, 40 with ADHD-inattentive [ADHD-I] and 43 with ADHD-combined [ADHD-C, or inattentive and hyperactive]) and 87 healthy control subjects (mean age, 11...
November 22, 2017: Radiology
Mannudeep Kalra, Ge Wang, Colin G Orton
Of the 8.8 million deaths from cancer in 2015, per the World Health Organization, lung cancer was the most common cause of death (1.69 million). With the introduction of low-dose CT for lung cancer screening and emergence of precision medicine for often-fatal lung cancer, there is a need for deep access to critical information, i.e. radiomics, not possible with conventional subjective interpretation of CT images. Quantitative image analyses for radiomics have been applied in lung cancers to determine their spatial complexity, genomic composition, viability or aggressiveness, and responses to targeted therapies...
November 21, 2017: Medical Physics
Hongyu Zhou, Di Dong, Bojiang Chen, Mengjie Fang, Yue Cheng, Yuncun Gan, Rui Zhang, Liwen Zhang, Yali Zang, Zhenyu Liu, Hairong Zheng, Weimin Li, Jie Tian
OBJECTIVES: To analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer. METHODS: This was a retrospective analysis of 348 patients with lung cancer enrolled between 2014 and February 2015. A feature set containing clinical features and 485 radiomic features was extracted from the pretherapy CT images. Feature selection via concave minimization (FSV) was used to select effective features. A support vector machine (SVM) was used to evaluate the predictive ability of each feature...
November 17, 2017: Translational Oncology
Stephen S F Yip, Chintan Parmar, John Kim, Elizabeth Huynh, Raymond H Mak, Hugo J W L Aerts
PURPOSE: PET-based radiomic features have demonstrated great promises in predicting genetic data. However, various experimental parameters can influence the feature extraction pipeline, and hence, Here, we investigated how experimental settings affect the performance of radiomic features in predicting somatic mutation status in non-small cell lung cancer (NSCLC) patients. METHODS: 348 NSCLC patients with somatic mutation testing and diagnostic PET images were included in our analysis...
December 2017: European Journal of Radiology
Vishwa S Parekh, Michael A Jacobs
Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors...
2017: NPJ Breast Cancer
C Le Fèvre, L Poty, G Noël
The many advances in data collection computing systems (data collection, database, storage), diagnostic and therapeutic possibilities are responsible for an increase and a diversification of available data. Big data offers the capacities, in the field of health, to accelerate the discoveries and to optimize the management of patients by combining a large volume of data and the creation of therapeutic models. In radiotherapy, the development of big data is attractive because data are very numerous et heterogeneous (demographics, radiomics, genomics, radiogenomics, etc...
November 14, 2017: Cancer Radiothérapie: Journal de la Société Française de Radiothérapie Oncologique
Thibaud P Coroller, Wenya Linda Bi, Elizabeth Huynh, Malak Abedalthagafi, Ayal A Aizer, Noah F Greenwald, Chintan Parmar, Vivek Narayan, Winona W Wu, Samuel Miranda de Moura, Saksham Gupta, Rameen Beroukhim, Patrick Y Wen, Ossama Al-Mefty, Ian F Dunn, Sandro Santagata, Brian M Alexander, Raymond Y Huang, Hugo J W L Aerts
OBJECTIVES: The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making. METHODS: A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype...
2017: PloS One
Marina Romanchikova, Karl Harrison, Neil G Burnet, Andrew Fc Hoole, Michael Pf Sutcliffe, Michael Andrew Parker, Rajesh Jena, Simon J Thomas
OBJECTIVE: To enable fast and customisable automated collection of radiotherapy data from tomotherapy storage. METHODS: Human-readable data maps (TagMaps) were created to generate DICOM-RT data from tomotherapy archives, and provided access to "hidden" information comprising delivery sinograms, positional corrections and adaptive-RT doses. RESULTS: 797 datasets totalling 25,000 scans were batch-exported in 31.5 hours. All archived information was restored, including the data not available via commercial software...
November 10, 2017: British Journal of Radiology
Marta Bogowicz, Ralph T H Leijenaar, Stephanie Tanadini-Lang, Oliver Riesterer, Martin Pruschy, Gabriela Studer, Jan Unkelbach, Matthias Guckenberger, Ender Konukoglu, Philippe Lambin
PURPOSE: This study investigated an association of post-radiochemotherapy (RCT) PET radiomics with local tumor control in head and neck squamous cell carcinoma (HNSCC) and evaluated the models against two radiomics software implementations. MATERIALS AND METHODS: 649 features, available in two radiomics implementations and based on the same definitions, were extracted from HNSCC primary tumor region in 18F-FDG PET scans 3 months post definitive RCT (training cohort n = 128, validation cohort n = 50) and compared using the intraclass correlation coefficient (ICC)...
November 6, 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Maliazurina Saad, Tae-Sun Choi
BACKGROUND: Tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels. They are categorized in terms of a differentiated appearance under a microscope. Non-small-cell lung cancer tumors are categorized into three main subgroups: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In approximately 20% of pathology reports, they are returned unclassified or classified as not-otherwise-specified (NOS) owing to scant materials or poor tumor differentiation...
October 31, 2017: Computers in Biology and Medicine
Hyungjin Kim, Chang Min Park, Bhumsuk Keam, Sang Joon Park, Miso Kim, Tae Min Kim, Dong-Wan Kim, Dae Seog Heo, Jin Mo Goo
PURPOSE: To determine if the radiomic features on CT can predict progression-free survival (PFS) in epidermal growth factor receptor (EGFR) mutant adenocarcinoma patients treated with first-line EGFR tyrosine kinase inhibitors (TKIs) and to identify the incremental value of radiomic features over conventional clinical factors in PFS prediction. METHODS: In this institutional review board-approved retrospective study, pretreatment contrast-enhanced CT and first follow-up CT after initiation of TKIs were analyzed in 48 patients (M:F = 23:25; median age: 61 years)...
2017: PloS One
Qiao Huang, Lin Lu, Laurent Dercle, Philip Lichtenstein, Yajun Li, Qian Yin, Min Zong, Lawrence Schwartz, Binsheng Zhao
Radiomic features characterize tumor imaging phenotype. Nonsmall cell lung cancer (NSCLC) tumors are known for their complexity in shape and wide range in density. We explored the effects of variable tumor contouring on the prediction of epidermal growth factor receptor (EGFR) mutation status by radiomics in NSCLC patients treated with a targeted therapy (Gefitinib). Forty-six early stage NSCLC patients (EGFR mutant:wildtype = 20:26) were included. Three experienced radiologists independently delineated the tumors using a semiautomated segmentation software on a noncontrast-enhanced baseline and three-week post-therapy CT scan images that were reconstructed using 1...
January 2018: Journal of Medical Imaging
Hugo Jwl Aerts
Artificial intelligence (AI), especially deep learning, has the potential to fundamentally alter clinical radiology. AI algorithms, which excel in quantifying complex patterns in data, have shown remarkable progress in applications ranging from self-driving cars to speech recognition. The AI application within radiology, known as radiomics, can provide detailed quantifications of the radiographic characteristics of underlying tissues. This information can be used throughout the clinical care path to improve diagnosis and treatment planning, as well as assess treatment response...
November 2, 2017: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
Joost J M van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina G H Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, Hugo J W L Aerts
Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images...
November 1, 2017: Cancer Research
Rivka R Colen, Takeo Fujii, Mehmet Asim Bilen, Aikaterini Kotrotsou, Srishti Abrol, Kenneth R Hess, Joud Hajjar, Maria E Suarez-Almazor, Anas Alshawa, David S Hong, Dunia Giniebra-Camejo, Bettzy Stephen, Vivek Subbiah, Ajay Sheshadri, Tito Mendoza, Siqing Fu, Padmanee Sharma, Funda Meric-Bernstam, Aung Naing
We present the first reported work that explores the potential of radiomics to predict patients who are at risk for developing immunotherapy-induced pneumonitis. Despite promising results with immunotherapies, immune-related adverse events (irAEs) are challenging. Although less common, pneumonitis is a potentially fatal irAE. Thus, early detection is critical for improving treatment outcomes; an urgent need to identify biomarkers that predict patients at risk for pneumonitis exists. Radiomics, an emerging field, is the automated extraction of high fidelity, high-dimensional imaging features from standard medical images and allows for comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment...
October 27, 2017: Investigational New Drugs
Ahmed E Fetit, Jan Novak, Daniel Rodriguez, Dorothee P Auer, Christopher A Clark, Richard G Grundy, Andrew C Peet, Theodoros N Arvanitis
Brain tumours are the most common solid cancers in children in the UK and are the most common cause of cancer deaths in this age group. Despite current advances in MRI, non-invasive diagnosis of paediatric brain tumours has yet to find its way into routine clinical practice. Radiomics, the high-throughput extraction and analysis of quantitative image features (e.g. texture), offers potential solutions for tumour characterization and decision support. In the search for diagnostic oncological markers, the primary aim of this work was to study the application of MRI texture analysis (TA) for the classification of paediatric brain tumours...
October 26, 2017: NMR in Biomedicine
Bin Zhang, Fusheng Ouyang, Dongsheng Gu, Yuhao Dong, Lu Zhang, Xiaokai Mo, Wenhui Huang, Shuixing Zhang
We aimed to investigate the potential of radiomic features of magnetic resonance imaging (MRI) to predict progression in patients with advanced nasopharyngeal carcinoma (NPC). One hundred and thirteen consecutive patients (01/2007-07/2013) (training cohort: n = 80; validation cohort: n = 33) with advanced NPC were enrolled. A total of 970 initial features were extracted from T2-weighted (T2-w) (n = 485) and contrast-enhanced T1-weighted (CET1-w) MRI (n = 485) for each patient. We used least absolute shrinkage and selection operator (Lasso) method to select features that were most significantly associated with the progression...
September 22, 2017: Oncotarget
Ya-Ping Wu, Yu-Song Lin, Wei-Guo Wu, Cong Yang, Jian-Qin Gu, Yan Bai, Mei-Yun Wang
Brain tumor segmentation is the first and the most critical step in clinical applications of radiomics. However, segmenting brain images by radiologists is labor intense and prone to inter- and intraobserver variability. Stable and reproducible brain image segmentation algorithms are thus important for successful tumor detection in radiomics. In this paper, we propose a supervised brain image segmentation method, especially for magnetic resonance (MR) brain images with glioma. This paper uses hard edge multiplicative intrinsic component optimization to preprocess glioma medical image on the server side, and then, the doctors could supervise the segmentation process on mobile devices in their convenient time...
2017: Journal of Healthcare Engineering
Hwan-Ho Cho, Hyunjin Park
Gliomas are primary brain tumors arising from glial cells. Gliomas can be classified into different histopathologic grades according to World Health Oraganization (WHO) grading system which represents malignancy. In this paper, we present a method to predict the grades of Gliomas using Radiomics imaging features. MICCAI Brain Tumor Segmentation Challenge (BRATs 2015) training data, its segmentation ground truth and the ground truth labels were used for this work. 45 radiomics features based on histogram, shape and gray-level co-occurrence matrix (GLCM) were extracted from each FLAIR, T1, T1-Contrast, T2 image to quantify the property of Gliomas...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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