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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
Tongtong Liu, Guoqing Wu, Jinhua Yu, Yi Guo, Yuanyuan Wang, Zhifeng Shi, Liang Chen
Radiomics can convert digital images to mineable data by extracting a huge number of image features. Because of the high dimensions of radiomics features, feature selection is a very important step which affects the performance of the final prediction or classification. In this paper, we propose a feature selection criterion for radiomics analysis of glioma based on Magnetic Resonance Image (MRI). The proposed method named as minimum Redundancy, Maximum Relevance and Maximum Sparse Representation Coefficient (mRMRMSRC) criterion, which take three factors into consideration at the same time: relevance between features and labels with or without the influence of all other features, and redundancy between each couple of features...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Marco Bologna, Eros Montin, Valentina D A Corino, Luca T Mainardi
Radiomics extracts a large number of features from medical images to perform a quantitative characterization. Aim of this study was to assess radiomic features stability and relevance. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of 18 patients diagnosed with soft-tissue sarcomas (STSs). Thirty-seven intensity-based features were computed on the regions of interest (ROIs). First, ROIs of the images were subjected to translations and rotations in specific ranges...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Rafael Ortiz-Ramon, Andres Larroza, Estanislao Arana, David Moratal
Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Durgesh Kumar Dwivedi, Yonatan Chatzinoff, Yue Zhang, Qing Yuan, Michael Fulkerson, Rajiv Chopra, James Brugarolas, Jeffrey A Cadeddu, Payal Kapur, Ivan Pedrosa
OBJECTIVE: To implement a platform for co-localization of in vivo quantitative multi-parametric magnetic resonance imaging (mpMRI) features with ex vivo surgical specimens of patients with renal masses using patient-specific 3D-printed tumor molds, which may aid in targeted tissue procurement and radiomics/radiogenomic analyses. METHODS: Volumetric segmentation of 6 renal masses was performed with 3D Slicer ( to create a three-dimensional (3D) tumor model...
October 19, 2017: Urology
Pierre Lovinfosse, Marc Polus, Daniel Van Daele, Philippe Martinive, Frédéric Daenen, Mathieu Hatt, Dimitris Visvikis, Benjamin Koopmansch, Frédéric Lambert, Carla Coimbra, Laurence Seidel, Adelin Albert, Philippe Delvenne, Roland Hustinx
PURPOSE: The aim of this study was to investigate the prognostic value of baseline (18)F-FDG PET/CT textural analysis in locally-advanced rectal cancer (LARC). METHODS: Eighty-six patients with LARC underwent (18)F-FDG PET/CT before treatment. Maximum and mean standard uptake values (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), histogram-intensity features, as well as 11 local and regional textural features, were evaluated...
October 18, 2017: European Journal of Nuclear Medicine and Molecular Imaging
Stephen R Bowen, William T C Yuh, Daniel S Hippe, Wei Wu, Savannah C Partridge, Saba Elias, Guang Jia, Zhibin Huang, George A Sandison, Dennis Nelson, Michael V Knopp, Simon S Lo, Paul E Kinahan, Nina A Mayr
BACKGROUND: Robust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy. PURPOSE: To conduct a technical exploration of longitudinal changes in tumor heterogeneity patterns on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI) and FDG positron emission tomography / computed tomography (PET/CT), and their association to radiation therapy (RT) response in cervical cancer...
October 16, 2017: Journal of Magnetic Resonance Imaging: JMRI
Stefan Leger, Alex Zwanenburg, Karoline Pilz, Fabian Lohaus, Annett Linge, Klaus Zöphel, Jörg Kotzerke, Andreas Schreiber, Inge Tinhofer, Volker Budach, Ali Sak, Martin Stuschke, Panagiotis Balermpas, Claus Rödel, Ute Ganswindt, Claus Belka, Steffi Pigorsch, Stephanie E Combs, David Mönnich, Daniel Zips, Mechthild Krause, Michael Baumann, Esther G C Troost, Steffen Löck, Christian Richter
Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma...
October 16, 2017: Scientific Reports
Philipp Kickingereder, Ulf Neuberger, David Bonekamp, Paula L Piechotta, Michael Götz, Antje Wick, Martin Sill, Annekathrin Kratz, Russell T Shinohara, David T W Jones, Alexander Radbruch, John Muschelli, Andreas Unterberg, Jürgen Debus, Heinz-Peter Schlemmer, Christel Herold-Mende, Stefan Pfister, Andreas von Deimling, Wolfgang Wick, David Capper, Klaus H Maier-Hein, Martin Bendszus
Background: To analyze the potential of radiomics for disease stratification beyond key molecular, clinical and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n=1043) were extracted from the multiparametric MRI of 181 patients with newly-diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI-test-retest cohort) and selected for analysis...
September 28, 2017: Neuro-oncology
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