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https://www.readbyqxmd.com/read/28731408/defining-the-biological-basis-of-radiomic-phenotypes-in-lung-cancer
#1
Patrick Grossmann, Olya Stringfield, Nehme El-Hachem, Marilyn M Bui, Emmanuel Rios Velazquez, Chintan Parmar, Ralph Th Leijenaar, Benjamin Haibe-Kains, Philippe Lambin, Robert Gillies, Hugo Jwl Aerts
Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors...
July 21, 2017: ELife
https://www.readbyqxmd.com/read/28722083/-a-perception-of-the-development-trend-of-radiological-t-staging-in-gastric-cancer-through-uicc-ajcc-8-th-edition
#2
Lei Tang
More and more emphases were put on pre-therapy staging following the promotion of multidisciplinary team(MDT) collaboration and individualized medicine in gastric cancer. However, the accuracy of traditional radiology staging system which applies mechanically pathological staging is not satisfactory during clinical practice, therefore, the objectivity of treatment decisions is affected. Under such conditions, the newly introduced staging system from Union for International Cancer Control/American Joint Committee on Cancer(UICC/AJCC)(8(th) edition) divided out an independent cTNM system for the first time, and summarized the application range, operation specification and staging criteria...
July 25, 2017: Zhonghua Wei Chang Wai Ke za Zhi, Chinese Journal of Gastrointestinal Surgery
https://www.readbyqxmd.com/read/28717412/diffusion-magnetic-resonance-imaging-a-molecular-imaging-tool-caught-between-hope-hype-and-the-real-world-of-personalized-oncology
#3
REVIEW
Abhishek Mahajan, Sneha S Deshpande, Meenakshi H Thakur
"Personalized oncology" is a multi-disciplinary science, which requires inputs from various streams for optimal patient management. Humongous progress in the treatment modalities available and the increasing need to provide functional information in addition to the morphological data; has led to leaping progress in the field of imaging. Magnetic resonance imaging has undergone tremendous progress with various newer MR techniques providing vital functional information and is becoming the cornerstone of "radiomics/radiogenomics"...
June 28, 2017: World Journal of Radiology
https://www.readbyqxmd.com/read/28712700/radiomic-analysis-of-dce-mri-for-prediction-of-response-to-neoadjuvant-chemotherapy-in-breast-cancer-patients
#4
Ming Fan, Guolin Wu, Hu Cheng, Juan Zhang, Guoliang Shao, Lihua Li
OBJECTIVES: To enhance the accurate prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer patients by using a quantitative analysis of dynamic enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: A dataset of 57 cancer patients with breast DCE-MR images acquired before NAC was used. Among them, 47 patients were Responders, and 10 patients were non-Responders based on the RECIST criteria. The breast regions were segmented on the MR images, and a total of 158 radiomic features were computed to represent the morphologic, dynamic, and the texture of the tumors as well as the background parenchymal features...
June 28, 2017: European Journal of Radiology
https://www.readbyqxmd.com/read/28711211/advanced-tissue-characterization-and-texture-analysis-using-dual-energy-computed-tomography-horizons-and-emerging-applications
#5
REVIEW
Reza Forghani, Ashok Srinivasan, Behzad Forghani
In the last article of this issue, advanced analysis capabilities of DECT is reviewed, including spectral Hounsfield unit attenuation curves, virtual monochromatic images, material decomposition maps, tissue effective Z determination, and other advanced post-processing DECT tools, followed by different methods of analysis of the attenuation curves generated using DECT. The article concludes with exciting future horizons and potential applications, such as the use of the rich quantitative data in dual energy CT scans for texture or radiomic analysis and the use of machine learning methods for generation of prediction models using spectral data...
August 2017: Neuroimaging Clinics of North America
https://www.readbyqxmd.com/read/28710497/deep-learning-based-radiomics-dlr-and-its-usage-in-noninvasive-idh1-prediction-for-low-grade-glioma
#6
Zeju Li, Yuanyuan Wang, Jinhua Yu, Yi Guo, Wei Cao
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN...
July 14, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28708732/investigating-the-robustness-neighborhood-gray-tone-difference-matrix-and-gray-level-co-occurrence-matrix-radiomic-features-on-clinical-computed-tomography-systems-using-anthropomorphic-phantoms-evidence-from-a-multivendor-study
#7
Usman Mahmood, Aditya P Apte, Joseph O Deasy, C Ross Schmidtlein, Amita Shukla-Dave
OBJECTIVE: The aim of this study was to determine if optimized imaging protocols across multiple computed tomography (CT) vendors could result in reproducible radiomic features calculated from an anthropomorphic phantom. METHODS: Materials with varying degrees of heterogeneity were placed throughout the lungs of the phantom. Twenty scans of the phantom were acquired on 3 CT manufacturers with chest CT protocols that had optimized protocol parameters. Scans were reconstructed using vendor-specific standards and lung kernels...
July 13, 2017: Journal of Computer Assisted Tomography
https://www.readbyqxmd.com/read/28693537/erratum-to-intratumoral-and-peritumoral-radiomics-for-the-pretreatment-prediction-of-pathological-complete-response-to-neoadjuvant-chemotherapy-based-on-breast-dce-mri
#8
Nathaniel M Braman, Maryam Etesami, Prateek Prasanna, Christina Dubchuk, Hannah Gilmore, Pallavi Tiwari, Donna Plecha, Anant Madabhushi
No abstract text is available yet for this article.
July 10, 2017: Breast Cancer Research: BCR
https://www.readbyqxmd.com/read/28689314/overview-of-deep-learning-in-medical-imaging
#9
REVIEW
Kenji Suzuki
The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification...
July 8, 2017: Radiological Physics and Technology
https://www.readbyqxmd.com/read/28688683/predictive-and-prognostic-value-of-ct-based-radiomics-signature-in-locally-advanced-head-and-neck-cancers-patients-treated-with-concurrent-chemoradiotherapy-or-bioradiotherapy-and-its-added-value-to-human-papillomavirus-status
#10
Dan Ou, Pierre Blanchard, Silvia Rosellini, Antonin Levy, France Nguyen, Ralph T H Leijenaar, Ingrid Garberis, Philippe Gorphe, François Bidault, Charles Ferté, Charlotte Robert, Odile Casiraghi, Jean-Yves Scoazec, Philippe Lambin, Stephane Temam, Eric Deutsch, Yungan Tao
OBJECTIVES: To explore prognostic and predictive value of radiomics in patients with locally advanced head and neck squamous cell carcinomas (LAHNSCC) treated with concurrent chemoradiotherapy (CRT) or bioradiotherapy (BRT). MATERIALS AND METHODS: Data of 120 patients (CRT vs. BRT matched 2:1) were retrospectively analyzed. A total of 544 radiomics features of the primary tumor were extracted from radiotherapy planning computed tomography scans. Cox proportional hazards models were used to examine the association between survival and radiomics features with false discovery rate correction...
August 2017: Oral Oncology
https://www.readbyqxmd.com/read/28687979/-radio-oncomics-the-potential-of-radiomics-in-radiation-oncology
#11
Jan Caspar Peeken, Fridtjof Nüsslin, Stephanie E Combs
INTRODUCTION: Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. METHODS: After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features...
July 7, 2017: Strahlentherapie und Onkologie: Organ der Deutschen Röntgengesellschaft ... [et Al]
https://www.readbyqxmd.com/read/28681390/a-deep-feature-fusion-methodology-for-breast-cancer-diagnosis-demonstrated-on-three-imaging-modality-datasets
#12
Natalia Antropova, Benjamin Q Huynh, Maryellen L Giger
Deep learning methods for radiomics/computer-aided diagnosis (CADx) are often prohibited by small datasets, long computation time, and the need for extensive image preprocessing. We present a methodology that extracts and pools low- to mid-level features using a pre-trained convolutional neural network and fuses them with handcrafted radiomic features computed using conventional CADx methods. Our fusion-based method demonstrates significant improvements to previous breast cancer CADx methods across three clinical imaging modalities (dynamic contrast-enhanced MRI, full-field digital mammography, and ultrasound) in terms of predictive performance in the task of estimating lesion malignancy...
July 6, 2017: Medical Physics
https://www.readbyqxmd.com/read/28678022/eigentumors-for-prediction-of-treatment-failure-in-patients-with-early-stage-breast-cancer-using-dynamic-contrast-enhanced-mri-a-feasibility-study
#13
Hui Shan M Chan, Bas H M van der Velden, Claudette E Loo, Kenneth G A Gilhuijs
We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data...
July 5, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28675381/matched-computed-tomography-segmentation-and-demographic-data-for-oropharyngeal-cancer-radiomics-challenges
#14
(no author information available yet)
Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that 'radiomics', or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes...
July 4, 2017: Scientific Data
https://www.readbyqxmd.com/read/28668428/liver-mri-from-basic-protocol-to-advanced-techniques
#15
Henrique Donato, Manuela França, Isabel Candelária, Filipe Caseiro-Alves
Liver MR is a well-established modality with multiparametric capabilities. However, to take advantage of its full capacity, it is mandatory to master the technique and optimize imaging protocols, apply advanced imaging concepts and understand the use of different contrast media. Physiologic artefacts although inherent to upper abdominal studies can be minimized using triggering techniques and new strategies for motion control. For standardization, the liver MR protocol should include motion-resistant T2-w sequences, in-op phase GRE T1 and T2-w fast spin echo sequences with fat suppression...
August 2017: European Journal of Radiology
https://www.readbyqxmd.com/read/28653477/radiomic-analysis-of-soft-tissues-sarcomas-can-distinguish-intermediate-from-high-grade-lesions
#16
Valentina D A Corino, Eros Montin, Antonella Messina, Paolo G Casali, Alessandro Gronchi, Alfonso Marchianò, Luca T Mainardi
PURPOSE: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). MATERIALS AND METHODS: MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features...
June 27, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28646744/systematic-biobanking-novel-imaging-techniques-and-advanced-molecular-analysis-for-precise-tumor-diagnosis-and-therapy-the-polish-mobit-project
#17
Jacek Niklinski, Adam Kretowski, Marcin Moniuszko, Joanna Reszec, Anna Michalska-Falkowska, Magdalena Niemira, Michal Ciborowski, Radoslaw Charkiewicz, Dorota Jurgilewicz, Miroslaw Kozlowski, Rodryg Ramlau, Cezary Piwkowski, Miroslaw Kwasniewski, Monika Kaczmarek, Andrzej Ciereszko, Tomasz Wasniewski, Robert Mroz, Wojciech Naumnik, Ewa Sierko, Magdalena Paczkowska, Joanna Kisluk, Anetta Sulewska, Adam Cybulski, Zenon Mariak, Boguslaw Kedra, Jacek Szamatowicz, Paweł Kurzawa, Lukasz Minarowski, Angelika Edyta Charkiewicz, Barbara Mroczko, Jolanta Malyszko, Christian Manegold, Lothar Pilz, Heike Allgayer, Mohammed L Abba, Hartmut Juhl, Frauke Koch
Personalized and precision medicine is gaining recognition due to the limitations by standard diagnosis and treatment; many areas of medicine, from cancer to psychiatry, are moving towards tailored and individualized treatment for patients based on their clinical characteristics and genetic signatures as well as novel imaging techniques. Advances in whole genome sequencing have led to identification of genes involved in a variety of diseases. Moreover, biomarkers indicating severity of disease or susceptibility to treatment are increasingly being characterized...
June 21, 2017: Advances in Medical Sciences
https://www.readbyqxmd.com/read/28641699/-quantitative-imaging-assessment-of-tumor-response-to-chemoradiation-%C3%A2-in-lung-cancer
#18
REVIEW
Yuxin Jiao, Yanping Ren, Xiangpeng Zheng
Precision medicine demands accurate assessment of tumor response to therapies with the purpose of timely optimization or adjustment of the given treatment regimens. Chemoradiation remains the standard of care in advanced lung cancers and imaging-based noninvasive response evaluation could improve therapeutic efficacy and reduce treatment-related severe side effects. In this review, we overviewed the applications and pitfalls of major imaging modalities in response evaluation in lung cancer from a quantitative perspective...
June 20, 2017: Zhongguo Fei Ai za Zhi, Chinese Journal of Lung Cancer
https://www.readbyqxmd.com/read/28637483/analyzing-human-decisions-in-igrt-of-head-and-neck-cancer-patients-to-teach-image-registration-algorithms-what-experts-know
#19
Eva Maria Stoiber, Nina Bougatf, Hendrik Teske, Christian Bierstedt, Dieter Oetzel, Jürgen Debus, Rolf Bendl, Kristina Giske
BACKGROUND: In IGRT of deformable head-and-neck anatomy, patient setup corrections are derived by rigid registration methods. In practice, experienced radiation therapists often correct the resulting vectors, thus indicating a different prioritization of alignment of local structures. Purpose of this study is to transfer the knowledge experts apply when correcting the automatically generated result (pre-match) to automated registration. METHODS: Datasets of 25 head-and-neck-cancer patients with daily CBCTs and corresponding approved setup correction vectors were analyzed...
June 21, 2017: Radiation Oncology
https://www.readbyqxmd.com/read/28629560/ct-based-radiomics-signature-for-differentiating-borrmann-type-iv-gastric-cancer-from-primary-gastric-lymphoma
#20
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
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