'Rapid Learning health care in oncology' - an approach towards decision support systems enabling customised radiotherapy'.
about
Creating a data exchange strategy for radiotherapy research: towards federated databases and anonymised public datasetsBiomarkers of treatment toxicity in combined-modality cancer therapies with radiation and systemic drugs: study design, multiplex methods, molecular networksInternational data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data miningOverview of the American Society for Radiation Oncology-National Institutes of Health-American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big DataPoint/Counterpoint. Future radiotherapy practice will be based on evidence from retrospective interrogation of linked clinical data sources rather than prospective randomized controlled clinical trials.Radiogenomics: radiobiology enters the era of big data and team science.Towards a semantic PACS: Using Semantic Web technology to represent imaging data.Impact of treatment planning and delivery factors on gastrointestinal toxicity: an analysis of data from the RADAR prostate radiotherapy trial.Standardized data collection to build prediction models in oncology: a prototype for rectal cancer.Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical dataThe effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis.Respiration-Averaged CT for Attenuation Correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer PatientsPersonalization and Patient Involvement in Decision Support Systems: Current TrendsPersonalized radiotherapy: concepts, biomarkers and trial designDifferent prognostic models for different patient populations: validation of a new prognostic model for patients with oropharyngeal cancer in Western Europe.Validation of a rectal cancer outcome prediction model with a cohort of Chinese patients.Applications and limitations of radiomicsPredicting distant failure in early stage NSCLC treated with SBRT using clinical parameters.Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [(18)F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation.Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting.Targeting tumour hypoxia to prevent cancer metastasis. From biology, biosensing and technology to drug development: the METOXIA consortium.PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE.Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer.Feature selection methodology for longitudinal cone-beam CT radiomics.Radiogenomics and radiotherapy response modeling.Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy.Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapyModern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine.Organizational development trajectory of a large academic radiotherapy department set up similarly to a prospective clinical trial: the MAASTRO experience.Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.Decision support systems for incurable non-small cell lung cancer: a systematic reviewOne, two, three or four ports… does it matter? Priorities in lung cancer surgery.Radiomics: the bridge between medical imaging and personalized medicine.Prospective validation of pathologic complete response models in rectal cancer: Transferability and reproducibility.Variability of average SUV from several hottest voxels is lower than that of SUVmax and SUVpeak.Stereotactic Radiosurgery in the Management of Patients With Brain Metastases of Non-Small Cell Lung Cancer: Indications, Decision Tools and Future Directions.Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.Data collection of patient outcomes: one institution's experience.
P2860
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P2860
'Rapid Learning health care in oncology' - an approach towards decision support systems enabling customised radiotherapy'.
description
2013 nî lūn-bûn
@nan
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
2013年论文
@zh
2013年论文
@zh-cn
name
'Rapid Learning health care in ...... ling customised radiotherapy'.
@en
type
label
'Rapid Learning health care in ...... ling customised radiotherapy'.
@en
prefLabel
'Rapid Learning health care in ...... ling customised radiotherapy'.
@en
P2093
P50
P1476
'Rapid Learning health care in ...... bling customised radiotherapy'
@en
P2093
Bart Reymen
Catharina M L Zegers
Emmanuel Rios Velazquez
Frank Hoebers
Georgi Nalbantov
Jeroen Buijsen
Liesbeth Boersma
M Scott Marshall
Philippe Lambin
Ralph T H Leijenaar
P304
P356
10.1016/J.RADONC.2013.07.007
P577
2013-08-28T00:00:00Z