VATE: VAlidation of high TEchnology based on large database analysis by learning machine

SUMMARY  The interaction between implementation of new technologies and different outcomes can allow a broad range of researches to be expanded. The purpose of this paper is to introduce the VAlidation of high TEchnology based on large database analysis by learning machine (VATE) project that aims to combine new technologies with outcomes related to rectal cancer in terms of tumor control and normal tissue sparing. Using automated computer bots and the knowledge for screening data it is possible to identify the factors that can mostly influence those outcomes. Population-based observational studies resulting from the linkage of different datasets will be conducted in order to develop predictive models that allow physicians to share decision with patients into a wider concept of tailored treatment.

[1]  J. Fregnani,et al.  KRAS and BRAF mutations and MSI status in precursor lesions of colorectal cancer detected by colonoscopy. , 2014, Oncology reports.

[2]  D. Sargent,et al.  Association of age with survival in patients with metastatic colorectal cancer: analysis from the ARCAD Clinical Trials Program. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  Shung-Haur Yang,et al.  The prognostic role of microsatellite instability, codon‐specific KRAS, and BRAF mutations in colon cancer , 2014, Journal of surgical oncology.

[4]  J. van Soest,et al.  An umbrella protocol for standardized data collection (SDC) in rectal cancer: a prospective uniform naming and procedure convention to support personalized medicine. , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[5]  Vincenzo Valentini,et al.  International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining. , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[6]  I. Tannock,et al.  Randomised controlled trials and population-based observational research: partners in the evolution of medical evidence , 2014, British Journal of Cancer.

[7]  P. Lambin,et al.  Learning methods in radiation oncology ‘Rapid Learning health care in oncology’ – An approach towards decision support systems enabling customised radiotherapy’ q , 2013 .

[8]  A. Zietman.  Falsification, fabrication, and plagiarism: the unholy trinity of scientific writing. , 2013, International journal of radiation oncology, biology, physics.

[9]  Daniel J Sargent,et al.  The predictive and prognostic value of sex in early-stage colon cancer: a pooled analysis of 33,345 patients from the ACCENT database. , 2013, Clinical colorectal cancer.

[10]  J. Stamey,et al.  Bayesian sensitivity analysis methods to evaluate bias due to misclassification and missing data using informative priors and external validation data. , 2013, Cancer epidemiology.

[11]  R. Glynne-Jones,et al.  The status of targeted agents in the setting of neoadjuvant radiation therapy in locally advanced rectal cancers. , 2013, Journal of gastrointestinal oncology.

[12]  J. Bosset,et al.  [Multidisciplinary management of rectal cancer]. , 2012, Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique.

[13]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[14]  K. Kahn,et al.  Effect of adjuvant chemotherapy on survival of patients with stage III colon cancer diagnosed after age 75 years. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[15]  Keith Marsolo,et al.  Approaches to Facilitate Institutional Review Board Approval of Multicenter Research Studies , 2012, Medical care.

[16]  Jihoon Kim,et al.  Grid Binary LOgistic REgression (GLORE): building shared models without sharing data , 2012, J. Am. Medical Informatics Assoc..

[17]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[18]  Vincenzo Valentini,et al.  Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  A. Vickers.  Prediction models: revolutionary in principle, but do they do more good than harm? , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[20]  C. Ratto,et al.  Potential pitfalls concerning colorectal cancer classification in the seventh edition of the AJCC Cancer Staging Manual. , 2011, Diseases of the colon and rectum.

[21]  A. Debucquoy,et al.  The ESTRO Breur Lecture 2010: toward a tailored patient approach in rectal cancer. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[22]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[23]  P. Lambin,et al.  Residual metabolic tumor activity after chemo-radiotherapy is mainly located in initially high FDG uptake areas in rectal cancer. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[24]  Joseph O Deasy,et al.  A Bayesian network approach for modeling local failure in lung cancer , 2011, Physics in medicine and biology.

[25]  C. Booth.  Evaluating Patient-Centered Outcomes in the Randomized Controlled Trial and Beyond: Informing the Future with Lessons from the Past , 2010, Clinical Cancer Research.

[26]  J F Fowler,et al.  21 years of biologically effective dose. , 2010, The British journal of radiology.

[27]  D De Ruysscher,et al.  Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. , 2010, Medical physics.

[28]  J. Borrás,et al.  Multidisciplinary Rectal Cancer Management: 2nd European Rectal Cancer Consensus Conference (EURECA-CC2). , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[29]  Shipeng Yu,et al.  Development and external validation of prognostic model for 2-year survival of non-small-cell lung cancer patients treated with chemoradiotherapy. , 2009, International journal of radiation oncology, biology, physics.

[30]  Elena B. Elkin,et al.  Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers , 2008, BMC Medical Informatics Decis. Mak..

[31]  V. Lemmens,et al.  Management and survival of colorectal cancer in the elderly in population-based studies. , 2007, European journal of cancer.

[32]  W. Mackillop,et al.  Impact of adoption of chemoradiotherapy on the outcome of cervical cancer in Ontario: results of a population-based cohort study. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[33]  E. Elkin,et al.  Decision Curve Analysis: A Novel Method for Evaluating Prediction Models , 2006, Medical decision making : an international journal of the Society for Medical Decision Making.

[34]  S. Bentzen.  Preventing or reducing late side effects of radiation therapy: radiobiology meets molecular pathology , 2006, Nature Reviews Cancer.

[35]  A. Neugut,et al.  Racial disparities in treatment and survival among women with early-stage breast cancer. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[36]  S. Tyldesley,et al.  Association between age and the utilization of radiotherapy in Ontario. , 2000, International journal of radiation oncology, biology, physics.

[37]  C B Begg,et al.  Racial differences in the treatment of early-stage lung cancer. , 1999, The New England journal of medicine.

[38]  W. Mackillop,et al.  Associations between community income and cancer survival in Ontario, Canada, and the United States. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  Christel Daniel-Le Bozec,et al.  An Ontological Approach for the Exploitation of Clinical Data , 2013, MedInfo.

[40]  P. Lambin,et al.  Predicting outcomes in radiation oncology—multifactorial decision support systems , 2013, Nature Reviews Clinical Oncology.

[41]  P. Lambin,et al.  Is it time for tailored treatment of rectal cancer? From prescribing by consensus to prescribing by numbers. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[42]  Karin Haustermans,et al.  Development and external validation of a predictive model for pathological complete response of rectal cancer patients including sequential PET-CT imaging. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[43]  Jordi Giralt,et al.  Radiotherapy plus cetuximab for locoregionally advanced head and neck cancer: 5-year survival data from a phase 3 randomised trial, and relation between cetuximab-induced rash and survival. , 2010, The Lancet. Oncology.

[44]  Valentini,et al.  Multidisciplinary rectal cancer management , 2009 .

[45]  Alan L. Rector,et al.  A Deployment and Research Roadmap for Semantic Interoperability: the EU SemanticHEALTH project , 2008, MIE.

[46]  Kun Liu,et al.  Random projection-based multiplicative data perturbation for privacy preserving distributed data mining , 2006, IEEE Transactions on Knowledge and Data Engineering.

[47]  Frank E. Harrell,et al.  Resampling, Validating, Describing, and Simplifying the Model , 2001 .