Perspective of the Large Databases and Ontologic Models of Creation of Preclinical and Clinical Results

In the last decade, remarkable advances in cancer care have created new challenges leading the clinical practice towards a personalized medicine, with an essential role of decision support systems (DSS). Numerous information routinely collected in clinical practice are standardized through the creation of ontologies and included into large databases. Using innovative “rapid-learning” research techniques, it is possible to analyze data and “extract” the factors that can mostly influence the pre-defined outcomes. The availability of reliable and consistent prediction tools makes possible to stratify population in specific risk groups, identifying patients who can better benefit from a specific treatment procedure.

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

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

[3]  Andre Dekker,et al.  VATE: VAlidation of high TEchnology based on large database analysis by learning machine , 2014 .

[4]  Elizabeth Eisenhauer,et al.  Nomograms for predicting survival of patients with newly diagnosed glioblastoma: prognostic factor analysis of EORTC and NCIC trial 26981-22981/CE.3. , 2008, The Lancet. Oncology.

[5]  H. White,et al.  Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. , 2001, Journal of clinical epidemiology.

[6]  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.

[7]  Ruijuan Hu.  Medical Data Mining Based on Decision Tree Algorithm , 2011, Comput. Inf. Sci..

[8]  Ferenc A. Jolesz,et al.  Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2011, PloS one.

[9]  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.

[10]  James A. Hendler,et al.  The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities , 2001 .

[11]  Jigneshkumar L Patel,et al.  Applications of artificial neural networks in medical science. , 2007, Current clinical pharmacology.

[12]  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.

[13]  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.

[14]  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.

[15]  W. Mason,et al.  Emerging Biomarkers in Glioblastoma , 2013, Cancers.

[16]  R. Marcos-Gragera,et al.  Population-based survival analyses of central nervous system tumors from 1994 to 2008. An up-dated study in the temozolomide-era. , 2014, Cancer epidemiology.

[17]  Chalapathy Neti,et al.  Rapid-learning system for cancer care. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

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

[19]  Pascal O. Zinn,et al.  A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature , 2012, PloS one.

[20]  J. Dewar,et al.  Impact of multidisciplinary team management in head and neck cancer patients , 2011, British Journal of Cancer.

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

[22]  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.

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

[24]  W. Mason,et al.  Canadian recommendations for the treatment of glioblastoma multiforme , 2007, Current oncology.

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

[26]  Brian Litt,et al.  Genetic Algorithm-Evolved Bayesian Network Classifier for Medical Applications , 2006 .

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

[28]  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.

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

[30]  H. Fine,et al.  Molecular characterizations of glioblastoma, targeted therapy, and clinical results to date , 2015, Cancer.

[31]  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.

[32]  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.

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

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

[35]  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 .