Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

Journal: Annals of surgical oncology

Published Date: Mar 10, 2015

Abstract

BACKGROUND: The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches.

Authors

A Enshaei

Medical School, Northern Institute for Cancer Research, University of Newcastle Upon Tyne, Newcastle upon Tyne, UK.
C N Robson

Medical School, Northern Institute for Cancer Research, University of Newcastle Upon Tyne, Newcastle upon Tyne, UK.
R J Edmondson

Chair of Gynaecological Oncology, Faculty Institute for Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, St. Mary's Hospital, Manchester, UK. richard.edmondson@manchester.ac.uk.

Keywords

Algorithms Area Under Curve Bayes Theorem Carcinoma, Ovarian Epithelial Cytoreduction Surgical Procedures Decision Trees Female Humans Neoplasms, Glandular and Epithelial Neural Networks, Computer Ovarian Neoplasms Predictive Value of Tests Prognosis Proportional Hazards Models ROC Curve Support Vector Machine Survival Rate

External Resources

View on PubMed Access via DOI PubMed (25752894)

Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

Abstract

Authors

Keywords

External Resources

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