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


METHODS
The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression.


RESULTS
The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73.


CONCLUSIONS
These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.
Medical School, Northern Institute for Cancer Research, University of Newcastle Upon Tyne, Newcastle upon Tyne, UK.
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A
Enshaei
Medical School, Northern Institute for Cancer Research, University of Newcastle Upon Tyne, Newcastle upon Tyne, UK.
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CN
Robson
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.
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Edmondson
D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D000077216:Carcinoma, Ovarian Epithelial; D065426:Cytoreduction Surgical Procedures; D003663:Decision Trees; D005260:Female; D006801:Humans; D009375:Neoplasms, Glandular and Epithelial / Q000601:surgery*; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000601:surgery*; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D060388:Support Vector Machine; D015996:Survival Rate