Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts

Authors:
Amir R. Razavi;Hans Gill;Hans Åhlfeldt;Nosrat Shahsavar
Affiliations:
Department of Biomedical Engineering, Division of Medical Informatics, Linköping University, University Hospital, Linköping, Sweden S-58185;Department of Biomedical Engineering, Division of Medical Informatics, Linköping University, University Hospital, Linköping, Sweden S-58185;Department of Biomedical Engineering, Division of Medical Informatics, Linköping University, University Hospital, Linköping, Sweden S-58185;Department of Biomedical Engineering, Division of Medical Informatics, Linköping University, University Hospital, Linköping, Sweden S-58185 and Regional Oncology Centre, University Hospi ...
Venue:
Journal of Medical Systems
Year:
2007

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Abstract

Breast malignancy is the second most common cause of cancer death among women in Western countries. Identifying high-risk patients is vital in order to provide them with specialized treatment. In some situations, such as when access to experienced oncologists is not possible, decision support methods can be helpful in predicting the recurrence of cancer. Three thousand six hundred ninety-nine breast cancer patients admitted in south-east Sweden from 1986 to 1995 were studied. A decision tree was trained with all patients except for 100 cases and tested with those 100 cases. Two domain experts were asked for their opinions about the probability of recurrence of a certain outcome for these 100 patients. ROC curves, area under the ROC curves, and calibration for predictions were computed and compared. After comparing the predictions from a model built by data mining with predictions made by two domain experts, no significant differences were noted. In situations where experienced oncologists are not available, predictive models created with data mining techniques can be used to support physicians in decision making with acceptable accuracy.