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

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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.

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Acknowledgment

This study was supported by grant no. F2003-513 from FORSS, the Health Research Council in the South-East of Sweden. Special thanks are due to the South-East Swedish Breast Cancer Study Group for fruitful collaboration and support in this study.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Division of Medical Informatics, Linköping University, University Hospital, S-58185, Linköping, Sweden

    Amir R. Razavi, Hans Gill, Hans Åhlfeldt & Nosrat Shahsavar

  2. Regional Oncology Centre, University Hospital, Linköping, Sweden

    Nosrat Shahsavar

Authors
  1. Amir R. Razavi
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  2. Hans Gill
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  3. Hans Åhlfeldt
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  4. Nosrat Shahsavar
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Correspondence to Amir R. Razavi.

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Razavi, A.R., Gill, H., Åhlfeldt, H. et al. Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts. J Med Syst 31, 263–273 (2007). https://doi.org/10.1007/s10916-007-9064-1

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  • DOI: https://doi.org/10.1007/s10916-007-9064-1

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