Abstract
Background and purpose
To study the accuracy, specificity and sensitivity of the Naïve Bayesian Classifier
(NBC) in the assessment of individual risk of cancer relapse or progression after
radiotherapy (RT).
Materials and methods
Data of 142 brain tumour patients irradiated from 2000 to 2005 were analyzed. Ninety-six
attributes related to disease, patient and treatment were chosen. Attributes in binary
form consisted of the training set for NBC learning. NBC calculated an individual
conditional probability of being assigned to: relapse or progression (1), or no relapse
or progression (0) group. Accuracy, attribute selection and quality of classifier
were determined by comparison with actual treatment results, leave-one-out and cross
validation methods, respectively.
Clinical setting test utilized data of 35 patients. Treatment results at classification
were unknown and were compared with classification results after 3 months.
Results
High classification accuracy (84%), specificity (0.87) and sensitivity (0.80) were
achieved, both for classifier training and in progressive clinical evaluation.
Conclusions
NBC is a useful tool to support the assessment of individual risk of relapse or progression
in patients diagnosed with brain tumour undergoing RT postoperatively.
Keywords
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Article info
Publication history
Published online: November 19, 2007
Accepted:
October 11,
2007
Received in revised form:
October 8,
2007
Received:
July 1,
2007
Identification
Copyright
© 2007 Elsevier Ireland Ltd. Published by Elsevier Inc. All rights reserved.
