Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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Although external beam radiotherapy is one of the most commonly prescribed treatments for prostate cancer, severe complications may arise as a result of high delivered doses to the neighboring organs at risk, namely the bladder and the rectum. The prediction of this toxicity events are commonly based on the planned dose distribution using the dose-volume histograms within predictive models. However, as different spatial dose distributions may produce similar dose-volume histograms, these models may not be accurate in revealing the subtleties of the dose-effect relationships. Using the prescribed dose, we propose a voxel-based principal component analysis method for characterizing and classifying those individuals at risk of rectal bleeding. Sixty-five cases of patients treated for prostate cancer were reviewed; fifteen of them presented rectal bleeding within two years after the treatment. The method was able to classify rectal bleeding with 0.8 specificity and 0.73 sensitivity. In addition, eigenimages with the most discriminant features suggest that some specific dose patterns are related to rectal bleeding.