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Spatial Characterization and Classification of Rectal Bleeding in Prostate Cancer Radiotherapy with a Voxel-Based Principal Components Analysis Model for 3D Dose Distribution

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6963))

Abstract

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.

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Chen, B. et al. (2011). Spatial Characterization and Classification of Rectal Bleeding in Prostate Cancer Radiotherapy with a Voxel-Based Principal Components Analysis Model for 3D Dose Distribution. In: Madabhushi, A., Dowling, J., Huisman, H., Barratt, D. (eds) Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions. Prostate Cancer Imaging 2011. Lecture Notes in Computer Science, vol 6963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23944-1_7

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  • DOI: https://doi.org/10.1007/978-3-642-23944-1_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23943-4

  • Online ISBN: 978-3-642-23944-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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