A Novel Classification Method for Prediction of Rectal Bleeding in Prostate Cancer Radiotherapy Based on a Semi-Nonnegative ICA of 3D Planned Dose Distributions

作者：Coloigner Julie^*; Fargeas Aureline; Kachenoura Amar; Wang Lu; Drean Gael; Lafond Caroline; Senhadji Lotfi; de Crevoisier Renaud; Acosta Oscar; Albera Laurent

来源：IEEE Journal of Biomedical and Health Informatics, 2015, 19(3): 1168-1177.

DOI：10.1109/JBHI.2014.2328315

• Classification
• feature extraction
• prostate cancer
• radiotherapy
• rectal bleeding
• semi-nonnegative ICA algorithm
• side effects

The understanding of dose/side-effects relationships in prostate cancer radiotherapy is crucial to define appropriate individual's constraints for the therapy planning. Most of the existing methods to predict side-effects do not fully exploit the rich spatial information conveyed by the three-dimensional planned dose distributions. We propose a new classification method for three-dimensional individuals' doses, based on a new semi-nonnegative ICA algorithm to identify patients at risk of presenting rectal bleeding from a population treated for prostate cancer. The method first determines two bases of vectors from the population data: the two bases span vector subspaces, which characterize patients with and without rectal bleeding, respectively. The classification is then achieved by calculating the distance of a given patient to the two subspaces. The results, obtained on a cohort of 87 patients (at two year follow-up) treated with radiotherapy, showed high performance in terms of sensitivity and specificity.

• 出版日期2015-5