Source:Radiotherapy and Oncology
Author(s): Auréline Fargeas, Oscar Acosta, Juan David Ospina Arrango, Amine Ferhat, Nathalie Costet, Laurent Albera, David Azria, Pascal Fenoglietto, Gilles Créhange, Véronique Beckendorf, Mathieu Hatt, Amar Kachenoura, Renaud de Crevoisier
Background and purposeTo evaluate the benefit of independent component analysis (ICA)-based models for predicting rectal bleeding (RB) following prostate cancer radiotherapy.Materials and methodsA total of 593 irradiated prostate cancer patients were prospectively analyzed for Grade ≥2 RB. ICA was used to extract two informative subspaces (presenting RB or not) from the rectal DVHs, enabling a set of new pICA parameters to be estimated. These DVH-based parameters, along with others from the principal component analysis (PCA) and functional PCA, were compared to "standard" features (patient/treatment characteristics and DVH bins) using the Cox proportional hazards model for RB prediction. The whole cohort was divided into: (i) training (N = 339) for ICA-based subspace identification and Cox regression model identification and (ii) validation (N = 254) for RB prediction capability evaluation using the C-index and the area under the receiving operating curve (AUC), by comparing predicted and observed toxicity probabilities.ResultsIn the training cohort, multivariate Cox analysis retained pICA and PC as significant parameters of RB with 0.65 C-index. For the validation cohort, the C-index increased from 0.64 when pICA was not included in the Cox model to 0.78 when including pICA parameters. When pICA was not included, the AUC for 3-, 5-, and 8-year RB prediction were 0.68, 0.66, and 0.64, respectively. When included, the AUC increased to 0.83, 0.80, and 0.78, respectively.ConclusionAmong the many various extracted or calculated features, ICA parameters improved RB prediction following prostate cancer radiotherapy.
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