Independent component analysis for rectal bleeding prediction following prostate cancer radiotherapy. Issue 2 (February 2018)

• Record Type: Journal Article
• Authors: Fargeas, Auréline; Acosta, Oscar; Ospina Arrango, Juan David; Ferhat, Amine; Costet, Nathalie; Albera, Laurent; Azria, David; Fenoglietto, Pascal; Créhange, Gilles; Beckendorf, Véronique; Hatt, Mathieu; Kachenoura, Amar; de Crevoisier, Renaud
• Is Part Of: Radiotherapy and oncology. Volume 126:Issue 2(2018)
• Page Start: 263
• Page End: 269
• Publication Date: 2018-02
• Subjects: Prostate cancer radiotherapy -- Toxicity -- Rectal bleeding -- Predictive model -- Independent component analysis; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2017.11.011
• Languages: English
• ISSNs: 0167-8140

Abstract

Abstract: Background and purpose: To evaluate the benefit of independent component analysis (ICA)-based models for predicting rectal bleeding (RB) following prostate cancer radiotherapy. Materials and methods: A 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. Results: In 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. Conclusion: Among the many various extracted or calculated features, ICA parameters improved RB prediction following prostate cancer radiotherapy.