Independent component analysis for rectal bleeding prediction following prostate cancer radiotherapy

• Published in: Radiotherapy and oncology 2018-02, Vol.126 (2), p.263-269
• Main 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
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Bioengineering; Cohort Studies; Engineering Sciences; Gastrointestinal Hemorrhage - epidemiology; Gastrointestinal Hemorrhage - etiology; Humans; Independent component analysis; Life Sciences; Male; Middle Aged; Multivariate Analysis; Predictive model; Principal Component Analysis; Probability; Proportional Hazards Models; Prospective Studies; Prostate cancer radiotherapy; Prostatic Neoplasms - radiotherapy; Radiation Injuries - epidemiology; Radiation Injuries - etiology; Rectal bleeding; Rectal Diseases - epidemiology; Rectal Diseases - etiology; Signal and Image processing; Toxicity
• ISSN: 0167-8140; 1879-0887
• DOI: 10.1016/j.radonc.2017.11.011

To evaluate the benefit of independent component analysis (ICA)-based models for predicting rectal bleeding (RB) following prostate cancer radiotherapy. 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. 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. Among the many various extracted or calculated features, ICA parameters improved RB prediction following prostate cancer radiotherapy.