Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy
Background and purpose: To explore the use of texture analysis (TA) features of patients’ 3D dose distributions to improve prediction modelling of treatment complication rates in prostate cancer radiotherapy. Material and methods: Late toxicity scores, dose distributions, and non-treatment related (NTR) predictors for late toxicity, such as age and baseline symptoms, of 351 patients of the hypofractionation arm of the HYPRO randomized trial were used in this study. Apart from DVH parameters, also TA features of rectum and bladder 3D dose distributions were used for predictive modelling of gastrointestinal (GI) and genitourinary (GU) toxicities. Logistic Normal Tissue Complication Probability (NTCP) models were derived, using only NTR parameters, NTR + DVH, NTR + TA, and NTR + DVH + TA. Results: For rectal bleeding, the area under the curve (AUC) for using only NTR parameters was 0.58, which increased to 0.68, and 0.73, when adding DVH or TA parameters respectively. For faecal incontinence, the AUC went up from 0.63 (NTR only), to 0.68 (+DVH) and 0.73 (+TA). For nocturia, adding TA features resulted in an AUC increase from 0.64 to 0.66, while no improvement was seen when including DVH parameters in the modelling. For urinary incontinence, the AUC improved from 0.68 to 0.71 (+DVH) and 0.73 (+TA). For GI, model improvements resulting from adding TA parameters to NTR instead of DVH were statistically significant (p < 0.04). Conclusion: Inclusion of 3D dosimetric texture analysis features in predictive modelling of GI and GU toxicity rates in prostate cancer radiotherapy improved prediction performance, which was statistically significant for GI.
|, , , , ,|
|Radiotherapy & Oncology|
|Organisation||Department of Radiation Oncology|
Rossi, L, Bijman, R. (Rik), Schillemans, W, Aluwini, S, Cavedon, C. (Carlo), Witte, M.G, … Heijmen, B.J.M. (2018). Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy. Radiotherapy & Oncology. doi:10.1016/j.radonc.2018.07.027