ESTRO 2023 - Abstract Book

S1878

Digital Posters

ESTRO 2023

Results In LR prediction, the best performance was achieved with DLEM dosiomics features, reaching a C-index (median(interquartile-range)) of 0.70(0.18) and statistically significant KM curves (p-value=0.0201, Fig.2). In particular, patients at high-risk of LR were characterized by the presence of periodic patterns within the dose map. In HD-LR prediction, the model with highest performance was built on features extracted from LETd maps (0.86(0.22)), with DLEM and DMKM models showing promising but weaker results (0.83(0.21), 0.80(0.21)). Specifically, high-risk HD-LR patients were characterized by more heterogeneous LETd values and concentrated on lower values than low-risk patients. Similarly, in terms of KM curves, LETd-based models outperformed those of DLEM and DMKM, achieving highly significant low/high risk separations (p-values = 0.0009(Fig.2), 0.0075, 0.0072, respectively). Finally, for both LR and HD-LR prediction, DVH-based models were found to be weakly predictive compared with dosiomics ones (max C-indices of 0.58 (LR) and 0.65 (HD-LR)).

Conclusion LETd maps shows a great potential as prognostic factor for SC HD-LR in CIRT, and dosiomics appears to be the most promising approach against more conventional methods (e.g., DVH-based).

PO-2095 In silico identification of dose equivalences in hypofractionated prostate cancer radiotherapy

C. Sosa-Marrero 1 , A. Briens 2 , P. Fontaine 1 , O. Acosta 1 , R. de Crevoisier 1,2