ESTRO 2023 - Abstract Book

S1877

Digital Posters

ESTRO 2023

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Conclusion Despite the encouraging results of combining image normalization and ComBat, this technique is still complex to implement in a multicenter setting. Our results suggest that deep learning-based methods could provide a tool capable of simultaneously mitigating the effects of multiple acquisition/reconstruction parameters on radiomic features. A normalization method based on a Cycle-GAN will be implemented on multi-scanner MR images and its normalization power will be evaluated.

PO-2094 Dosiomics applied to biological dose and LET maps to predict Local Recurrence in Sacral Chordoma

G. Parrella 1 , L. Morelli 1 , S. Annunziata 1 , S. Molinelli 2 , G. Magro 2 , M. Ciocca 2 , A. Chalaszczyk 3 , M.R. Fiore 3 , C. Paganelli 1 , E. Orlandi 3 , G. Baroni 1 1 Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, Italy; 2 National Center for Oncological Hadrontherapy, CNAO, Medical Physics unit, Pavia, Italy; 3 National Center for Oncological Hadrontherapy, CNAO, Clinical Unit, Pavia, Italy Purpose or Objective Sacral chordoma (SC) is a rare, locally invasive, and aggressive malignant tumour characterized by high radioresistance and probability of recurrence. Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in SC. However, the poor tumor characterization and the lack of prognostic biomarkers do not allow improving the efficacy and the personalization of SC treatments. The aim of this study is to apply, for the first time, a dosiomics approach to biological dose and dose-averaged Linear Energy Transfer (LETd) maps, towards the identification of possible prognostic factors for CIRT treatment outcome prediction. Materials and Methods 50 SC patients treated with CIRT at the National Centre for Oncological Hadrontherapy (Italy) were retrospectively selected together with treatment outcome information in terms of LR. After a median follow-up time of 42.6 moths, 24 patients were classified as recurrence-free (Local Control, LC) while in LR was found in 26 patients and, among these, 13 presented a recurrence in a high-dose region (High Dose Local Recurrence, HD-LR). Dosiomics features and conventional DVH parameters were extracted from LETd maps and biological dose maps (DLEM, DMKM, Fig.1). Cox proportional hazard models regularized with an elastic-net penalty (r-Cox) were encapsulated in a repeated 5-fold cross-validation routine and patients were stratified in low/high risk of adverse treatment outcome in terms of both LR and HD-LR. Prediction performance of dosiomics- and DVH-based models were evaluated in terms of Harrell Concordance Index (C-index) and Kaplan-Meier (KM) survival curves were estimated and evaluated through log- rank tests ( α =0.05).