A Dosiomics Analysis Based on Linear Energy Transfer and Biological Dose Maps to Predict Local Recurrence in Sacral Chordomas after Carbon-Ion Radiotherapy

SIMPLE SUMMARY: The poor tumor characterization and the lack of prognostic biomarkers hinder the efficacy and the personalization of treatments for Sacral Chordomas (SC), for which Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options. The aim of this work is to apply, for the first time, a dosiomics approach to biological dose and dose-averaged Linear Energy Transfer (LET(d)) maps, towards the identification of possible prognostic factors and the future integration of decision supportive tools in CIRT workflows. We conducted a time-to-event analysis on a pool of 50 SC patients, investigating the performances of regularized Cox models (r-Cox) and survival Support Vector Machines (s-SVM) in predicting Local Recurrence (LR). LET(d) distributions confirmed their important role for patient stratification into high/low-risk groups for recurrencies in high-dose regions, showing a potential as a possible source of prognostic factors for CIRT applied to SC. ABSTRACT: Carbon Ion Radiotherapy (CIRT) is one of the most promising therapeutic options to reduce Local Recurrence (LR) in Sacral Chordomas (SC). The aim of this work is to compare the performances of survival models fed with dosiomics features and conventional DVH metrics extracted from relative biological effectiveness (RBE)-weighted dose (D(RBE)) and dose-averaged Linear Energy Transfer (LET(d)) maps, towards the identification of possible prognostic factors for LR in SC patients treated with CIRT. This retrospective study included 50 patients affected by SC with a focus on patients that presented a relapse in a high-dose region. Survival models were built to predict both LR and High-Dose Local Recurrencies (HD-LR). The models were evaluated through Harrell Concordance Index (C-index) and patients were stratified into high/low-risk groups. Local Recurrence-free Kaplan–Meier curves were estimated and evaluated through log-rank tests. The model with highest performance (median(interquartile-range) C-index of 0.86 (0.22)) was built on features extracted from LET(d) maps, with D(RBE) models showing promising but weaker results (C-index of 0.83 (0.21), 0.80 (0.21)). Although the study should be extended to a wider patient population, LET(d) maps show potential as a 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).