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).

The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-CT volume pairs were collected and a three-channel cGAN (accounting for air, bones, soft tissues) was used to generate sCTs. The network was tested on five held-out MRI volumes for two scenarios: (i) a CT-based segmentation of the MRI channels, to assess the quality of sCTs and (ii) an MRI manual segmentation, to simulate an MRI-only treatment scenario. The sCTs were evaluated by means of similarity metrics (e.g., mean absolute error, MAE) and geometrical criteria (e.g., dice coefficient). Recalculated CIRT plans were evaluated through dose volume histogram, gamma analysis and range shift analysis. The CT-based test set presented optimal MAE on bones (86.03 ± 10.76 HU), soft tissues (55.39 ± 3.41 HU) and air (54.42 ± 11.48 HU). Higher values were obtained from the MRI-only test set (MAE_BONE = 154.87 ± 22.90 HU). The global gamma pass rate reached 94.88 ± 4.9% with 3%/3 mm, while the range shift reached a median (IQR) of 0.98 (3.64) mm. The three-channel cGAN can generate acceptable abdominal sCTs and allow for CIRT dose recalculations comparable to the clinical plans.

A low linear energy transfer (LET) in the target can reduce the effectiveness of carbon ion radiotherapy (CIRT). This study aimed at exploring benefits and limitations of LET optimization for large sacral chordomas (SC) undergoing CIRT. Seventeen cases were used to tune LET-based optimization, and seven to independently test interfraction plan robustness. For each patient, a reference plan was optimized on biologically-weighted dose cost functions. For the first group, 7 LET-optimized plans were obtained by increasing the gross tumor volume (GTV) minimum LET_d (minLET_d) in the range 37–55 keV/μm, in steps of 3 keV/μm. The optimal LET-optimized plan (LET_OPT) was the one maximizing LET_d, while adhering to clinical acceptability criteria. Reference and LET_OPT plans were compared through dose and LETd metrics (D_x, L_x to x% volume) for the GTV, clinical target volume (CTV), and organs at risk (OARs). The 7 held-out cases were optimized setting minLET_d to the average GTV L_98% of the investigation cohort. Both reference and LET_OPT plans were recalculated on re-evaluation CTs and compared. GTV L_98% increased from (31.8 ± 2.5)keV/μm to (47.6 ± 3.1)keV/μm on the LET_OPT plans, while the fraction of GTV receiving over 50 keV/μm increased on average by 36% (p < 0.001), without affecting target coverage goals, or impacting LET_d and dose to OARs. The interfraction analysis showed no significant worsening with minLET_d set to 48 keV/μm. LET_d optimization for large SC could boost the LET_d in the GTV without significantly compromising plan quality, potentially improving the therapeutic effects of CIRT for large radioresistant tumors.

To investigate the role of dosiomics features extracted from physical dose (D_PHYS), RBE-weighted dose (D_RBE) and dose-averaged Linear Energy Transfer (LET_d), to predict the risk of local recurrence (LR) in skull base chordoma (SBC) treated with Carbon Ion Radiotherapy (CIRT). Thus, define and evaluate dosiomics-driven tumor control probability (TCP) models. 54 SBC patients were retrospectively selected for this study. A regularized Cox proportional hazard model (r-Cox) and Survival Support Vector Machine (s-SVM) were tuned within a repeated Cross Validation (CV) and patients were stratified in low/high risk of LR. Models' performance was evaluated through Harrell's concordance statistic (C-index), and survival was represented through Kaplan-Meier (KM) curves. A multivariable logistic regression was fit to the selected feature sets to generate a dosiomics-driven TCP model for each map. These were compared to a reference model built with clinical parameters in terms of f-score and accuracy. The LETd maps reached a test C-index of 0.750 and 0.786 with r-Cox and s-SVM, and significantly separated KM curves. D_PHYS maps and clinical parameters showed promising CV outcomes with C-index above 0.8, despite a poorer performance on the test set and patients stratification. The LETd-based TCP showed a significatively higher f-score (0.67[0.52-0.70], median[IQR]) compared to the clinical model (0.4[0.32-0.63], p < 0.025), while D_PHYS achieved a significatively higher accuracy (D_PHYS: 0.73[0.65-0.79], Clinical: 0.6 [0.52-0.72]). This analysis supports the role of LETd as relevant source of prognostic factors for LR in SBC treated with CIRT. This is reflected in the TCP modeling, where LETd and D_PHYS showed an improved performance with respect to clinical models.

To integrate patient-specific cell count data from diffusion-weighted MRI (DWI) into the linear-quadratic (LQ) Poisson tumor control probability (TCP) model for sacral chordomas (SC) treated with carbon ion radiotherapy (CIRT), aiming to improve local control (LC) and local relapse (LR) prediction. We considered data from 37 of the first 50 SC patients consecutively treated at the National Centre for Oncological Hadrontherapy (CNAO, Pavia, Italy). LQ Poisson formalism was revised to integrate either a linear (TCP_LIN) or logarithmic (TCP_LOG) dependence on clonogenic cell count, derived from baseline DWI through an optimal match with in-silico simulations. The models were compared with the case of a uniform cell density of 10⁷ cells/cm³, as widely adopted in the literature. All models were fitted on 27 patients and tested on 10 held-out cases to assess the performance, both in terms of area under the receiver-operator curve (AUC) and considering the statistical differences in TCP between LR and LC. In contrast to the constant cell density model, DWI-based models significantly separated the TCP of LC and LR patients, with TCP_LOG describing an average TCP of 71.3 % ± 9.56 % for LC patients, compared to 48.9 % ± 9.49 % for LR test cases. AUC values of 0.92 and 0.96 were respectively achieved by TCP_LIN and TCP_LOG, compared to 0.88 for constant cell density, on the test set. DWI-based cell count data can significantly improve the performance of TCP models in predicting the probability of LC of SC treated with CIRT.

Dose Painting (DP) in radiotherapy is a strategy to account for the tumor microstructural heterogeneity. This study evaluated a DP approach in carbon ion radiotherapy (CIRT), using cell count estimates derived from diffusion-weighted magnetic resonance imaging (DWI). Thirty-seven large sacral chordoma (SC) patients were analysed. Voxel-wise cell count was estimated from DWI using a published microstructural model. A Poisson-based tumor control probability (TCP) model, fitted on 27 patients, guided DP optimization in a research version of RayStation 2024A. The approach was tested on 10 patients, comparing uniform-dose plans against two strategies: dose redistribution (DR), which maintained the mean gross tumor volume (GTV) dose, and dose escalation (DE), which allowed a 3 % increase. Plan evaluation on targets and organs at risk (OARs) included dose-volume histogram metrics (D_95%, D_50%, D_1%) and dose-averaged linear energy transfer (LET_d)-volume histogram metrics (L_98%, L_50%, L_1%) to assess clinical acceptability. TCP gain quantified the benefit of biologically targeted strategies. TCP uncertainty was evaluated by propagating the microstructural model’s errors to generate best- and worst-case cell count maps. DE and DR plans met clinical acceptability criteria. DE increased TCP from 75.5 ± 5.6 % to 83.3 ± 3.9 % (p < 0.001), with −3 to +5 percentage points variation under uncertainty. DR plans showed a TCP gain of 1.8 ± 1.0 percentage points. No significant dose or LET_d increase was observed in OARs, while DE plans showed a lower L_98% in GTV. Dose painting based on microstructural modelling in CIRT showed potential to improve TCP while sparing OARs.