Background The aim of this study is to evaluate results in terms of local control (LC), overall survival (OS), and toxicity profile and to better identify factors influencing clinical outcome of skull base chordoma treated with proton therapy (PT) and carbon ion radiotherapy (CIRT). Methods We prospectively collected and analyzed data of 135 patients treated between November 2011 and December 2018. Total prescription dose in the PT group (70 patients) and CIRT group (65 patients) was 74 Gy relative biological effectiveness (RBE) delivered in 37 fractions and 70.4 Gy(RBE) delivered in 16 fractions, respectively (CIRT in unfavorable patients). LC and OS were evaluated using the Kaplan–Meier method. Univariate and multivariate analyses were performed, to identify prognostic factors on clinical outcomes. Results After a median follow-up of 44 (range, 6–87) months, 14 (21%) and 8 (11%) local failures were observed in CIRT and PT group, respectively. Five-year LC rate was 71% in CIRT cohort and 84% in PT cohort. The estimated 5-year OS rate in the CIRT and PT group was 82% and 83%, respectively. On multivariate analysis, gross tumor volume (GTV), optic pathways, and/or brainstem compression and dose coverage are independent prognostic factors of local failure risk. High rate toxicity grade ≥3 was reported in 11% of patients. Conclusions Particle radiotherapy is an effective treatment for skull base chordoma with acceptable late toxicity. GTV, optic pathways, and/or brainstem compression and target coverage were independent prognostic factors for LC. Key Points • Proton and carbon ion therapy are effective and safe in skull base chordoma. • Prognostic factors are GTV, organs at risk compression, and dose coverage. • Dual particle therapy and customized strategy was adopted.

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In particle therapy, the uncertainty of the delivered particle range during the patient irradiation limits the optimization of the treatment planning. Therefore, an in vivo treatment verification device is required, not only to improve the plan robustness, but also to detect significant interfractional morphological changes during the treatment itself. In this article, an effective and robust analysis to detect regions with a significant range discrepancy is proposed. This study relies on an in vivo treatment verification by means of in-beam Positron Emission Tomography (PET) and was carried out with the INSIDE system installed at the National Center of Oncological Hadrontherapy (CNAO) in Pavia, which is under clinical testing since July 2019. Patients affected by head-and-neck tumors treated with protons have been considered. First, in order to tune the analysis parameters, a Monte Carlo (MC) simulation was carried out to reproduce a patient who required a replanning because of significant morphological changes found during the treatment. Then, the developed approach was validated on the experimental measurements of three patients recruited for the INSIDE clinical trial (ClinicalTrials.gov ID: NCT03662373), showing the capability to estimate the treatment compliance with the prescription both when no morphological changes occurred and when a morphological change did occur, thus proving to be a promising tool for clinicians to detect variations in the patients treatments.

Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.

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