Background: Currently, 13 Asian and European facilities deliver carbon ion radiotherapy (CIRT) for preclinical and clinical activity, and, to date, 55 clinical studies including CIRT for adult and paediatric solid neoplasms have been registered. The National Center for Oncological Hadrontherapy (CNAO) is the only Italian facility able to accelerate both protons and carbon ions for oncological treatment and research. Methods: To summarise and critically evaluate state-of-the-art knowledge on the application of carbon ion radiotherapy in oncological settings, the authors conducted a literature search till December 2022 in the following electronic databases: PubMed, Web of Science, MEDLINE, Google Scholar, and Cochrane. The results of 68 studies are reported using a narrative approach, highlighting CNAO’s clinical activity over the last 10 years of CIRT. Results: The ballistic and radiobiological hallmarks of CIRT make it an effective option in several rare, radioresistant, and difficult-to-treat tumours. CNAO has made a significant contribution to the advancement of knowledge on CIRT delivery in selected tumour types. Conclusions: After an initial ramp-up period, CNAO has progressively honed its clinical, technical, and dosimetric skills. Growing engagement with national and international networks and research groups for complex cancers has led to increasingly targeted patient selection for CIRT and lowered barriers to facility access.

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.

Objective: Accurate patient positioning is crucial in particle therapy due to the geometrical selectivity of particles. We report and discuss the National Center for Oncological Hadrontherapy (CNAO) experience in positioning accuracy and stability achieved with solid thermoplastic masks fixed on index base plates and assessed by daily orthogonal X-ray imaging. Methods: Positioning data were retrospectively collected (between 2012 and 2018) and grouped according to the treated anatomical site. 19696 fractions of 1325 patients were evaluated. The study was designed to assess: (i) the number of fractions in which a single correction vector was applied(SCV); (ii) the number of fractions in which further setup verification was performed (SV); (iii) the number of fractions in which SV lead to an additional correction within (MCV5min) 5 minutes from the first setup correction; (iv) the systematic (Σ) and random (σ) error components of the correction vectors applied. Results: A SCV was applied in 71.5% of fractions, otherwise SV was required. In 30.6% of fractions with SV, patient position was not further revised. In the remaining fractions, MCV5min were applied mainly in extracranial and cranial sites respectively. Interfraction Σ was ≤ 1.7 mm/0.7° and σ was ≤ 1.2 mm/0.6° in cranial sites while in extracranial sites Σ was ≤ 5.5 mm/0.9° and σ was ≤4.4 mm/0.9°. Setup residuals were submillimetric in all sites. In cranial patients, maximum intrafractional Σ was 0.8 mm/0.4°. Conclusion: This report extensively quantifies inter- and intrafraction setup accuracy on an institutional basis and confirms the need of image guidance to fully benefit from the geometrical selectivity of particles. Advances in knowledge: The reported analysis provides a board institutional data set on the evaluation of patient immobilization and bony anatomy alignment for several particle therapy clinical indications.

Eye tracking techniques based on deep learning are rapidly spreading in a wide variety of application fields. With this study, we want to exploit the potentiality of eye tracking techniques in ocular proton therapy (OPT) applications. We implemented a fully automatic approach based on two-stage convolutional neural networks (CNNs): the first stage roughly identifies the eye position and the second one performs a fine iris and pupil detection. We selected 707 video frames recorded during clinical operations during OPT treatments performed at our institute. 650 frames were used for training and 57 for a blind test. The estimations of iris and pupil were evaluated against the manual labelled contours delineated by a clinical operator. For iris and pupil predictions, Dice coefficient (median = 0.94 and 0.97), Szymkiewicz–Simpson coefficient (median = 0.97 and 0.98), Intersection over Union coefficient (median = 0.88 and 0.94) and Hausdorff distance (median = 11.6 and 5.0 (pixels)) were quantified. Iris and pupil regions were found to be comparable to the manually labelled ground truths. Our proposed framework could provide an automatic approach to quantitatively evaluating pupil and iris misalignments, and it could be used as an additional support tool for clinical activity, without impacting in any way with the consolidated routine.

Image-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed tomography (sCT) generation from cone beam CT (CBCT) towards adaptive PT (APT) of paediatric patients. Firstly, 44 CBCTs of 15 young pelvic patients were pre-processed to reduce ring artefacts and rigidly registered on same-day CT scans (i.e., verification CT scans, vCT scans) and then inputted to the CycleGAN network (employing either Res-Net and U-Net generators) to synthesise sCT. In particular, 36 and 8 volumes were used for training and testing, respectively. Image quality was evaluated qualitatively and quantitatively using the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) between registered CBCT (rCBCT) and vCT and between sCT and vCT to evaluate the improvements brought by CycleGAN. Despite limitations due to the sub-optimal input image quality and the small field of view (FOV), the quality of sCT was found to be overall satisfactory from a quantitative and qualitative perspective. Our findings indicate that CycleGAN is promising to produce sCT scans with acceptable CT-like image texture in paediatric settings, even when CBCT with narrow fields of view (FOV) are employed.