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.

This paper provides insights into the use of Proton Beam Therapy (PBT) in pediatric patients with non-rhabdomyosarcoma soft tissue sarcomas (NRSTS). NRSTS are a heterogeneous group of rare and aggressive mesenchymal extraskeletal tumors, presenting complex and challenging clinical management scenarios. The overall survival rate for patients with NRSTS is around 70%, but the outcome is strictly related to the presence of various variables, such as the histological subtype, grade of malignancy and tumor stage at diagnosis. Multimodal therapy is typically considered the preferred treatment for high-grade NRSTS. Radiotherapy plays a key role in the treatment of children and adolescents with NRSTS. However, the potential for radiation-induced side effects partially limits its use. Therefore, PBT represents a very suitable therapeutic option for these patients. The unique depth-dose characteristics of protons can be leveraged to minimize doses to healthy tissue significantly, potentially allowing for increased tumor doses and enhanced preservation of surrounding tissues. These benefits suggest that PBT may improve local control while reducing toxicity and improving quality of life. While clear evidence of therapeutic superiority of PBT over other modern photon techniques in NRSTS is still lacking—partly due to the limited data available—PBT can be an excellent treatment option for young patients with these tumors. A dedicated international comprehensive collaborative approach is essential to better define its role within the multidisciplinary management of NRSTS. Shared guidelines for PBT indications—based on the patient’s age, estimated outcome, and tumor location—and centralization in high-level referral centers are needed to optimize the use of resources, since access to PBT remains a challenge due to the limited number of available proton therapy facilities.

Advances in therapeutic techniques and multimodal approaches have significantly improved the success rates of treatment for pediatric malignancies, with cure rates now close to 80%. This has led to an increase in long-term survival, with 0.10-0.15% of the general population being survivors of childhood cancer. In Italy, cancer registry data suggest that 75% of treated children become long-term survivors. However, these survivors face significant risks of late adverse events, including chronic diseases and severe conditions, highlighting the need for specialized follow-up care. Radiotherapy, a cornerstone of pediatric cancer treatment, contributes to late toxicities due to the susceptibility of growing tissues. Proton therapy offers advantages in reducing treatment-related toxicity, reducing the risk of secondary cancers, and allowing dose escalation for radioresistant tumors. Comparative studies suggest that proton therapy is superior in sparing healthy tissues and reducing long-term toxicities. Despite these benefits, challenges such as the high cost, limited proton therapy centers, and the need for clinical trials hinder the widespread adoption of proton therapy. Efforts to centralize care in high-ranking centers and ensure equitable access to proton therapy are crucial. In Italy, pediatric solid tumors are now eligible for proton therapy under national health policies, ensuring free access for all children. Dedicated proton therapy centers must provide comprehensive care involving multidisciplinary teams and supportive environments for pediatric patients and their families. Addressing current limitations and enhancing care environments are essential for improving outcomes for pediatric oncology patients.

The purpose of this retrospective study was to simulate a daily pre-alignment strategy to mitigate systematic positioning errors in image-guided pediatric hadron therapy. All pediatric patients (32 patients, 853 fractions) treated from December 2021 and September 2022 at our Institution were retrospectively considered. For all fractions, daily correction vectors (CVs) resulting from image registration for patient positioning were retrieved in the form of txt files from the hospital database. For each fraction, an adjusted correction vector (V′) was then computed as the difference between the actual one (V) and the algebraic average of the previous ones, as to simulate patient pre-alignment before imaging. The Euclidean norm of each V′ was computed and normalized with respect to that of the corresponding V to derive N. Pre-correcting all the coordinate values led to a 46% average reduction (min 20%, max 60%) in CVs, considering the first 27 fractions (average value in this cohort of patients). Such a potential improvement (N < 1) was observed for the most patients’ fractions (781/853, 91.6%). For the remaining 72/853 cases (8.4%), a remarkable worsening (N > 2) involved only 7/853 (0.82%) fractions. The presented strategy shows promising outcomes in order to ameliorate pediatric patient setup before imaging. However, further investigations to identify patients most likely to benefit from this approach are warranted.

Aim: An Early-Stage Non-Small Cell Lung Cancer (ES-NSCLC) patient candidate for stereotactic body radiotherapy (SBRT) may start their treatment without a histopathological assessment, due to relevant comorbidities. The aim of this study is twofold: (i) build prognostic models to test the association between CT-derived radiomic features (RFs) and the outcomes of interest (overall survival (OS), progression-free survival (PFS) and loco-regional progression-free survival (LRPFS)); (ii) quantify whether the combination of clinical and radiomic descriptors yields better prediction than clinical descriptors alone in prognostic modeling for ES-NSCLC patients treated with SBRT. Methods: Simulation CT scans of ES-NSCLC patients treated with curative-intent SBRT at the European Institute of Oncology (IEO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy between 2013 and 2023 were retrospectively retrieved. PyRadiomics v3.0.1 was used for image preprocessing and subsequent RFs extraction and selection. A radiomic score was calculated for each patient, and three prognostic models (clinical model, radiomic model, clinical-radiomic model) for each survival endpoint were built. Relative performances were compared using the C-index. All analyses were considered statistically significant if p < 0.05. The statistical analyses were performed using R Software version 4.1. Results: A total of 100 patients met the inclusion criteria. Median age at diagnosis was 76 (IQR: 70–82) years, with a median Charlson Comorbidity Index (CCI) of 7 (IQR: 6–8). At the last available follow-up, 76 patients were free of disease, 17 were alive with disease, and 7 were deceased. Considering relapses, progression of any kind was diagnosed in 31 cases. Regarding model performances, the radiomic score allowed for excellent prognostic discrimination for all the considered endpoints. Of note, the use of RFs alone proved to be more informative than clinical characteristics alone for the prediction of both OS and LRPFS, but not for PFS, for which the individual predictive performances slightly favored the clinical model. Conclusion: The use of RFs for outcome prediction in this clinical setting is promising, and results seem to be rather consistent across studies, despite some methodological differences that should be acknowledged. Further studies are being planned in our group to externally validate these findings, and to better determine the potential of RFs as non-invasive and reproducible biomarkers in ES-NSCLC.