Background and purpose: In gliomas, characterization of the molecular landscape plays a critical role in determining prognosis and guiding treatment regimens. Imaging biomarker models hold promise for non-invasive characterization of glioma subtypes. We, comprehensively, assessed the potential of magnetic resonance imaging (MRI) data for predicting the survival status and molecular subtypes of glioma.

Methods: We introduce a novel method for quantifying the spatial distribution of gliomas within brain anatomy. The method measures the volumetric ratio of 32 brain anatomical structures affected by the tumor. This novel feature set was combined with established radiomics to build models for predicting O6-methylguanine-DNA methyltransferase (MGMT) methylation status, isocitrate dehydrogenase (IDH) mutation status, and Overall Survival (OS) time of glioma patients. The performance of these models was evaluated on preoperative MRIs of 1788 subjects from four independent datasets, employing both cross-validation (CV) and cross-dataset evaluation strategies.

Results: The proposed feature set revealed no regular patterns in tumor locations across the brain. Integration of these features with radiomics improved model performance for the three tasks. The best performance, in terms of AUROC, respectively, for CV and cross-data tests were: 0.685 and 0.628 for MGMT status, 0.972 and 0.764 for IDH status, and 0.748 and 0.719 for OS time status.

Conclusions: Our experiments demonstrate the potential of imaging biomarkers for IDH prediction, highlighting the challenges associated with predicting MGMT and OS only from image data. This underscores the need for additional information beyond MRI, for accurate prediction of these prognostic markers.

Background: Although out-of-field radiation doses are lower than in-field doses, they may still contribute to late toxicities and second cancer risk in classical Hodgkin lymphoma (cHL) patients. Moreover, treatment planning systems (TPS) often underestimate these doses, especially in healthy tissues.

Methods: Dosimetric parameters were compared between pencil beam scanning proton therapy (PBS) and volumetric modulated arc therapy (VMAT) in a cohort of 20 patients from the PRO-Hodgkin study. Planning CT scans were reconstructed into virtual whole-body CTs using the IS²aR software. Out-of-field doses were estimated with analytical models and Monte Carlo simulations in whole-body phantoms, then integrated with TPS in-field doses to calculate total equivalent dose. Dose distributions, dose–volume histograms (DVHs), non-target tissue integral dose equivalent (NTIHs), mean organ doses, and lifetime attributable risks of Second cancer (LARs) were evaluated for critical healthy tissues.

Results: PBS consistently resulted in lower out-of-field doses than VMAT. In both modalities, contributions were relatively small, not exceeding 5% of the prescribed dose. For organs near the target such as thyroid, lungs, breasts, heart, and esophagus, mean dose differences between PBS and VMAT ranged from 1.3 to 4.5 Sv, while for distant organs differences remained below 0.3 Sv. Incorporating out-of-field doses into LAR estimates increased predicted risks, but PBS reduced these values by up to 40% compared to VMAT.

Conclusion: The radiation burden in high-precision radiotherapy, particularly from out-of-field radiation, should be accounted for in epidemiological studies and incorporated into treatment planning optimization to minimize second cancer risks in cHL patients.

Current radiotherapy for malignant tumors often adopts a “one-size-fits-all” approach, prescribing the same irradiation dose for patients with similar clinical indications. However, advancements in functional imaging allow for biologically individualized strategies, with dose distribution tailored to the specific tumor biology. This study proposes a novel approach to biologically individualized radiotherapy, exploiting the synergistic combination of the tumor clonogenic cell information from [¹⁸F]FDG PET images and radiosensitivity from [¹⁸F]fluoromisonidazole (FMISO) PET images. Methods: Twenty-eight patients with head and neck squamous cell carcinoma (HNSCC) were analyzed. Using imaging biomarkers, individualized tumor profiles were obtained from oxygen partial pressure and clonogenic cell density maps derived from [¹⁸F]FMISO and [¹⁸F]FDG PET, respectively. Dose-escalated radiotherapy plans aiming at 95% tumor control probability (TCP) were generated using automated planning. Plans were assessed for clinical feasibility and expected TCP. Results: Planned dose distributions achieved greater than 90% TCP in all cases. All treatment plans met standard clinical feasibility criteria for the main organs-at-risk constraints, except for the few cases with significant target overlap, demonstrating the overall feasibility of the personalized strategy. Conclusion: The proposed biologically individualized treatment strategy demonstrated feasibility and clinical applicability. Combining [¹⁸F]FDG and [¹⁸F]FMISO PET imaging potentially shifts the success rate of HNSCC treatment from approximately 60% at 5 y, as reported in the literature, to a projected TCP of 90%. This treatment strategy holds promise for improving patient outcomes through more precise and effective treatment.

Introduction: Biomarker-driven strategies are central to personalized oncology, yet local treatments such as radiotherapy still lack validated stratification frameworks. Conventional frequentist trial designs often require prohibitively large patient cohorts without incorporating previously validated data. We therefore propose a Bayesian trial framework, which utilizes information of a patient’s biomarker and historical information of the biomarker effect to optimize the assigned dose. The aim of this work is to develop and illustrate such a framework for radiotherapy in rectal cancer organ preservation.

Methods: We designed a prospective two-arm Bayesian response-adaptive trial concept incorporating the principle of optimal stopping. Biomarker measurements obtained during treatment guided dose adaptation, illustrated here using the imaging-derived early regression index (ERI). Feasibility and statistical performance were evaluated through a fully simulated trial in rectal cancer organ preservation. Sensitivity analysis was applied to investigate the effect of prior information on the posterior distribution.

Results: Simulations modeled the patients’ response using biomarker-dependent tumor control and biomarker-independent toxicity curves. One subgroup of patients would benefit most from moderate dose escalation, achieving improved tumor control without excessive toxicity. In contrast, poor and excellent responders gained limited additional benefit at clinically acceptable doses. Under the optimistic prior scenario, the proposed design could claim the benefit of the adaptive dose with fewer than 100 simulated patients, while with weakly informative priors less than 150 patients are needed.

Conclusion: This Bayesian response-adaptive design provides a quantitative framework to integrate biomarkers such as ERI into radiotherapy personalization. It enables a structured evaluation of dose–response relationships and may help facilitate the translation of biomarker findings into local cancer therapy, acknowledging the underlying model assumptions.

The “Hadrontherapy for Life” symposium in Caen, France, highlighted that a new era of radiobiology is fundamental for advancing particle therapy to the next level. A radiobiology capable of integrating molecular biology and omics technologies is needed to deeply analyze treatment responses and underlying mechanisms.

Key challenges discussed at the symposium included tumor hypoxia, which remains only partially mitigated by high-LET radiation, and the specificity of carbon ions, or more broadly, high-LET particles, considered as “new drugs” capable of providing systemic benefits beyond local tumor control, including their potential to promote immunogenicity. Moreover, emerging modalities, such as Ultra High Dose Rate irradiation and spatial fractionated beams, were also discussed, with consensus that all require dedicated and coordinated radiobiological investigations.

Infrastructure presentations highlighted the international capabilities of leading centers in Europe and Asia, emphasizing the importance of integrating radiobiology into clinical programs, advancing multi-ion experimentation, and adopting innovative experimental models, such as organoids and/or 3D cell cultures. Participants also stressed the need for greater access to animal experimentation facilities, which are essential for accelerating progress in the field. Furthermore, the meeting underscored translational endpoints such as biomarker development, a hot topic in current radiotherapy. The C400 accelerator enables Caen to incorporate radiobiology from its very inception, establishing a European hub for collaborative research. Round-table discussions emphasized the importance of harmonized protocols, dedicated in vivo irradiation rooms, international training programs with exchange of students and researchers, and comprehensive patient biobanking.

In summary, the symposium reinforced the essential role of radiobiology in advancing hadron therapy (HT), providing strategic directions for translational research, infrastructure development, and international collaborations to accelerate personalized and effective particle therapy.

Objective: To investigate the potential impact of two different combinations of neutron and gamma radiation on gene expression and dicentric chromosomes in peripheral blood mononuclear cells (PBMC).

Methods: Whole blood from 3 human donors was exposed to neutrons with an energy spectrum similar to that of the Hiroshima uranium bomb, to gamma radiation from a ⁶⁰Co source and to a 50:50 combination of both radiations, given in two orders of sequence. In all cases the total doses were 0.5, 0.75 and 1.0 ​Gy. Dicentric chromosomes were analyzed by light microscopy and the expression of six known radiation-responsive genes BBC3, CDKN1A, FDXR, GADD45A, MDM2, and XPC were analyzed by RT-qPCR.

Results: Per unit dose, exposure to neutrons lead to a higher level of dicentrics and gene expression as compared to gamma radiation. Dose-response relationships for both endpoints were linear, allowing calculating the expected outcome of combined exposure by arithmetic. For dicentric chromosomes, the RBE values for ⁶⁰Co → neutrons, neutrons → ⁶⁰Co and neutrons were 4.05, 3.62 and 7.30, respectively. For gene expression the RBE values were gene-specific, but showed values in the range of 1.14–3.01 for ⁶⁰Co → neutrons, 1.33–2.68 for neutrons → ⁶⁰Co and 1.39–3.91 for neutrons.

Conclusions: The results demonstrate that combined exposure to neutrons and gamma radiation, regardless of the order of sequence, leads to an additive response at both endpoints. This indicates that calibration curves for mixed beams can be constructed from dose response relationships of the single beam components.

Background: In clinical proton radiotherapy, a constant relative biological effectiveness (RBE) of 1.1 is typically applied. Due to abundant evidence of variable RBE effects from in vitro data, multiple variable RBE models have been suggested, typically by describing the α and β parameters in the linear quadratic (LQ) model as a function of dose averaged linear energy transfer (LETd).

Purpose: This work introduces a new variable RBE model based on the dirty dose concept, where dose deposited in voxels with a corresponding LET exceeding a specific threshold is considered “dirty” in the sense that it has a biological effect above the one predicted by a constant RBE of 1.1. As only one LET level, corresponding to a specific energy for a given particle in a given medium, needs to be monitored, this offers several advantages, such as simplified calculations by removing the need for intricate end of range LET calculations and averaging procedures, as well as opening up for more efficient experimental assessment of the cell specific model parameters.

Methods: Previously published in vitro data were utilized, where surviving fraction (SF), dose and LETd were reported for a pristine proton beam with varying physical PMMA thicknesses placed upstream of the cells. The setup was re-simulated to extract dirty dose metrics for the corresponding reported LETd-values. Models were created by setting the α parameter of the LQ model as a function of the fraction of dirty dose and subsequently benchmarked against models based on other radiation quality metrics by comparing the root-mean-square-error (RMSE) of the predicted and actual cell surviving fraction.

Results: Variable RBE models based on dirty dose perform on par with conventional radiation quality metrics with a RMSE of 0.38 for a dirty dose-based model with a threshold of 7 keV/μ⁢m, compared to 0.42 and 0.36 for a LETd-based and Qeff,d-based model, respectively. Higher chosen LET thresholds typically performed better and lower performed worse.

Conclusion: The results indicate that models based on dirty dose metrics perform equally well as conventional radiation quality metrics. Due to the simplified calculations involved and the potential for more efficient measurement techniques for data generation, dirty dose-based models might be the most conservative and practical approach for creating future proton variable RBE models.

The adoption of online adaptive MR-guided radiotherapy (MRgRT) for Head and Neck Cancer (HNC) treatment faces challenges due to the com- plexity of manual HNC tumor delineation. This study focused on the problem of HNC tumor segmentation and investigated the effects of different preprocessing techniques, robust segmentation models, and ensembling steps on segmentation accuracy to propose an optimal solution. We contributed to the MICCAI Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) challenge which contains segmentation of HNC tumors in Task1) pre-RT and Task2) mid- RT MR images. In the internal validation phase, the most accurate results were achieved by ensembling two models trained on maximally cropped and contrast- enhanced images which yielded average volumetric Dice scores of (0.680, 0.785) and (0.493, 0.810) for (GTVp, GTVn) on pre-RT and mid-RT volumes. For the final testing phase, the models were submitted under the team’s name of “Stock- holm_Trio” and the overall segmentation performance achieved aggregated Dice scores of (0.795, 0.849) and (0.553, 0.865) for pre- and mid-RT tasks, respectively. The developed models are available at https://github.com/Astarakee/miccai24.

Purpose: To propose a methodology for integrating the out-of-field and imaging doses to the in-field dose received by radiotherapy (RT) patients. In addition, the impact of considering the total dose in planning and radiation-induced second malignancies (RISM) risk assessment will be evaluated in several scenarios comprising photon and proton treatments.

Methods: The total dose is the voxel-wise sum of the doses from the different radiation sources (accounting for the radiobiological effectiveness) produced during the whole RT chain. The dose from the plan and imaging procedures were obtained by measurements for a photon prostate treatment and by calculation (combining treatment planning system, analytical models, and Monte Carlo simulations) for two lymphoma treatments, one using photons and the other, protons. Dose distributions, dose volume histograms (DVHs) metrics, mean organ doses, and RISM risks were evaluated for each radiation exposure in each treatment.

Results: In general, the contribution of the imaging doses is low compared to the dose administered during RT treatment, being higher in proton therapy. However, for some organs, for instance testes in the prostate case, the imaging dose becomes higher than the scattered dose from the treatment fields. Plan evaluations revealed shifts in cumulative DVHs with the inclusion of out-of-field and imaging doses, though minimal clinical impact is expected. Risk assessment showed increased estimates with total dose.

Conclusions: The methodology enables accounting for the total dose for optimization of plans and imaging protocols, prospective risk predictions and retrospective epidemiological analyses.