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Abstract Proton therapy gives less dose to healthy tissue compared to conventional X-ray therapy, but systematic comparisons of normal tissue responses are lacking. The aim of this study was to investigate late tissue responses in the salivary glands following proton- or X-irradiation of the head and neck in mice. Moreover, we aimed at investigating molecular responses by monitoring the cytokine levels in serum and saliva. Female C57BL/6J mice underwent local fractionated irradiation with protons or X-rays to the maximally tolerated acute level. Saliva and serum were collected before and at different time points after irradiation to assess salivary gland function and cytokine expression. To study late responses in the major salivary glands, histological analyses were performed on tissues collected at day 105 after onset of irradiation. Saliva volume after proton and X-irradiation was significantly lower than for controls and remained reduced at all time points after irradiation. Protons caused reduced saliva production and fewer acinar cells in the submandibular glands compared to X-rays at day 105. X-rays induced a stronger inflammatory cytokine response in saliva compared to protons. This work supports previous preclinical findings and indicate that the relative biological effectiveness of protons in normal tissue might be higher than the commonly used value of 1.1.

Abstract Combining radiation therapy with immunotherapy is a strategy to improve both treatments. The purpose of this study was to compare responses for two syngeneic head and neck cancer (HNC) tumor models in mice following X-ray or proton irradiation with or without immune checkpoint inhibition (ICI). MOC1 (immunogenic) and MOC2 (less immunogenic) tumors were inoculated in the right hind leg of each mouse (C57BL/6J, n = 398). Mice were injected with anti-PDL1 (10 mg/kg, twice weekly for 2 weeks), and tumors were treated with single-dose irradiation (5–30 Gy) with X-rays or protons. MOC2 tumors grew faster and were more radioresistant than MOC1 tumors, and all mice with MOC2 tumors developed metastases. Irradiation reduced the tumor volume in a dose-dependent manner. ICI alone reduced the tumor volume for MOC1 with 20% compared to controls, while no reduction was seen for MOC2. For MOC1, there was a clear treatment synergy when combining irradiation with ICI for radiation doses above 5 Gy and there was a tendency for X-rays being slightly more biologically effective compared to protons. For MOC2, there was a tendency of protons being more effective than X-rays, but both radiation types showed a small synergy when combined with ICI. Although the responses and magnitudes of the therapeutic effect varied, the optimal radiation dose for maximal synergy appeared to be in the order of 10–15 Gy, regardless of tumor model.

BackgroundRadiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI.PurposeThe purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images.Materials and methodsFDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew’s correlation coefficient (MCC), and the F1 score calculated for both classes separately.ResultsCNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours.ConclusionHigh performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.

Abstract The main objective of radiotherapy is to exploit the curative potential of ionizing radiation while inflicting minimal radiation-induced damage to healthy tissue and sensitive organs. Proton beam therapy has been developed to irradiate the tumor with higher precision and dose conformity compared to conventional X-ray irradiation. The dose conformity of this treatment modality may be further improved if narrower proton beams are used. Still, this is limited by multiple Coulomb scattering of protons through tissue. The primary aim of this work was to develop techniques to produce narrow proton beams and investigate the resulting dose profiles. We introduced and assessed three different proton beam shaping techniques: (1) metal collimators (100/150 MeV), (2) focusing of conventional- (100/150 MeV), and (3) focusing of high-energy (350 MeV, shoot-through) proton beams. Focusing was governed by the initial value of the Twiss parameter $$\alpha$$ α ( $$\alpha _0$$ α 0 ), and can be implemented with magnetic particle accelerator optics. The dose distributions in water were calculated by Monte Carlo simulations using Geant4, and evaluated by target to surface dose ratio (TSDR) in addition to the transverse beam size ( $$\sigma _T$$ σ T ) at the target. The target was defined as the location of the Bragg peak or the focal point. The different techniques showed greatly differing dose profiles, where focusing gave pronouncedly higher relative target dose and efficient use of primary protons. Metal collimators with radii $$~3.6~\hbox {mm}$$ > 3.6 mm ). In contrast, a focused beam of conventional ( $$150~\hbox {MeV}$$ 150 MeV ) energy produced a very high TSDR ( $$>~80$$ > 80 ) with similar $$\sigma _T$$ σ T as a collimated beam. High-energy focused beams were able to produce TSDRs $$>~100$$ > 100 and $$\sigma _T$$ σ T around 1.5 mm. From this study, it appears very attractive to implement magnetically focused proton beams in radiotherapy of small lesions or tumors in close vicinity to healthy organs at risk. This can also lead to a paradigm change in spatially fractionated radiotherapy. Magnetic focusing would facilitate FLASH irradiation due to low losses of primary protons.

Abstract Background Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy. Methods This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients. Discussion The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.

Extracellular vesicles (EVs) are membrane-bound particles released from cells, and their cargo can alter the function of recipient cells. EVs from X-irradiated cells have been shown to play a likely role in non-targeted effects. However, EVs derived from proton irradiated cells have not yet been studied. We aimed to investigate the proteome of EVs and their cell of origin after proton or X-irradiation. The EVs were derived from a human oral squamous cell carcinoma (OSCC) cell line exposed to 0, 4, or 8 Gy from either protons or X-rays. The EVs and irradiated OSCC cells underwent liquid chromatography–mass spectrometry for protein identification. Interestingly, we found different protein profiles both in the EVs and in the OSCC cells after proton irradiation compared to X-irradiation. In the EVs, we found that protons cause a downregulation of proteins involved in cell growth and DNA damage response compared to X-rays. In the OSCC cells, proton and X-irradiation induced dissimilar cell death pathways and distinct DNA damage repair systems. These results are of potential importance for understanding how non-targeted effects in normal tissue can be limited and for future implementation of proton therapy in the clinic.

BackgroundRadiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose to surrounding tissues. Currently the GTV is contoured manually in medical images, which is a time-consuming and challenging task.PurposeThe purpose of this study was to evaluate the applicability of deep learning-based automatic segmentation of the GTV in canine patients with HNC.Materials and methodsContrast-enhanced computed tomography (CT) images and corresponding manual GTV contours of 36 canine HNC patients and 197 human HNC patients were included. A 3D U-Net convolutional neural network (CNN) was trained to automatically segment the GTV in canine patients using two main approaches: (i) training models from scratch based solely on canine CT images, and (ii) using cross-species transfer learning where models were pretrained on CT images of human patients and then fine-tuned on CT images of canine patients. For the canine patients, automatic segmentations were assessed using the Dice similarity coefficient (Dice), the positive predictive value, the true positive rate, and surface distance metrics, calculated from a four-fold cross-validation strategy where each fold was used as a validation set and test set once in independent model runs.ResultsCNN models trained from scratch on canine data or by using transfer learning obtained mean test set Dice scores of 0.55 and 0.52, respectively, indicating acceptable auto-segmentations, similar to the mean Dice performances reported for CT-based automatic segmentation in human HNC studies. Automatic segmentation of nasal cavity tumors appeared particularly promising, resulting in mean test set Dice scores of 0.69 for both approaches.ConclusionIn conclusion, deep learning-based automatic segmentation of the GTV using CNN models based on canine data only or a cross-species transfer learning approach shows promise for future application in RT of canine HNC patients.

Cytokines are mediators of inflammation that could lead to fibrosis. The aim was to monitor cytokine levels in saliva and serum after locally fractionated radiotherapy of the head and neck in mice and investigate associations with salivary gland fibrosis and hyposalivation. C57BL/6 mice were randomized to sham or X-ray irradiation of 66 Gy in 10 fractions over 5 days. Blood and saliva were collected on days −7, 5, 35, 80, and 105 following cytokine analysis. The harvested submandibular salivary gland was assessed for the presence of fibrosis. Decision tree regression analysis was used to investigate whether cytokine levels could predict late endpoints in terms of hyposalivation or fibrosis. Significant formation of fibrosis in gland tissue and reduced saliva production was found after irradiation. The pro-inflammatory cytokines IL-1α, TNF, TIMP1, G-CSF, KC, and MIP-1α showed increased levels in saliva in irradiated mice and a strong correlation with late endpoints. The decision tree analysis largely separated controls from irradiated animals, with IL-1α being the strongest predictor. Pro-inflammatory cytokines in saliva, but not in serum, were associated with late endpoints. This indicates that cytokine expression in saliva is a good biomarker for local salivary gland damage with IL-1α as the strongest single predictor.

Background and purpose: Dose painting by numbers (DPBN) require a high degree of dose modulation to fulfill the image-based voxel wise dose prescription. The aim of this study was to assess the dosimetric accuracy of 18F-fluoro-2-deoxy-glucose positron emission tomography(18F-FDG-PET)-based DPBN in an anthropomorphic lung phantom using alanine dosimetry. Materials and methods: A linear dose prescription based on 18F-FDG-PET image intensities within the gross tumor volume (GTV) of a lung cancer patient was employed. One DPBN scheme with low dose modulation (Scheme A; minimum/maximum fraction dose to the GTV 2.92/4.26 Gy) and one with a high modulation (Scheme B; 2.81/4.52 Gy) were generated. The plans were transferred to a computed tomograpy (CT) scan of a thorax phantom based on CT images of the patient. Using volumetric modulated arc therapy (VMAT), DPBN was delivered to the phantom with embedded alanine dosimeters. A plan was also delivered to an intentionally misaligned phantom. Absorbed doses at various points in the phantom were measured by alanine dosimetry. Results: A pointwise comparison between GTV doses from prescription, treatment plan calculation and VMAT delivery showed high correspondence, with a mean and maximum dose difference of

Hyper-radiosensitivity (HRS) is the increased sensitivity to low doses of ionizing radiation observed in most cell lines. We previously demonstrated that HRS is permanently abolished in cells irradiated at a low dose rate (LDR), in a mechanism dependent on transforming growth factor β3 (TGF-β3). In this study, we aimed to elucidate the activation and receptor binding of TGF-β3 in this mechanism. T-47D cells were pretreated with inhibitors of potential receptors and activators of TGF-β3, along with addition of small extracellular vesicles (sEVs) from LDR primed cells, before their radiosensitivity was assessed by the clonogenic assay. The protein content of sEVs from LDR primed cells was analyzed with mass spectrometry. Our results show that sEVs contain TGF-β3 regardless of priming status, but only sEVs from LDR primed cells remove HRS in reporter cells. Inhibition of the matrix metalloproteinase (MMP) family prevents removal of HRS, suggesting an MMP-dependent activation of TGF-β3 in the LDR primed cells. We demonstrate a functional interaction between TGF-β3 and activin receptor like kinase 1 (ALK1) by showing that TGF-β3 removes HRS through ALK1 binding, independent of ALK5 and TGF-βRII. These results are an important contribution to a more comprehensive understanding of the mechanism behind TGF-β3 mediated removal of HRS.