Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

[This corrects the article DOI: 10.3389/fmed.2023.1217037.].

Combination of proton- or X-irradiation with anti-PDL1 immunotherapy in two murine oral cancers.

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.

TGF-ß3 increases the severity of radiation-induced oral mucositis and salivary gland fibrosis in a mouse model.

PURPOSE: Toxicities from head and neck (H&N) radiotherapy (RT) may affect patient quality of life and can be dose-limiting. Proteins from the transforming growth factor beta (TGF-ß) family are key players in the fibrotic response. While TGF-ß1 is known to be pro-fibrotic, TGF-ß3 has mainly been considered anti-fibrotic. Moreover, TGF-ß3 has been shown to act protective against acute toxicities after radio- and chemotherapy. In the present study, we investigated the effect of TGF-ß3 treatment during fractionated H&N RT in a mouse model. MATERIALS AND METHODS: 30 C57BL/6J mice were assigned to three treatment groups. The RT + TGF-ß3 group received local fractionated H&N RT with 66 Gy over five days, combined with TGF-ß3-injections at 24-hour intervals. Animals in the RT reference group received identical RT without TGF-ß3 treatment. The non-irradiated control group was sham-irradiated according to the same RT schedule. In the follow-up period, body weight and symptoms of oral mucositis and lip dermatitis were monitored. Saliva was sampled at five time points. The experiment was terminated 105 d after the first RT fraction. Submandibular and sublingual glands were preserved, sectioned, and stained with Masson's trichrome to visualize collagen. RESULTS: A subset of mice in the RT + TGF-ß3 group displayed increased severity of oral mucositis and increased weight loss, resulting in a significant increase in mortality. Collagen content was significantly increased in the submandibular and sublingual glands for the surviving RT + TGF-ß3 mice, compared with non-irradiated controls. In the RT reference group, collagen content was significantly increased in the submandibular gland only. Both RT groups displayed lower saliva production after treatment compared to controls. TGF-ß3 treatment did not impact saliva production. CONCLUSIONS: When repeatedly administered during fractionated RT at the current dose, TGF-ß3 treatment increased acute H&N radiation toxicities and increased mortality. Furthermore, TGF-ß3 treatment may increase the severity of radiation-induced salivary gland fibrosis.

Chronic fatigue in long-term survivors of head and neck cancer treated with radiotherapy.

BACKGROUND: There is lack of evidence on chronic fatigue (CF) following radiotherapy (RT) in survivors of head and neck cancer (HNC). We aimed to compare CF in HNC survivors > 5 years post-RT with a reference population and investigate factors associated with CF and the possible impact of CF on health-related quality of life (HRQoL). MATERIAL AND METHODS: In this cross-sectional study we included HNC survivors treated in 2007-2013. Participants filled in patient-reported outcome measures and attended a one-day examination. CF was measured with the Fatigue Questionnaire and compared with a matched reference population using t-tests and Cohen's effect size. Associations between CF, clinical and RT-related factors were investigated using logistic regression. HRQoL was measured with the EORTC Quality of Life core questionnaire. RESULTS: The median age of the 227 HNC survivors was 65 years and median time to follow-up was 8.5 years post-RT. CF was twice more prevalent in HNC survivors compared to a reference population. In multivariable analyses, female sex (OR 3.39, 95 % CI 1.82-6.31), comorbidity (OR 2.17, 95 % CI 1.20-3.94) and treatment with intensity-modulated RT (OR 2.13, 95 % CI 1.16-3.91) were associated with CF, while RT dose parameters were not. Survivors with CF compared to those without, had significantly worse HRQoL. CONCLUSIONS: CF in HNC survivors is particularly important for female patients, while specific factors associated with RT appear not to play a role. The high CF prevalence in long-term HNC survivors associated with impaired HRQoL is important information beneficial for clinicians and patients to improve patient follow-up.

Magnetic resonance imaging of anal cancer: tumor characteristics and early prediction of treatment outcome.

PURPOSE: To analyze tumor characteristics derived from pelvic magnetic resonance imaging (MRI) of patients with squamous cell carcinoma of the anus (SCCA) before and during chemoradiotherapy (CRT), and to compare the changes in these characteristics between scans of responders vs. nonresponders to CRT. METHODS: We included 52 patients with a pelvic 3T MRI scan prior to CRT (baseline scan); 39 of these patients received an additional scan during week 2 of CRT (second scan). Volume, diameter, extramural tumor depth (EMTD), and external anal sphincter infiltration (EASI) of the tumor were assessed. Mean, kurtosis, skewness, standard deviation (SD), and entropy values were extracted from apparent diffusion coefficient (ADC) histograms. The main outcome was locoregional treatment failure. Correlations were evaluated with Wilcoxon's signed rank-sum test and Pearson's correlation coefficient, quantile regression, univariate logistic regression, and area under the ROC curve (AUC) analyses. RESULTS: In isolated analyses of the baseline and second MRI scans, none of the characteristics were associated with outcome. Comparison between the scans showed significant changes in several characteristics: volume, diameter, EMTD, and ADC skewness decreased in the second scan, although the mean ADC increased. Small decreases in volume and diameter were associated with treatment failure, and these variables had the highest AUC values (0.73 and 0.76, respectively) among the analyzed characteristics. CONCLUSION: Changes in tumor volume and diameter in an early scan during CRT could represent easily assessable imaging-based biomarkers to eliminate the need for analysis of more complex MRI characteristics.

Radiation-induced long-term dysphagia in survivors of head and neck cancer and association with dose-volume parameters.

BACKGROUND: Although dysphagia is a common side effect after radiotherapy (RT) of head and neck cancer (HNC), data on long-term dysphagia is scarce. We aimed to 1) compare radiation dose parameters in HNC survivors with and without dysphagia, 2) investigate factors associated with long-term dysphagia and its possible impact on health-related quality of life (HRQoL), and 3) investigate how our data agree with existing NTCP models. METHODS: This cross-sectional study conducted in 2018-2020, included HNC survivors treated in 2007-2013. Participants attended a one-day examination in hospital and filled in patient questionnaires. Dysphagia was measured with the EORTC QLQ-H&N35 swallowing scale. Toxicity was scored with CTCAE v.4. We contoured swallowing organs at risk (SWOAR) on RT plans, calculated dose-volume histograms (DVHs), performed logistic regression analyses and tested our data in established NTCP models. RESULTS: Of the 239 participants, 75 (31%) reported dysphagia. Compared to survivors without dysphagia, this group had reduced HRQoL and the DVHs for infrahyoid SWOAR were significantly shifted to the right. Long-term dysphagia was associated with age (OR 1.07, 95% CI 1.03-1.10), female sex (OR 2.75, 95% CI 1.45-5.21), and mean dose to middle pharyngeal constrictor muscle (MD-MPCM) (OR 1.06, 95% CI 1.03-1.09). NTCP models overall underestimated the risk of long-term dysphagia. CONCLUSIONS: Long-term dysphagia was associated with higher age, being female, and high MD-MPCM. Doses to distally located SWOAR seemed to be risk factors. Existing NTCP models do not sufficiently predict long-term dysphagia. Further efforts are needed to reduce the prevalence and consequences of this late effect.

Proton Compared to X-Irradiation Induces Different Protein Profiles in Oral Cancer Cells and Their Derived Extracellular Vesicles.

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.

Stability of metabolic tumor volume may enable radiotherapy dose painting in anal cancer.

PURPOSE: To evaluate the variability of the 18F-FDG-PET/CT-based metabolic tumor volume (MTV) in anal cancers during fractionated chemoradiotherapy (CRT), and assess the impact of this variability on dosimetric accuracy in MTV-targeted dose painting. METHODS: Eleven patients with anal squamous cell carcinoma who received fractionated chemoradiotherapy with curative intent were included. 18F-FDG PET/CT images were acquired at pre- and mid-treatment. Target volumes and organs at risk (OARs) were contoured manually on both image series. The MTV was generated from the PET images by thresholding. Treatment plans were retrospectively optimized for both image series using volumetric modulated arc therapy (VMAT). Standard plans prescribed 48.6 Gy, 54 Gy and 57.5 Gy in 27 fractions to elective regions, lymph node metastases and primary tumor, respectively. Dose painting plans included an extra dose level of 65 Gy to the MTV. Pre-treatment plans were transferred and re-calculated at mid-treatment basis. RESULTS: MTV decreased from pre- to mid-treatment in 10 of the 11 patients. On average, 71 % of MTVmid overlapped with MTVpre. The median and mean doses to the MTV were robust against anatomical changes, but the transferred dose painting plans had lower D98% values than the original and re-optimized plans. No major differences were found between standard and dose painting plans for OARs. CONCLUSIONS: Despite volumetric changes in the MTV, adequate dose coverage was observed in most dose painting plans. The findings indicate little or no need for adaptive dose painting at mid-treatment. Dose painting appears to be a safe treatment alternative with similar dose sparing of OARs.

Cytokine Levels in Saliva Are Associated with Salivary Gland Fibrosis and Hyposalivation in Mice after Fractionated Radiotherapy of the Head and Neck.

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.

Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

Background: Radiomics 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. Purpose: The 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 methods: FDG 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. Results: CNNs 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. Conclusion: High 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.

Acute normal tissue responses in a murine model following fractionated irradiation of the head and neck with protons or X-rays.

BACKGROUND: The purpose of this study was to investigate acute normal tissue responses in the head and neck region following proton- or X-irradiation of a murine model. MATERIALS AND METHODS: Female C57BL/6J mice were irradiated with protons (25 or 60 MeV) or X-rays (100 kV). The radiation field covered the oral cavity and the major salivary glands. For protons, two different treatment plans were used, either with the Bragg Peak in the middle of the mouse (BP) or outside the mouse (transmission mode; TM). Delivered physical doses were 41, 45, and 65 Gy given in 6, 7, and 10 fractions for BP, TM, and X-rays, respectively. Alanine dosimetry was used to assess delivered doses. Oral mucositis and dermatitis were scored using CTC v.2.0-based tables. Saliva was collected at baseline, right after end of irradiation, and at day 35. RESULTS: The measured dose distribution for protons (TM) and X-rays was very similar. Oral mucositis appeared earlier, had a higher score and was found in a higher percentage of mice after proton irradiation compared to X-irradiation. Dermatitis, on the other hand, had a similar appearance after protons and X-rays. Compared to controls, saliva production was lower right after termination of proton- and X-irradiation. The BP group demonstrated saliva recovery compared to the TM and X-ray group at day 35. CONCLUSION: With lower delivered doses, proton irradiation resulted in similar skin reactions and increased oral mucositis compared to X-irradiation. This indicates that the relative biological effectiveness of protons for acute tissue responses in the mouse head and neck is greater than the clinical standard of 1.1. Thus, there is a need for further investigations of the biological effect of protons in normal tissues.

A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers.

Locally advanced cervical cancer (LACC) and anal and oropharyngeal squamous cell carcinoma (ASCC and OPSCC) are mostly caused by oncogenic human papillomaviruses (HPV). In this paper, we developed machine learning (ML) models based on clinical, biological, and radiomic features extracted from pre-treatment fluorine-18-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET) images to predict the survival of patients with HPV-induced cancers. For this purpose, cohorts from five institutions were used: two cohorts of patients treated for LACC including 104 patients from Gustave Roussy Campus Cancer (Center 1) and 90 patients from Leeds Teaching Hospitals NHS Trust (Center 2), two datasets of patients treated for ASCC composed of 66 patients from Institut du Cancer de Montpellier (Center 3) and 67 patients from Oslo University Hospital (Center 4), and one dataset of 45 OPSCC patients from the University Hospital of Zurich (Center 5). Radiomic features were extracted from baseline [18F]-FDG PET images. The ComBat technique was applied to mitigate intra-scanner variability. A modified consensus nested cross-validation for feature selection and hyperparameter tuning was applied on four ML models to predict progression-free survival (PFS) and overall survival (OS) using harmonized imaging features and/or clinical and biological variables as inputs. Each model was trained and optimized on Center 1 and Center 3 cohorts and tested on Center 2, Center 4, and Center 5 cohorts. The radiomic-based CoxNet model achieved C-index values of 0.75 and 0.78 for PFS and 0.76, 0.74, and 0.75 for OS on the test sets. Radiomic feature-based models had superior performance compared to the bioclinical ones, and combining radiomic and bioclinical variables did not improve the performances. Metabolic tumor volume (MTV)-based models obtained lower C-index values for a majority of the tested configurations but quite equivalent performance in terms of time-dependent AUCs (td-AUC). The results demonstrate the possibility of identifying common PET-based image signatures for predicting the response of patients with induced HPV pathology, validated on multi-center multiconstructor data.

Combined RBE and OER optimization in proton therapy with FLUKA based on EF5-PET.

INTRODUCTION: Tumor hypoxia is associated with poor treatment outcome. Hypoxic regions are more radioresistant than well-oxygenated regions, as quantified by the oxygen enhancement ratio (OER). In optimization of proton therapy, including OER in addition to the relative biological effectiveness (RBE) could therefore be used to adapt to patient-specific radioresistance governed by intrinsic radiosensitivity and hypoxia. METHODS: A combined RBE and OER weighted dose (ROWD) calculation method was implemented in a FLUKA Monte Carlo (MC) based treatment planning tool. The method is based on the linear quadratic model, with α and ß parameters as a function of the OER, and therefore a function of the linear energy transfer (LET) and partial oxygen pressure (pO2 ). Proton therapy plans for two head and neck cancer (HNC) patients were optimized with pO2 estimated from [18 F]-EF5 positron emission tomography (PET) images. For the ROWD calculations, an RBE of 1.1 (RBE1.1,OER ) and two variable RBE models, Rørvik (ROR) and McNamara (MCN), were used, alongside a reference plan without incorporation of OER (RBE1.1 ). RESULTS: For the HNC patients, treatment plans in line with the prescription dose and with acceptable target ROWD could be generated with the established tool. The physical dose was the main factor modulated in the ROWD. The impact of incorporating OER during optimization of HNC patients was demonstrated by the substantial difference found between ROWD and physical dose in the hypoxic tumor region. The largest physical dose differences between the ROWD optimized plans and the reference plan was 12.2 Gy. CONCLUSION: The FLUKA MC based tool was able to optimize proton treatment plans taking the tumor pO2 distribution from hypoxia PET images into account. Independent of RBE-model, both elevated LET and physical dose were found in the hypoxic regions, which shows the potential to increase the tumor control compared to a conventional optimization approach.

An investigation of luminescence properties of CaF2: Dy nanophosphor irradiated with gamma rays and low energy proton beams.

This paper reports the luminescence properties of nanocrystalline calcium fluoride doped with dysprosium (CaF2: Dy). The nanophosphor has been synthesized by the chemical co-precipitation technique and the dopant concentration has been optimized at 0.3 mol% using thermoluminescence (TL) intensity emitted post 50Gy gamma dose irradiation of samples doped with different dopant concentrations. X-ray diffraction shows the formation of crystalline particles with an average size of 49.233 nm. Photoluminescence (PL) emission spectrum shows the characteristic peaks at 455 nm, 482 nm, 573 nm corresponding to 4I15/2 to 6H15/2, 4F9/2 to 6H15/2 and 4F9/2 to 6H13/2 Dy3+ transitions respectively. PL excitation spectrum shows a peak at 327 nm which corresponds to the Dy3+ transition of 6H15/2 to 4L19/2. Gamma (of 1.25 MeV) and low energy proton beam (of 30 keV) irradiated nanophosphor shows a variation in TL glow curve structure and peak position with an increase in radiation dose/fluence. However, the nanophosphor shows a wide linear dose response for 60Co gamma radiation in the range 10 Gy - 1.5 kGy and for low energy proton beam in the fluence range of 1012-1014 ions/cm2. Srim 2013 has been used to calculate the ion beam parameters including the range of protons in CaF2: Dy 0.3 mol%. The nanophosphor CaF2: Dy could be further investigated as a potential dosimeter for gamma rays and proton beam by studying its TL properties for different energies of these radiations.

Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning.

Background: Radiotherapy (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. Purpose: The 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 methods: Contrast-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. Results: CNN 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. Conclusion: In 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.

2D mapping of radiation dose and clonogenic survival for accurate assessment ofin vitroX-ray GRID irradiation effects.

Spatially fractionated radiation therapy (SFRT or GRID) is an approach to deliver high local radiation doses in an 'on-off' pattern. To better appraise the radiobiological effects from GRID, a framework to link local radiation dose to clonogenic survival needs to be developed. A549 lung cancer cells were irradiated in T25 cm2flasks using 220 kV x-rays with an open field or through a tungsten GRID collimator with periodical 5 mm openings and 10 mm blockings. Delivered nominal doses were 2, 5, and 10 Gy. A novel approach for image segmentation was used to locate the centroid of surviving colonies in scanned images of the cell flasks. GafchromicTMfilm dosimetry (GFD) and FLUKA Monte Carlo (MC) simulations were employed to map the dose at each surviving colony centroid. Fitting the linear-quadratic (LQ) function to clonogenic survival data for open field irradiation, the expected survival level at a given dose level was calculated. The expected survival levels were then mapped together with the observed levels in the GRID-irradiated flasks. GFD and FLUKA MC gave similar dose distributions, with a mean peak-to-valley dose ratio of about 5. LQ-parameters for open field irradiation gaveα=0.24±0.02Gy-1andß=0.019±0.002Gy-2. The mean relative percentage deviation between observed and predicted survival in the (peak; valley) dose regions was (4.6; 3.1) %, (26.6; -1.0) %, and (129.8; -2.3) % for 2, 5 and 10 Gy, respectively. In conclusion, a framework for mapping of surviving colonies following GRID irradiation together with predicted survival levels from homogeneous irradiation was presented. For the given cell line, our findings indicate that GRID irradiation causes reduced survival in the peak regions compared to an open field configuration.

A preclinical model to investigate normal tissue damage following fractionated radiotherapy to the head and neck.

Radiotherapy (RT) of head and neck (H&N) cancer is known to cause both early- and late-occurring toxicities. To better appraise normal tissue responses and their dependence on treatment parameters such as radiation field and type, as well as dose and fractionation scheme, a preclinical model with relevant endpoints is required. 12-week old female C57BL/6 J mice were irradiated with 100 or 180 kV X-rays to total doses ranging from 30 to 85 Gy, given in 10 fractions over 5 days. The radiation field covered the oral cavity, swallowing structures and salivary glands. Monte Carlo simulations were employed to estimate tissue dose distribution. The follow-up period was 35 days, in order to study the early radiation-induced effects. Baseline and post irradiation investigations included macroscopic and microscopic examinations of the skin, lips, salivary glands and oral mucosa. Saliva sampling was performed to assess the salivary gland function following radiation exposure. A dose dependent radiation dermatitis in the skin was observed for doses above 30 Gy. Oral mucositis in the tongue appeared as ulcerations on the ventral surface of the tongue for doses of 75-85 Gy. The irradiated mice showed significantly reduced saliva production compared to controls. In summary, a preclinical model to investigate a broad panel of normal tissue responses following fractionated irradiation of the H&N region was established. The optimal dose to study early radiation-induced effects was found to be around 75 Gy, as this was the highest tolerated dose that gave acute effects similar to that observed in cancer patients.

Sharp dose profiles for high precision proton therapy using strongly focused proton beams.

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 [Formula: see text] ([Formula: see text]), 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 ([Formula: see text]) 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 [Formula: see text] gave low TSDRs ([Formula: see text]) and large [Formula: see text]([Formula: see text]). In contrast, a focused beam of conventional ([Formula: see text]) energy produced a very high TSDR ([Formula: see text]) with similar [Formula: see text] as a collimated beam. High-energy focused beams were able to produce TSDRs [Formula: see text] and [Formula: see text] 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.

Risk of recurrence after chemoradiotherapy identified by multimodal MRI and 18F-FDG-PET/CT in locally advanced cervical cancer.

BACKGROUND AND PURPOSE: MRI, applying dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) sequences, and 18F-fluorodeoxyglucose (18F-FDG) PET/CT provide information about tumor aggressiveness that is unexploited in treatment of locally advanced cervical cancer (LACC). We investigated the potential of a multimodal combination of imaging parameters for classifying patients according to their risk of recurrence. MATERIALS AND METHODS: Eighty-two LACC patients with diagnostic MRI and FDG-PET/CT, treated with chemoradiotherapy, were collected. Thirty-eight patients with MRI only were included for validation of MRI results. Endpoints were survival (disease-free, cancer-specific, overall) and tumor control (local, locoregional, distant). Ktrans, reflecting vascular function, apparent diffusion coefficient (ADC), reflecting cellularity, and standardized uptake value (SUV), reflecting glucose uptake, were extracted from DCE-MR, DW-MR and FDG-PET images, respectively. By applying an oxygen consumption and supply-based method, ADC and Ktrans parametric maps were voxel-wise combined into hypoxia images that were used to determine hypoxic fraction (HF). RESULTS: HF showed a stronger association with outcome than the single modality parameters. This association was confirmed in the validation cohort. Low HF identified low-risk patients with 95% precision. Based on the 50th SUV-percentile (SUV50), patients with high HF were divided into an intermediate- and high-risk group with high and low SUV50, respectively. This defined a multimodality biomarker, HF/SUV50. HF/SUV50 increased the precision of detecting high-risk patients from 41% (HF alone) to 57% and showed prognostic significance in multivariable analysis for all endpoints. CONCLUSION: Multimodal combination of MR- and FDG-PET/CT-images improves classification of LACC patients compared to single modality images and clinical factors.