Search

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

Background: Lymphopenia during radiotherapy (RT) may have an adverse effect on treatment outcome. The aim of this study is to investigate associations between lymphopenia and RT parameters in patients with advanced lung cancer. Moreover, to investigate the prognostic role of lymphopenia, blood protein levels, and treatment and patient-related factors. Material and Methods: Sixty-two advanced stage non-small cell lung cancer (NSCLC) patients were retrospectively analyzed. Blood counts were available prior to, during, and after RT (3Gyx10). For each patient, a thoracic volume of interest (VOI) –including thoracic soft tissue and trabecular bone– was obtained by applying a CT window of −500 to 1200 HU in the planning CT. Dose parameters from thoracic VOI and other regions including lungs and vertebrae were calculated. Association between risk of lymphopenia ≥ G3 (lymphocytes at nadir according to CTCAE v4.0) and therapeutic parameters was investigated using Logistic regression. Relationships between overall survival (OS) and RT dose parameters, baseline blood counts and protein levels, and clinical factors were evaluated using Log-rank and Cox models. Result: Mean thoracic RT dose (odds ratio [OR] 1.67; p = 0.04), baseline lymphocytes (OR 0.65; p = 0.01), and corticosteroids use (OR 6.07; p = 0.02) were significantly associated with increased risk of lymphopenia ≥ G3 in multivariable analysis. Worse OS was associated with high mean thoracic RT dose, high CRP/Albumin, large tumor volume and corticosteroids use (p 0.12 (hazard ratio [HR] 2.28, p = 0.03) and corticosteroid use (HR 2.52, p = 0.01) were independently associated with worse OS. Conclusion: High thoracic RT dose gave a higher risk of lymphopenia ≥ G3; hence limiting dose volume to the thorax may be valuable in preventing severe lymphopenia for patients receiving palliative fractionated RT. Still, lymphopenia ≥ G3 was not associated with worse OS. however, high baseline CRP/Albumin was associated with poorer OS and may carry important information as a prognostic factor of OS in advanced NSCLC receiving palliative RT. Keywords: Radiotherapy, Hematologic toxicity, Lymphopenia, Corticosteroid, C-reactive protein/Albumin, Lung cancer, Overall survival

Abstract Background Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) may be used to depict tumour vascular structure and for therapy response assessment in various tumour sites. The purpose of the current work is to examine whether ultra-early changes in tumour physiology following cytotoxic treatment with doxorubicin and liver X receptor (LXR) agonist GW3965 are detectable by DCE-MRI. Methods 36 female, athymic nude foxn1nu mice with bilaterally implanted breast cancer xenografts (17 with ER-positive HBCx34, 19 with triple-negative HBCx39) were randomised in the following treatment groups; control, GW3965 (40 mg/kg p.o.), doxorubicin (8 mg/kg i.v.) and a combination therapy of GW3965 and doxorubicin. DCE-MRI (3D FLASH on a 7 T preclinical scanner) was performed at baseline and one and six days after onset of treatment. Wash-in (30 s p.i.) and wash-out (300 s p.i.) enhancement were quantified from dynamic uptake curves, before voxel-by-voxel fitting to the pharmacokinetic Tofts model and generation of maps for the resulting parameters K trans, νe and νB. Treatment effect was evaluated by univariate repeated measures mixed-effects maximum likelihood regression models applied to median tumour data. Results We found no effects of any treatment 24 h post treatment. After 6 days, doxorubicin given as both mono- and combination therapy gave significant increases of ~ 30% in wash-in enhancement (p 0.1). Conclusions Twenty-four h after onset of treatment was too early to evaluate treatment effects by DCE-MRI. Early enhancement and K trans were approximately equally sensitive metrics to capture treatment effects six days pt. Pharmacokinetic modelling however allowed us to attribute the observed effect to changes in tumour perfusion rather than increased retention.

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.

Background: Emerging biomarkers from medical imaging or molecular characterization of tumour biopsies open up for combining the two and exploiting their synergy in treatment planning of cancer patients. We generated a paired data set of imaging- and gene-based hypoxia biomarkers in cervical cancer, appraised the influence of intratumour heterogeneity in patient classification, and investigated the benefit of combining the methodologies in prediction of chemoradiotherapy failure. Methods: Hypoxic fraction from dynamic contrast enhanced (DCE)-MR images and an expression signature of six hypoxia-responsive genes were assessed as imaging- and gene-based biomarker, respectively in 118 patients. Findings: Dichotomous biomarker cutoff to yield similar hypoxia status by imaging and genes was defined in 41 patients, and the association was validated in the remaining 77 patients. The two biomarkers classified 75% of 118 patients with the same hypoxia status, and inconsistent classification was not related to imaging-defined intratumour heterogeneity in hypoxia. Gene-based hypoxia was independent on tumour cell fraction in the biopsies and showed minor heterogeneity across multiple samples in 9 tumours. Combining imaging- and gene-based classification gave a significantly better prediction of PFS than one biomarker alone. A combined dichotomous biomarker optimized in 77 patients showed a large separation in PFS between more and less hypoxic tumours, and separated the remaining 41 patients with different PFS. The combined biomarker showed prognostic value together with tumour stage in multivariate analysis. Interpretation: Combining imaging- and gene-based biomarkers may enable more precise and informative assessment of hypoxia-related chemoradiotherapy resistance in cervical cancer. Funding: Norwegian Cancer Society, South-Eastern Norway Regional Health Authority, and Norwegian Research Council.

Background and purpose: Multimodal imaging is increasingly included in the assessment of prostate cancer patients, and there is a need to study whether different techniques provide similar or complementary information. In the initial perfusion phase contrast agents and radioactive labelled tracers act as blood-pool agents and may show similar characteristics. The purpose of the current work was to compare time-activity- and time-concentration-curves (TCs) of dynamic 18F-fluciclovine (18F-anti-1-amino-2-[F]-fluorocyclobutane-1-carboxylic acid, FACBC) positron emission tomography (PET) and dynamic contrast-enhanced magnetic resonance imaging (DCE MRI). Materials and methods: Dynamic FACBC PET and DCE MRI were performed on 22 patients with intermediate or high-risk prostate cancer within 23 days prior to robot-assisted laparoscopic prostatectomy. Index tumour was delineated in the images using whole mount tissue sections as reference standard. Tumour TCs from PET and MRI were compared visually and quantitatively by calculating correlation coefficients between the curves at different time points after injection. Results: For the first minute post injection, the mean correlation coefficient between the TCs from PET and MRI was 0.92 (range; 0.75–0.99). After the first minute, MRI showed washout while PET showed plateau kinetics. Conclusion: Dynamic FACBC and DCE MRI showed similar wash-in time curve characteristics. At later time points, FACBC plateaued whereas MR contrast medium washed out. In DCE MRI, the usefulness of wash-in information is well documented. Whether wash-in information from dynamic FACBC can provide added value remains to be documented. Keywords: Prostate carcinoma, Fluciclovine, FACBC, MRI, Uptake characteristics

Purpose: Radiation therapy effectively kills cancer cells and elicits local effects in the irradiated tissue. The aim of this study was to investigate the kinetics of cytokines in the serum of patients with lung cancer undergoing radiation therapy and to identify associations with metabolic tumor burden as determined by 2-deoxy-2-fluoro-D-glucose (18F-FDG) positron emission tomography (PET). Methods and materials: Forty-five patients with advanced non-small cell lung cancer were included in a phase 2 clinical trial and randomized between fractionated thoracic radiation therapy alone or concurrent with an epidermal growth factor receptor inhibitor. Blood was sampled at 4 different time points: prior to treatment, midtherapy, at the end of therapy, and 6 to 8 weeks after the start of treatment. The serum concentrations of 48 cytokines and 9 matrix metalloproteinases were measured with multiplex immunoassays. A subset of patients was examined by 18F-FDG PET/computed tomography before, during, and after radiation therapy. The maximum standardized uptake values (SUVmax) of the primary lung tumor, whole-body metabolic tumor volume, and total lesion glycolysis were calculated, and correlations between the PET parameters and cytokines were investigated. Results: The SUVmax decreased from baseline through midtherapy to posttherapy 18F-FDG PET/computed tomography (P = .018). The serum levels of C-C motif chemokine ligand (CCL) 23, CCL24, C-X3-C motif chemokine ligand 1, and interleukin-8 (C-X-C motif ligand [CXCL]8) were significantly correlated to SUVmax, metabolic tumor volume, and total lesion glycolysis before, during, and after radiation therapy. CXCL2 (P = .030) and CXCL6 (P = .010) decreased after the start of therapy and changed significantly across the sample time points. Serum concentrations of CCL15 (P = .031), CXCL2 (P = .028), and interleukin-6 (P = .007) were positively correlated to the irradiated volume during the second week of treatment. Conclusions: Cytokine serum levels vary and correlate with metabolic tumor burden in patients with advanced non-small cell lung cancer undergoing palliative thoracic radiation therapy.

Abstract Purpose To compare target volume delineation of anal cancer using positron emission tomography (PET) and magnetic resonance imaging (MRI) with respect to inter-observer and inter-modality variability. Methods Nineteen patients with anal cancer undergoing chemoradiotherapy were prospectively included. Planning computed tomography (CT) images were co-registered with 18F–fluorodexocyglucose (FDG) PET/CT images and T2 and diffusion weighted (DW) MR images. Three oncologists delineated the Gross Tumor Volume (GTV) according to national guidelines and the visible tumor tissue (GTVT). MRI and PET based delineations were evaluated by absolute volumes and Dice similarity coefficients. Results The median volume of the GTVs was 27 and 31 cm3 for PET and MRI, respectively, while it was 6 and 11 cm3 for GTVT. Both GTV and GTVT volumes were highly correlated between delineators (r = 0.90 and r = 0.96, respectively). The median Dice similarity coefficient was 0.75 when comparing the GTVs based on PET/CT (GTVPET) with the GTVs based on MRI and CT (GTVMRI). The median Dice coefficient was 0.56 when comparing the visible tumor volume evaluated by PET (GTVT_PET) with the same volume evaluated by MRI (GTVT_MRI). Margins of 1–2 mm in the axial plane and 7–8 mm in superoinferior direction were required for coverage of the individual observer’s GTVs. Conclusions The rather good agreement between PET- and MRI-based GTVs indicates that either modality may be used for standard target delineation of anal cancer. However, larger deviations were found for GTVT, which may impact future tumor boost strategies.

Purpose: To investigate effects of radiotherapy (RT) and erlotinib on pulmonary glucose uptake using 2-deoxy-2-(18F)fluoro-D-glucose (18F-FDG) positron emission tomography (PET) during and after treatment of non-small cell lung cancer (NSCLC) and to identify associations between serum cytokine levels and lung glucose uptake. Material and methods: Twenty-seven patients with advanced NSCLC, receiving RT alone or concomitant RT and erlotinib therapy, were examined by 18F-FDG PET before, during, and after treatment. A total of 57 18F-FDG PET scans were analyzed. Pulmonary 18F-FDG uptake and radiotherapy dose mapping were used to acquire dose-response curves for each patient, where subsequent linear regression gave a glucose uptake level in the un-irradiated parts of the lungs (SUV0) and a response slope (ΔSUV). Serum cytokine levels at corresponding time points were assessed using a multiplex bioassay. Correlations between the most robust cytokines and lung 18F-FDG dose response parameters were further investigated. Results: From the dose response analysis, SUV0 at post-therapy was significantly higher (P

Abstract Aim To evaluate the patterns of loco-regional recurrences in head and neck cancer patients Methods Twenty-six out of 112 patients treated with primary or postoperative 3D CRT or IMRT for their primary and recurrent disease between 2007 and 2013 were included. The CT images of recurrent disease were rigidly registered with the primary CT images for each patient. To assess overlaps and overlap localization, the recurrence volume overlapping with the primary target volume was identified. For relapses occurring in the regional lymph nodes, the epicenter distance in recurrences and primary volumes and dose in recurrences were also identified. The recurrences were defined as in-field, marginal or out-of-field. Results The majority of the failures occurred within 1 year after completed primary treatment. The dose differences in recurrence volume were not statistically significant when patients were treated with IMRT or 3D CRT. Recurrence in 15/26 of the included patients occurred in the regional lymph nodes located fully or partly inside the primary target volume or the elective lymph node region. The majority of recurrences were recognized as in-field, independent of the primary treatment. Conclusion Recurrence in the majority of the patients occurred in the regional lymph nodes located in high dose area. The cause of recurrence may be due to inadequate total dose in the primary treatment and/or lack of optimal primary diagnosis leading to inadequate primary target delineation.

Background and purpose: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) may be used to visualize tumor hypoxia, and was in this work explored in treatment planning of hypoxia-guided brachytherapy of patients with locally advanced cervical cancer (LACC). Materials and methods: Pharmacokinetic ABrix maps were derived from DCE-MR images taken prior to chemoradiotherapy of 78 patients with LACC. A logistic regression procedure was used to segment the tumor volume fraction from the ABrix maps that showed the strongest association with patient survival, denoted biological target volume (BTV) fraction. A hypoxia gene score was calculated from a biopsy-based gene signature and correlated against the BTV fraction. Brachytherapy planning based on the ABrix maps was performed, for 23 patients. A general planning aim was a minimum D90 dose of 7.5Â Gy to the tumor per brachytherapy fraction. Two planning approaches were explored: (1) a conventional uniform and (2) a non-uniform approach targeting the BTV to the highest dose possible. Results: The segmented BTV fraction was significantly associated local and locoregional control (PÂ =Â 0.025) and the hypoxia gene score (PÂ =Â 0.002). Comparing brachytherapy approaches 1 and 2, it was possible to dose escalate the BTV with 0.4Â Gy per fraction in median (D90; cohort range [0, 3.8]). Some tumors could not be dose escalated without violating the dose constraints to the organs at risk. Conclusions: Tumor regions associated with hypoxia may be targeted with brachytherapy. The presented methodology may become useful in future strategies to improve cure probability of resistant tumors. Keywords: Hypoxia, Uterine cervical cancer, Computer-assisted image analysis, Magnetic resonance imaging, Brachytherapy

Hypoxia is an adverse factor in cervical cancer, and hypoxia-related gene expression could be a powerful biomarker for identifying the aggressive hypoxic tumors. Reverse transcription quantitative PCR (RT-qPCR) is a valuable method for gene expression studies, but suitable reference genes for data normalization that are independent of hypoxia status and clinical parameters of cervical tumors are lacking. In the present work, we aimed to identify reference genes for RT-qPCR studies of hypoxia in squamous cervical cancer. From 422 candidate reference genes selected from the literature, we used Illumina array-based expression profiles to identify 182 genes not affected by hypoxia in cervical cancer, i.e. genes regulated by hypoxia in eight cervical cancer cell lines or correlating with the hypoxia-associated dynamic contrast-enhanced magnetic resonance imaging parameter ABrix in 42 patients, were excluded. Among the 182 genes, nine candidates (CHCHD1, GNB2L1, IPO8, LASP1, RPL27A, RPS12, SOD1, SRSF9, TMBIM6) that were not associated with tumor volume, stage, lymph node involvement or disease progression in array data of 150 patients, were selected for further testing by RT-qPCR. geNorm and NormFinder analyses of RT-qPCR data of 74 patients identified CHCHD1, SRSF9 and TMBIM6 as the optimal set of reference genes, with stable expression both overall and across patient subgroups with different hypoxia status (ABrix) and clinical parameters. The suitability of the three reference genes were validated in studies of the hypoxia-induced genes DDIT3, ERO1A, and STC2. After normalization, the RT-qPCR data of these genes showed a significant correlation with Illumina expression (P