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.

Outcome prediction based on [18F]FDG PET/CT in patients with pleural mesothelioma treated with ipilimumab and nivolumab +/- UV1 telomerase vaccine.

PURPOSE: The introduction of immunotherapy in pleural mesothelioma (PM) has highlighted the need for effective outcome predictors. This study explores the role of [18F]FDG PET/CT in predicting outcomes in PM treated with immunotherapy. METHODS: Patients from the NIPU trial, receiving ipilimumab and nivolumab +/- telomerase vaccine in second-line, were included. [18F]FDG PET/CT was obtained at baseline (n = 100) and at week-5 (n = 76). Metabolic tumour volume (MTV) and peak standardised uptake value (SUVpeak) were evaluated in relation to survival outcomes. Wilcoxon rank-sum test was used to assess differences in MTV, total lesion glycolysis (TLG), maximum standardised uptake value (SUVmax) and SUVpeak between patients exhibiting an objective response, defined as either partial response or complete response according to the modified Response Criteria in Solid Tumours (mRECIST) and immune RECIST (iRECIST), and non-responders, defined as either stable disease or progressive disease as their best overall response. RESULTS: Univariate Cox regression revealed significant associations of MTV with OS (HR 1.36, CI: 1.14, 1.62, p < 0.001) and PFS (HR 1.18, CI: 1.03, 1.34, p = 0.02), while multivariate analysis showed a significant association with OS only (HR 1.35, CI: 1.09, 1.68, p = 0.007). While SUVpeak was not significantly associated with OS or PFS in univariate analyses, it was significantly associated with OS in multivariate analysis (HR 0.43, CI: 0.23, 0.80, p = 0.008). Objective responders had significant reductions in TLG, SUVmax and SUVpeak at week-5. CONCLUSION: MTV provides prognostic value in PM treated with immunotherapy. High SUVpeak was not associated with inferior outcomes, which could be attributed to the distinct mechanisms of immunotherapy. Early reductions in PET metrics correlated with treatment response. STUDY REGISTRATION: The NIPU trial (NCT04300244) is registered at clinicaltrials.gov. https://classic. CLINICALTRIALS: gov/ct2/show/NCT04300244?cond=Pleural+Mesothelioma&cntry=NO&draw=2&rank=4.

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.

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.

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.

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.

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.

Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI.

BACKGROUND: Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time and increase delineation consistency. In this study, the applicability of deep learning for fully automatic delineation of the gross tumour volume (GTV) in patients with anal squamous cell carcinoma (ASCC) was evaluated for the first time. An extensive comparison of the effects single modality and multimodality combinations of computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) have on automatic delineation quality was conducted. MATERIAL AND METHODS: 18F-fluorodeoxyglucose PET/CT and contrast-enhanced CT (ceCT) images were collected for 86 patients with ASCC. A subset of 36 patients also underwent a study-specific 3T MRI examination including T2- and diffusion-weighted imaging. The resulting two datasets were analysed separately. A two-dimensional U-Net convolutional neural network (CNN) was trained to delineate the GTV in axial image slices based on single or multimodality image input. Manual GTV delineations constituted the ground truth for CNN model training and evaluation. Models were evaluated using the Dice similarity coefficient (Dice) and surface distance metrics computed from five-fold cross-validation. RESULTS: CNN-generated automatic delineations demonstrated good agreement with the ground truth, resulting in mean Dice scores of 0.65-0.76 and 0.74-0.83 for the 86 and 36-patient datasets, respectively. For both datasets, the highest mean Dice scores were obtained using a multimodal combination of PET and ceCT (0.76-0.83). However, models based on single modality ceCT performed comparably well (0.74-0.81). T2W-only models performed acceptably but were somewhat inferior to the PET/ceCT and ceCT-based models. CONCLUSION: CNNs provided high-quality automatic GTV delineations for both single and multimodality image input, indicating that deep learning may prove a versatile tool for target volume delineation in future patients with ASCC.

Low-Dose-Rate Radiation-Induced Secretion of TGF-ß3 Together with an Activator in Small Extracellular Vesicles Modifies Low-Dose Hyper-Radiosensitivity through ALK1 Binding.

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 ß3 (TGF-ß3). In this study, we aimed to elucidate the activation and receptor binding of TGF-ß3 in this mechanism. T-47D cells were pretreated with inhibitors of potential receptors and activators of TGF-ß3, 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-ß3 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-ß3 in the LDR primed cells. We demonstrate a functional interaction between TGF-ß3 and activin receptor like kinase 1 (ALK1) by showing that TGF-ß3 removes HRS through ALK1 binding, independent of ALK5 and TGF-ßRII. These results are an important contribution to a more comprehensive understanding of the mechanism behind TGF-ß3 mediated removal of HRS.

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients.

PURPOSE: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV from FDG-PET/CT images of patients with head and neck cancer (HNC). CNN models were compared to manual GTV delineations made by experienced specialists. New structure-based performance metrics were introduced to enable in-depth assessment of auto-delineation of multiple malignant structures in individual patients. METHODS: U-Net CNN models were trained and evaluated on images and manual GTV delineations from 197 HNC patients. The dataset was split into training, validation and test cohorts (n= 142, n = 15 and n = 40, respectively). The Dice score, surface distance metrics and the new structure-based metrics were used for model evaluation. Additionally, auto-delineations were manually assessed by an oncologist for 15 randomly selected patients in the test cohort. RESULTS: The mean Dice scores of the auto-delineations were 55%, 69% and 71% for the CT-based, PET-based and PET/CT-based CNN models, respectively. The PET signal was essential for delineating all structures. Models based on PET/CT images identified 86% of the true GTV structures, whereas models built solely on CT images identified only 55% of the true structures. The oncologist reported very high-quality auto-delineations for 14 out of the 15 randomly selected patients. CONCLUSIONS: CNNs provided high-quality auto-delineations for HNC using multimodality PET/CT. The introduced structure-wise evaluation metrics provided valuable information on CNN model strengths and weaknesses for multi-structure auto-delineation.

Repeatability of hypoxia dose painting by numbers based on EF5-PET in head and neck cancer.

BACKGROUND: Hypoxia dose painting is a radiotherapy technique to increase the dose to hypoxic regions of the tumour. Still, the clinical effect relies on the reproducibility of the hypoxic region shown in the medical image. 18F-EF5 is a hypoxia tracer for positron emission tomography (PET), and this study investigated the repeatability of 18F-EF5-based dose painting by numbers (DPBN) in head and neck cancer (HNC). MATERIALS AND METHODS: Eight HNC patients undergoing two 18F-EF5-PET/CT sessions (A and B) before radiotherapy were included. A linear conversion of PET signal intensity to radiotherapy dose prescription was employed and DPBN treatment plans were created using the image basis acquired at each PET/CT session. Also, plan A was recalculated on the image basis for session B. Voxel-by-voxel Pearson's correlation and quality factor were calculated to assess the DPBN plan quality and repeatability. RESULTS: The mean (SD) correlation coefficient between DPBN prescription and plan was 0.92 (0.02) and 0.93 (0.02) for sessions A and B, respectively, with corresponding quality factors of 0.02 (0.002) and 0.02 (0.003), respectively. The mean correlation between dose prescriptions at day A and B was 0.72 (0.13), and 0.77 (0.12) for the corresponding plans. A mean correlation of 0.80 (0.08) was found between plan A, recalculated on image basis B, and plan B. CONCLUSION: Hypoxia DPBN planning based on 18F-EF5-PET/CT showed high repeatability. This illustrates that 18F-EF5-PET provides a robust target for dose painting.

Treatment outcomes and prognostic factors after chemoradiotherapy for anal cancer.

BACKGROUND: Squamous cell carcinoma of the anus (SCCA) is a rare malignancy with rising incidence, associated with human papilloma virus (HPV). Chemoradiotherapy (CRT) is the preferred treatment. The purpose was to investigate treatment failure, survival and prognostic factors after CRT. MATERIAL AND METHODS: In this prospective observational study from a large regional centre, 141 patients were included from 2013 to 2017, and 132 were eligible for analysis. The main inclusion criteria were SCCA, planned radiotherapy, and performance status (ECOG) ≤2. Patient characteristics, disease stage, treatment, and treatment response were prospectively registered. Disease-free survival (DFS), overall survival (OS), and locoregional treatment failure after CRT were analysed. Hazard ratios (HRs) were estimated with Cox`s proportional hazards model. RESULTS: Median follow-up was 54 (range 6-71) months. Eighteen patients (14%) had treatment failures after CRT; of these 10 (8%) had residual tumour, and 8 (6%) relapse as first failure. The first treatment failure was locoregional (11 patients), distant (5 patients), and both (2 patients). Salvage abdomino-perineal resection was performed in 10 patients, 2 had resections of metastases, and 3 both. DFS was 85% at 3 years and 78% at 5 years. OS was 93% at 3 years and 86% at 5 years. In analyses adjusted for age and gender, HPV negative tumours (HR 2.5, p = 0.024), N3 disease (HR 2.6, p = 0.024), and tumour size ≥4 cm (HR 2.4, p = 0.038) were negative prognostic factors for DFS. CONCLUSION: State-of-the-art chemoradiotherapy for SCCA resulted in excellent outcomes, and improved survival compared with previous national data, with <15% treatment failures and a 3-year DFS of >80%.

Re-irradiation for recurrent rectal cancer - a single-center experience.

Background: There is no clear consensus on the use of re-irradiation (reRT) in the management of locally recurrent rectal cancer (LRRC). The aim of the present study was to investigate all reRT administered for rectal cancer at a large referral institution and to evaluate patient outcomes and toxicity.Material and methods: All patients with rectal cancer were identified who had received previous pelvic radiotherapy (RT) and underwent reRT during 2006-2016. Medical records and RT details of the primary tumor treatments and rectal cancer recurrence treatments were registered, including details on reRT, chemotherapy, surgery, adverse events, and long-term outcomes.Results: Of 77 patients who received ReRT, 67 had previously received pelvic RT for rectal cancer and were administered reRT for LRRC. Re-irradiation doses were 30.0-45.0 Gy, most often given as hyperfractionated RT in 1.2-1.5 Gy fractions twice daily with concomitant capecitabine. The median time since initial RT was 29 months (range, 13-174 months). Of 36 patients considered as potentially resectable, 20 underwent surgery for LRRC within 3 months after reRT. Operated patients had better 3-year overall survival (OS) (62%) compared to those who were not operated (16%; HR 0.32, p = .001). The median gross tumor volume (GTV) was 107 cm3, and 3-year OS was significantly better in patients with GTV <107 cm3 (44%) compared to patients with GTV ≥107 cm3 (21%; HR 0.52, p = .03).Conclusion: Three-year survival was significantly better for patients who underwent surgery after reRT or who had small tumor volume. Prospective clinical trials are recommended for further improvements in patient selection, outcomes, and toxicity assessment.

Synthesis, radiosynthesis, and positron emission tomography neuroimaging using 5-[18 F]fluoro-L-amino suberate.

System xc- (Sxc -) has emerged as a new biological target for PET studies to detect oxidative and excitotoxic stress. Notably, applications have, thus far, been limited to tumour imaging although Sxc- ) may play a major role in neurodegeneration. The synthesis procedures of tosylate precursor and its translation to Sxc - PET tracer 5[18F]fluoro-L-amino suberate by manual and automated radiosyntheses are described. A brain-PET study has been conducted to evaluate the tracer uptake into brain in healthy mice.

Influx rate of 18F-fluoroaminosuberic acid reflects cystine/glutamate antiporter expression in tumour xenografts.

PURPOSE: 18F-fluoroaminosuberic acid (18F-FASu) is a recently developed amino acid tracer for positron emission tomography (PET) of oxidative stress that may offer improved tumour assessment over the conventional tracer 18F-fluorodeoxyglucose (18F-FDG). Our aim was to evaluate and relate dynamic 18F-FASu and 18F-FDG uptake with pharmacokinetic modelling to transporter protein expression levels in a panel of diverse tumour xenograft lines. METHODS: Four different tumour xenograft lines were implanted in female athymic nude mice: MAS98.12 and HBCx3 (breast), TPMX (osteosarcoma) and A549 (lung). Dynamic PET over 60 min was performed on a small animal unit. The time-activity curves (TACs) for 18F-FASu and 18F-FDG in individual tumours were used to extract early (SUVE; 2 min p.i.) and late (SUVL; 55 min p.i.) standardised uptake values. Pharmacokinetic two-tissue compartment models were applied to the TACs to estimate rate constants K1-k4 and blood volume fraction vB. Relative levels of cystine/glutamate antiporter subunit xCT were assessed by western blotting, and expression of GLUT1 and CD31 by immunohistochemistry. RESULTS: 18F-FASu showed higher SUVE, whilst 18F-FDG exhibited higher SUVL. Influx rate K1 for 18F-FASu was significantly correlated with xCT levels (p = 0.001) and was significantly higher than K1 for 18F-FDG (p < 0.001). K1 for 18F-FDG was significantly correlated with GLUT1 levels (p = 0.002). vB estimated from 18F-FASu and 18F-FDG TACs was highly consistent and significantly correlated (r = 0.85, p < 0.001). Two qualitatively different 18F-FASu uptake profiles were identified: type α with low xCT expression and low K1 (A549 and HBCx3), and type ß with high xCT expression and high K1 (MAS98.12 and TPMX). CONCLUSION: The influx rate of 18F-FASu reflects xCT activity in tumour xenografts. Dynamic PET with pharmacokinetic modelling is needed to fully appraise 18F-FASu distribution routes.

Dose painting for re-irradiation of head and neck cancer.

BACKGROUND: For patients with recurrent or second primary disease, re-irradiation can be challenging due to overlap with previously irradiated volumes. Dose painting may be attractive for these patients, as the focus is on delivering maximal dose to areas of high tumor activity. Here, we compare dose painting by contours (DPBC) treatment plans based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) with conventional plans. MATERIAL AND METHODS: We included 10 patients with recurrent or second primary head and neck cancer (HNC) eligible for re-irradiation. Our conventional re-irradiation regimen is hyperfractionated radiotherapy 1.5 Gy twice daily over 4 weeks, giving a total dose of 60 Gy. For DPBC, we defined two prescription volumes, PV33 and PV66, corresponding to 33 and 66% of the highest FDG uptake in the tumor. The clinical target volume (CTV) prescription dose was 60 Gy, PV33; 65-67 Gy and PV66; 70-73 Gy. The DPBC plan is to be given the first 20 fractions and the conventional plan the last 20 fractions. Dose to organs at risk (OARs) were compared for DPBC and conventional treatment. By summation of the initial curative plan and the re-irradiation plan, we also evaluated differences in dose to the 2 ccm hot spot (D2cc). RESULTS: We achieved DPBC plans with adequate target coverage for all 10 patients. There were no significant differences in OAR doses between the standard plans and the DPBC plans (p=.7). Summation of the initial curative plan and the re-irradiation plan showed that the median D2cc increased from 130 Gy (range 113-132 Gy; conventional) to 140 Gy (range 115-145 Gy; DPBC). CONCLUSIONS: Our proposed DPBC could be straightforwardly implemented and all plans met the objectives. Re-irradiation of HNC with DPBC may increase tumor control without more side effects compared to conventional radiotherapy.

A new method to assess pulmonary changes using 18F-fluoro-2-deoxyglucose positron emission tomography for lung cancer patients following radiotherapy.

BACKGROUND: 18F-fluoro-2-deoxyglucose positron emission tomography (18F-FDG-PET) may be used for assessing radiation induced alterations in the lung. However, there is a need to further develop methodologies to improve quantification. Using computed tomography (CT), a local structure method has been shown to be superior to conventional CT-based analysis. Here, we investigate whether the local structure method based on 18F-FDG-PET improves radiotherapy (RT) dose-response quantification for lung cancer patients. MATERIAL AND METHODS: Sixteen patients with lung cancer undergoing fractionated RT were examined by 18F-FDG-PET/CT at three sessions (pre, mid, post) and the lung was delineated in the planning CT images. The RT dose matrix was co-registered with the PET images. For each PET image series, mean (µ) and standard deviation (σ) maps were calculated based on cubes in the lung (3 × 3 × 3 voxels), where the spread in pre-therapy µ and σ was characterized by a covariance ellipse in a sub-volume of 3 × 3 × 3 cubes. Mahalanobis distance was used to measure the distance of individual cube values to the origin of the ellipse and to further form local structure 'S' maps. The structural difference maps (ΔS) and mean difference maps (Δµ) were calculated by subtracting pre-therapy maps from maps at mid- and post-therapy. Corresponding maps based on CT images were also generated. RESULTS: ΔS identified new areas of interest in the lung compared to conventional Δµ maps. ΔS for PET and CT gave a significantly elevated lung signal compared to a control group during and post-RT (p < .05). Dose-response analyses by linear regression showed that ΔS between pre- and post-therapy for 18F-FDG-PET was the only parameter significantly associated with local lung dose (p = .04). CONCLUSIONS: The new method using local structures on 18F-FDG-PET provides a clearer uptake dose-response compared to conventional analysis and CT-based approaches and may be valuable in future studies addressing lung toxicity.

Monte Carlo simulations of a low energy proton beamline for radiobiological experiments.

BACKGROUND: In order to determine the relative biological effectiveness (RBE) of protons with high accuracy, radiobiological experiments with detailed knowledge of the linear energy transfer (LET) are needed. Cell survival data from high LET protons are sparse and experiments with low energy protons to achieve high LET values are therefore required. The aim of this study was to quantify LET distributions from a low energy proton beam by using Monte Carlo (MC) simulations, and to further compare to a proton beam representing a typical minimum energy available at clinical facilities. MATERIALS AND METHODS: A Markus ionization chamber and Gafchromic films were employed in dose measurements in the proton beam at Oslo Cyclotron Laboratory. Dose profiles were also calculated using the FLUKA MC code, with the MC beam parameters optimized based on comparisons with the measurements. LET spectra and dose-averaged LET (LETd) were then estimated in FLUKA, and compared with LET calculated from an 80 MeV proton beam. RESULTS: The initial proton energy was determined to be 15.5 MeV, with a Gaussian energy distribution of 0.2% full width at half maximum (FWHM) and a Gaussian lateral spread of 2 mm FWHM. The LETd increased with depth, from approximately 5 keV/µm in the entrance to approximately 40 keV/µm in the distal dose fall-off. The LETd values were considerably higher and the LET spectra were much narrower than the corresponding spectra from the 80 MeV beam. CONCLUSIONS: MC simulations accurately modeled the dose distribution from the proton beam and could be used to estimate the LET at any position in the setup. The setup can be used to study the RBE for protons at high LETd, which is not achievable in clinical proton therapy facilities.

Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning.

BACKGROUND: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach. MATERIALS AND METHODS: A method for tumour segmentation based on image voxel classification using Fisher?s Linear Discriminant Analysis (LDA) was developed. This was applied to magnetic resonance (MR) images of 78 patients with locally advanced cervical cancer. The segmentation was based on multiparametric MRI consisting of T2- weighted (T2w), T1-weighted (T1w) and dynamic contrast-enhanced (DCE) sequences, and included intensity and spatial information from the images. The model was trained and assessed using delineations made by two radiologists. RESULTS: Segmentation based on T2w or T1w images resulted in mean sensitivity and specificity of 94% and 52%, respectively. Including DCE-MR images improved the segmentation model?s performance significantly, giving mean sensitivity and specificity of 85?93%. Comparisons with radiologists? tumour delineations gave Dice similarity coefficients of up to 0.44. CONCLUSION: Voxel classification using a machine learning approach is a flexible and fully automatic method for tumour delineation. Combining all relevant MR image series resulted in high sensitivity and specificity. Moreover, the presented method can be extended to include additional imaging modalities.