Reduced radiation-induced toxicity by using proton therapy for the treatment of oropharyngeal cancer

Patients with squamous cell carcinoma of the oropharynx are generally treated with (chemo) radiation. Patients with oropharyngeal cancer have better survival than patients with squamous cell carcinoma of other head and neck subsites, especially when related to human papillomavirus. However, radiotherapy results in a substantial percentage of survivors suffering from significant treatment-related side-effects. Late radiation-induced side-effects are mostly irreversible and may even be progressive, and particularly xerostomia and dysphagia affect health-related quality of life. As the risk of radiation-induced side-effects highly depends on dose to healthy normal tissues, prevention of radiation-induced xerostomia and dysphagia and subsequent improvement of health-relatedquality of life can be obtained by applying proton therapy, which offers the opportunity to reduce the dose to both the salivary glands and anatomic structures involved in swallowing.This review describes the results of the first cohort studies demonstrating that proton therapy results in lower dose levels in multiple organs at risk, which translates into reduced acute toxicity (i.e. up to 3 months after radiotherapy), while preserving tumour control. Next to reducing mucositis, tube feeding, xerostomia and distortion of the sense of taste, protons can improve general well-being by decreasing fatigue and nausea. Proton therapy results in decreased rates of tube feeding dependency and severe weight loss up to 1 year after radiotherapy, and may decrease the risk of radionecrosis of the mandible. Also, the model-based approach for selecting patients for proton therapy in the Netherlands is described in this review and future perspectives are discussed

Evaluation of CBCT-based synthetic CTs for clinical adoption in proton therapy of head & neck patients.:E-Poster

PurposeIn adaptive proton therapy, weekly verification CTs (rCTs) are commonly acquired and used to monitor patient anatomy. Cone-Beam CTs (CBCT) on the other hand are used for daily pre-treatment position verification. These CBCT images however suffer from severe imaging artifacts preventing accurate proton dose calculations, meaning that CBCTs are unsuitable for treatment planning purposes. Recent advances in converting CBCT images to high quality synthetic CTs (sCTs) using Deep Convolution Neural Networks (DCNN) show that these sCTs can be suitable for proton dose calculations and therefore assist clinical adaptation decisions.The aim of this study was to compare weekly high definition rCTs to same-day sCT images of head and neck cancer patients in order to verify dosimetric accuracy of DCNN generated CBCT-based sCTs.Materials and MethodsA dataset of 46 previously treated head and neck cancer patients was used to generate synthetic CTs from daily pre-treatment patient alignment CBCTs using a previously developed and trained U-net like DCNN. Proton dose was then recalculated on weekly rCTs and same-day sCTs utilizing clinical treatment plans. To assess the dosimetric accuracy of sCTs, dose to the clinical target volumes (CTV D98) and mean dose in selected organs-at-risk (OAR; Oral cavity, Parotid gland left, Submandibular gland right) was calculated and compared between rCTs and same-day sCTs. Furthermore, Normal Tissue Complication Probability (NTCP) models for xerostomia and dysphagia were used to assess the clinical significance of dose differences.ResultsFor target volumes, the average difference in D98% between rCT and sCT pairs (N=284) was 0.34±3.86 % [-0.18±2.06 Gy] for the low dose CTV (54.25 Gy) and 0.23±3.62 % [-0.16±2.48 Gy] for the high dose CTV (70 Gy). For the OARs the following mean dose differences were observed; Oral Cavity: 4.15±9.78 % [0.75±1.39 Gy], Parotid L: 5.34±11.6 % [0.58±1.40 Gy], Submandibular R: 2.17±8.55 % [0.55±2.57 Gy]. The average NTCP difference was -0.15±0.58 % for grade 3 dysphagia, -0.26±0.54 % for grade 3 xerostomia, -0.53±1.20 % for grade 2 dysphagia and -0.71±1.40 % for grade 2 xerostomia. ConclusionFor target coverage and NTCP difference, the deep learning based sCTs showed high agreement with weekly verification CTs. However, some outliers were observed (also indicated by the increased standard deviation) and warrant further investigation and improvements before clinical implementation. Furthermore, stringent quality control tools for synthetic CTs are required to allow reliable deployment in adaptive proton therapy workflows.<br/

Social Support, Identity Affirmation, and Psychological Well-Being: A Developmental and Intersectional Comparison between Italian Cisgender and Non-Binary People with Bisexual Orientation

Incorporating the perspectives of positive psychology, intersectionality, and life course into minority stress theory, this study aimed to examine the relationships between social support, identity affirmation, and psychological well-being among 483 Italian individuals with bisexual orientation, accounting for differences in gender identity (cisgender vs. non-binary) and age groups (young, early, and middle adult). A mediation model was tested in which identity affirmation served as a presumed mediator between social support and psychological well-being. We also examined whether gender identity and age group moderated the hypothesized associations. Multivariate ANOVA and multigroup mediation analyses were conducted. Results showed that (a) cisgender individuals had higher social support and psychological well-being than non-binary individuals, but not identity affirmation, which was higher in the latter group, (b) psychological well-being, but not social support and identity affirmation, differed between groups, with the youngest cohort reporting worse health than their elders, (c) identity affirmation mediated the relationship between social support and psychological well-being, (d) mediation was significant only in binary individuals (compared to cisgender), whereas no age differences were found. Overall, this study highlights the need to consider bisexual individuals as a nonhomogeneous population living multiple life experiences, especially when minority identities intersect

A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy

Background and purpose: Accurate segmentation of organs-at-risk (OARs) is crucial but tedious and time-consuming in adaptive radiotherapy (ART). The purpose of this work was to automate head and neck OAR-segmentation on repeat CT (rCT) by an optimal combination of human and auto-segmentation for accurate prediction of Normal Tissue Complication Probability (NTCP). Materials and methods: Human segmentation (HS) of 3 observers, deformable image registration (DIR) based contour propagation and deep learning contouring (DLC) were carried out to segment 15 OARs on 15 rCTs. The original treatment plan was re-calculated on rCT to obtain mean dose (D-mean) and con-sequent NTCP-predictions. The average Dmean and NTCP-predictions of the three observers were referred to as the gold standard to calculate the absolute difference of D-mean and NTCP-predictions (vertical bar AD(mean)vertical bar and vertical bar ANTCP vertical bar). Results: The average vertical bar AD(mean)vertical bar of parotid glands in HS was 1.40 Gy, lower than that obtained with DIR and DLC (3.64 Gy, p < 0.001 and 3.72 Gy, p < 0.001, respectively). DLC showed the highest vertical bar AD(mean)vertical bar in middle Pharyngeal Constrictor Muscle (PCM) (5.13 Gy, p = 0.01). DIR showed second highest vertical bar AD(mean)vertical bar in the cricopharyngeal inlet (2.85 Gy, p = 0.01). The semi auto-segmentation (SAS) adopted HS, DIR and DLC for segmentation of parotid glands, PCM and all other OARs, respectively. The 90th percentile vertical bar ANTCP vertical bar was 2.19%, 2.24%, 1.10% and 1.50% for DIR, DLC, HS and SAS respectively. Conclusions: Human segmentation of the parotid glands remains necessary for accurate interpretation of mean dose and NTCP during ART. Proposed semi auto-segmentation allows NTCP-predictions within 1.5% accuracy for 90% of the cases. (C) 2021 The Author(s). Published by Elsevier B.V

Proton arc therapy increases the benefit of proton therapy for oropharyngeal cancer patients in the model based clinic

Background and purpose: In the model-based approach, patients qualify for proton therapy when the reduction in risk of toxicity (ΔNTCP) obtained with IMPT relative to VMAT is larger than predefined thresholds as defined by the Dutch National Indication Protocol (NIPP). Proton arc therapy (PAT) is an emerging technology which has the potential to further decrease NTCPs compared to IMPT. The aim of this study was to investigate the potential impact of PAT on the number of oropharyngeal cancer (OPC) patients that qualify for proton therapy.Materials and methods: A prospective cohort of 223 OPC patients subjected to the model-based selection procedure was investigated. 33 (15%) patients were considered unsuitable for proton treatment before plan comparison. When IMPT was compared to VMAT for the remaining 190 patients, 148 (66%) patients qualified for protons and 42 (19%) patients did not. For these 42 patients treated with VMAT, robust PAT plans were generated.Results: PAT plans provided better or similar target coverage compared to IMPT plans. In the PAT plans, integral dose was significantly reduced by 18% relative to IMPT plans and by 54% relative to VMAT plans. PAT decreased the mean dose to numerous organs-at-risk (OARs), further reducing NTCPs. The ΔNTCP for PAT relative to VMAT passed the NIPP thresholds for 32 out of the 42 patients treated with VMAT, resulting in 180 patients (81%) of the complete cohort qualifying for protons.Conclusion: PAT outperforms IMPT and VMAT, leading to a further reduction of NTCP-values and higher ΔNTCP-values, significantly increasing the percentage of OPC patients selected for proton therapy.</p

Head and neck IMPT probabilistic dose accumulation:Feasibility of a 2 mm setup uncertainty setting

OBJECTIVE: To establish optimal robust optimization uncertainty settings for clinical head and neck cancer (HNC) patients undergoing 3D image-guided pencil beam scanning (PBS) proton therapy. METHODS: We analyzed ten consecutive HNC patients treated with 70 and 54.25 GyRBE to the primary and prophylactic clinical target volumes (CTV) respectively using intensity-modulated proton therapy (IMPT). Clinical plans were generated using robust optimization with 5 mm/3% setup/range uncertainties (RayStation v6.1). Additional plans were created for 4, 3, 2 and 1 mm setup and 3% range uncertainty and for 3 mm setup and 3%, 2% and 1% range uncertainty. Systematic and random error distributions were determined for setup and range uncertainties based on our quality assurance program. From these, 25 treatment scenarios were sampled for each plan, each consisting of a systematic setup and range error and daily random setup errors. Fraction doses were calculated on the weekly verification CT closest to the date of treatment as this was considered representative of the daily patient anatomy. RESULTS: Plans with a 2 mm/3% setup/range uncertainty setting adequately covered the primary and prophylactic CTV (V95≥ 99% in 98.8% and 90.8% of the treatment scenarios respectively). The average organ-at-risk dose decreased with 1.1 GyRBE/mm setup uncertainty reduction and 0.5 GyRBE/1% range uncertainty reduction. Normal tissue complication probabilities decreased by 2.0%/mm setup uncertainty reduction and by 0.9%/1% range uncertainty reduction. CONCLUSION: The results of this study indicate that margin reduction below 3 mm/3% is possible but requires a larger cohort to substantiate clinical introduction

Practical robustness evaluation in radiotherapy - A photon and proton-proof alternative to PTV-based plan evaluation

Background and purpose: A planning target volume (PTV) in photon treatments aims to ensure that the clinical target volume (CTV) receives adequate dose despite treatment uncertainties. The underlying static dose cloud approximation (the assumption that the dose distribution is invariant to errors) is problematic in intensity modulated proton treatments where range errors should be taken into account as well. The purpose of this work is to introduce a robustness evaluation method that is applicable to photon and proton treatments and is consistent with (historic) PTV-based treatment plan evaluations. Materials and methods: The limitation of the static dose cloud approximation was solved in a multi-scenario simulation by explicitly calculating doses for various treatment scenarios that describe possible errors in the treatment course. Setup errors were the same as the CTV-PTV margin and the underlying theory of 3D probability density distributions was extended to 4D to include range errors, maintaining a 90% confidence level. Scenario dose distributions were reduced to voxel-wise minimum and maximum dose distributions; the first to evaluate CTV coverage and the second for hot spots. Acceptance criteria for CTV D98 and D2 were calibrated against PTV-based criteria from historic photon treatment plans. Results: CTV D98 in worst case scenario dose and voxel-wise minimum dose showed a very strong correlation with scenario average D98 (R-2 > 0.99). The voxel-wise minimum dose visualised CTV dose conformity and coverage in 3D in agreement with PTV-based evaluation in photon therapy. Criteria for CTV D98 and D2 of the voxel-wise minimum and maximum dose showed very strong correlations to PTV D98 and D2 (R-2 > 0.99) and on average needed corrections of -0.9% and +2.3%, respectively. Conclusions: A practical approach to robustness evaluation was provided and clinically implemented for PTV-less photon and proton treatment planning, consistent with PTV evaluations but without its static dose cloud approximation. (C) 2019 The Authors. Published by Elsevier B.V

Development of advanced preselection tools to reduce redundant plan comparisons in model-based selection of head and neck cancer patients for proton therapy

PURPOSE: In the Netherlands, head and neck cancer (HNC) patients are selected for proton therapy (PT) based on estimated normal tissue complication probability differences (ΔNTCP) between photons and protons, which requires a plan comparison (VMAT vs. IMPT). We aimed to develop tools to improve patient selection for plan comparisons. METHODS: This prospective study consisted of 141 consecutive patients in which a plan comparison was done. IMPT plans of patients not qualifying for PT were classified as 'redundant'. To prevent redundant IMPT planning, 5 methods that were primarily based on regression models were developed to predict IMPT Dmean to OARs, by using data from VMAT plans and volumetric data from delineated targets and OARs. Then, actual and predicted plan comparison outcomes were compared. The endpoint was being selected for proton therapy. RESULTS: Seventy out of 141 patients (49.6%) qualified for PT. Using the developed preselection tools, redundant IMPT planning could have been prevented in 49-68% of the remaining 71 patients not qualifying for PT (=specificity) when the sensitivity of all methods was fixed to 100%, i.e., no false negative cases (positive predictive value range: 57-68%, negative predictive value: 100%). CONCLUSION: The advanced preselection tools, which uses volume and VMAT dose data, prevented labour intensive creation of IMPT plans in up to 68% of non-qualifying patients for PT. No patients qualifying for PT would have been incorrectly denied a plan comparison. This method contributes significantly to a more cost-effective model-based selection of HNC patients for PT

Whole genome identification of Mycobacterium tuberculosis vaccine candidates by comprehensive data mining and bioinformatic analyses

<p>Abstract</p> <p>Background</p> <p><it>Mycobacterium tuberculosis</it>, the causative agent of tuberculosis (TB), infects ~8 million annually culminating in ~2 million deaths. Moreover, about one third of the population is latently infected, 10% of which develop disease during lifetime. Current approved prophylactic TB vaccines (BCG and derivatives thereof) are of variable efficiency in adult protection against pulmonary TB (0%–80%), and directed essentially against early phase infection.</p> <p>Methods</p> <p>A genome-scale dataset was constructed by analyzing published data of: (1) global gene expression studies under conditions which simulate intra-macrophage stress, dormancy, persistence and/or reactivation; (2) cellular and humoral immunity, and vaccine potential. This information was compiled along with revised annotation/bioinformatic characterization of selected gene products and <it>in silico </it>mapping of T-cell epitopes. Protocols for scoring, ranking and prioritization of the antigens were developed and applied.</p> <p>Results</p> <p>Cross-matching of literature and <it>in silico</it>-derived data, in conjunction with the prioritization scheme and biological rationale, allowed for selection of 189 putative vaccine candidates from the entire genome. Within the 189 set, the relative distribution of antigens in 3 functional categories differs significantly from their distribution in the whole genome, with reduction in the Conserved hypothetical category (due to improved annotation) and enrichment in Lipid and in Virulence categories. Other prominent representatives in the 189 set are the PE/PPE proteins; iron sequestration, nitroreductases and proteases, all within the Intermediary metabolism and respiration category; ESX secretion systems, resuscitation promoting factors and lipoproteins, all within the Cell wall category. Application of a ranking scheme based on qualitative and quantitative scores, resulted in a list of 45 best-scoring antigens, of which: 74% belong to the dormancy/reactivation/resuscitation classes; 30% belong to the Cell wall category; 13% are classical vaccine candidates; 9% are categorized Conserved hypotheticals, all potentially very potent T-cell antigens.</p> <p>Conclusion</p> <p>The comprehensive literature and <it>in silico</it>-based analyses allowed for the selection of a repertoire of 189 vaccine candidates, out of the whole-genome 3989 ORF products. This repertoire, which was ranked to generate a list of 45 top-hits antigens, is a platform for selection of genes covering all stages of <it>M. tuberculosis </it>infection, to be incorporated in rBCG or subunit-based vaccines.</p