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Abstract Background Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical. Methods Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach. Results We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail. Conclusions Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.

Abstract Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC); however, it can have adverse effects on patients’ long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information. The integration of MRI and a linear accelerator in the MR-Linac system allows for MR-guided radiation therapy (MRgRT), offering precise visualization and treatment delivery. This data descriptor offers a valuable repository for weekly intra-treatment diffusion-weighted imaging (DWI) data obtained from head and neck cancer patients. By analyzing the sequential DWI changes and their correlation with treatment response, as well as oncological and survival outcomes, the study provides valuable insights into the clinical implications of DWI in HNSCC.

Purpose: Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan. Methods and Materials: Data from 111 patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot. To validate our scoring method, 6 physicians were recruited to assess 60 plans, each using a scoring table consisting of a 5-point Likert scale (with scores ≥3 considered passing). Spearman correlation analysis was conducted to assess the association between increasing treatment plan percentile rank and physician rating, with Likert scores of 1 and 2 representing clinically unacceptable plans, scores of 3 and 4 representing plans needing minor edits, and a score of 5 representing clinically acceptable plans. Receiver operating characteristic curve analysis was used to assess the scoring system's ability to quantify plan quality. Results: Of the 60 plans scored by the physicians, 8 were deemed as clinically acceptable; these plans had an 89.0th ± 14.5 percentile value using our scoring system. The plans needing minor edits or deemed unacceptable had more variation, with scores falling in the 62.6nd ± 25.1 percentile and 35.6th ± 25.7 percentile, respectively. The estimated Spearman correlation coefficient between the physician score and treatment plan percentile was 0.53 (P < .001), indicating a moderate but statistically significant correlation. Receiver operating characteristic curve analysis demonstrated discernment between acceptable and unacceptable plan quality, with an area under the curve of 0.76. Conclusions: Our scoring system correlates with physician ratings while providing intuitive visual feedback for identifying good treatment plan quality, thereby indicating its utility in the quality assurance process.

Purpose: MR-guided radiotherapy (MRgRT) has the advantage of utilizing high soft tissue contrast imaging to track daily changes in target and critical organs throughout the entire radiation treatment course. Head and neck (HN) stereotactic body radiation therapy (SBRT) has been increasingly used to treat localized lesions within a shorter timeframe. The purpose of this study is to examine the dosimetric difference between the step-and-shot intensity modulated radiation therapy (IMRT) plans on Elekta Unity and our clinical volumetric modulated arc therapy (VMAT) plans on Varian TrueBeam for HN SBRT. Method: Fourteen patients treated on TrueBeam sTx with VMAT treatment plans were re-planned in the Monaco treatment planning system for Elekta Unity MR-Linac (MRL). The plan qualities, including target coverage, conformity, homogeneity, nearby critical organ doses, gradient index and low dose bath volume, were compared between VMAT and Monaco IMRT plans. Additionally, we evaluated the Unity adaptive plans of adapt-to-position (ATP) and adapt-to-shape (ATS) workflows using simulated setup errors for five patients and assessed the outcomes of our treated patients. Results: Monaco IMRT plans achieved comparable results to VMAT plans in terms of target coverage, uniformity and homogeneity, with slightly higher target maximum and mean doses. The critical organ doses in Monaco IMRT plans all met clinical goals; however, the mean doses and low dose bath volumes were higher than in VMAT plans. The adaptive plans demonstrated that the ATP workflow may result in degraded target coverage and OAR doses for HN SBRT, while the ATS workflow can maintain the plan quality. Conclusion: The use of Monaco treatment planning and online adaptation can achieve dosimetric results comparable to VMAT plans, with the additional benefits of real-time tracking of target volume and nearby critical structures. This offers the potential to treat aggressive and variable tumors in HN SBRT and improve local control and treatment toxicity.

Background and Purpose: Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck. Materials and Methods: CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate cohorts. For the lymph node levels, preference and clinical acceptability of AI vs human contours were scored. For the swallowing and chewing structures, clinical acceptability was scored. Quantitative analyses of the test sets were performed for AI vs human contours for all structures using overlap and distance metrics. Results: Median Dice Similarity Coefficient ranged from 0.77 to 0.89 for lymph node levels and 0.86 to 0.96 for chewing and swallowing structures. The AI contours were superior to or equally preferred to the manual contours at rates ranging from 75% to 91%; there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all AI-generated lymph node level contours, 92% were rated as usable with stylistic to no edits. Of the 340 contours in the chewing and swallowing cohort, 4% required minor edits. Conclusions: An accurate approach was developed to auto-contour lymph node levels and chewing and swallowing structures on CT images for patients with intact nodal anatomy. Only a small portion of test set auto-contours required minor edits.

While current MR-Linac (MRL) treatment workflows utilize a large table overlay during CT simulation to convert indexing between the two machines, we developed a look-up-table (LUT) as an alternative approach. After populating the LUT, index conversion factors were verified at three separate table locations. The resultant root-mean-square isocenter shifts on the MRL were 0.04/0.08 cm, 0.08/0.07 cm, and 0.09/0.08 cm with/without using the table overlay during simulation in the lateral, longitudinal, and vertical directions, respectively, which is within registration tolerance. Clinical implementation of the LUT has resulted in a more efficient MRL treatment workflow while maintaining accurate patient setup.

Abstract Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.

Background/objective: Pain is the most common acute symptom following radiation therapy (RT) for head and neck cancer (HNC). The multifactorial origin of RT-induced pain makes it highly challenging to manage. Multiple studies were conducted to identify genetic variants associated with cancer pain, however few of them focused on RT-induced acute pain. In this review, we summarize the potential mechanisms of acute pain after RT in HNC and identify genetic variants associated with RT-induced acute pain and relevant acute toxicities. Methods: A comprehensive search of Ovid Medline, EMBASE and Web of Science databases using terms including “Variants”, “Polymorphisms”, “Radiotherapy”, “Acute pain”, “Acute toxicity” published up to February 28, 2022, was performed by two reviewers. Review articles and citations were reviewed manually. The identified SNPs associated with RT-induced acute pain and toxicities were reported, and the molecular functions of the associated genes were described based on genetic annotation using The Human Gene Database; GeneCards. Results: A total of 386 articles were identified electronically and 8 more articles were included after manual search. 21 articles were finally included. 32 variants in 27 genes, of which 25% in inflammatory/immune response, 20% had function in DNA damage response and repair, 20% in cell death or cell cycle, were associated with RT-inflammatory pain and acute oral mucositis or dermatitis. 4 variants in 4 genes were associated with neuropathy and neuropathic pain. 5 variants in 4 genes were associated with RT-induced mixed types of post-RT-throat/neck pain. Conclusion: Different types of pain develop after RT in HNC, including inflammatory pain; neuropathic pain; nociceptive pain; and mixed oral pain. Genetic variants involved in DNA damage response and repair, cell death, inflammation and neuropathic pathways may affect pain presentation post-RT. These variants could be used for personalized pain management in HNC patients receiving RT.

Objectives: We aim to characterize the serial quantitative apparent diffusion coefficient (ADC) changes of the target disease volume using diffusion-weighted imaging (DWI) acquired weekly during radiation therapy (RT) on a 1.5 T MR-Linac and correlate these changes with tumor response and oncologic outcomes for head and neck squamous cell carcinoma (HNSCC) patients as part of a programmatic R-IDEAL biomarker characterization effort. Methods: Thirty patients with HNSCC who received curative-intent RT at MD Anderson Cancer Center, were included. Baseline and weekly MRI were obtained, and various ADC parameters were extracted from the regions of interest (ROIs). Baseline and weekly ADC parameters were correlated with response during and after RT, and the recurrence using the Mann-Whitney U test. The Wilcoxon signed-rank test was used to compare the weekly ADC versus baseline values. Weekly volumetric changes (Δvolume) for each ROI were correlated with ΔADC using Spearman’s Rho test. Recursive partitioning analysis (RPA) identified the optimal ΔADC threshold associated with different oncologic outcomes. Results: There was a significant rise in all ADC parameters at different time points of RT compared to baseline for both gross primary disease (GTV-P) and gross nodal disease volumes (GTV-N). The increased ADC values for GTV-P were statistically significant only for primary tumors achieving complete remission (CR) during RT. RPA identified GTV-P ΔADC 5th percentile > 13% at the mid-RT as the most significant parameter associated with primary tumors’ CR during RT (p

Abstract Background A primary goal in transoral robotic surgery (TORS) for oropharyngeal squamous cell cancer (OPSCC) survivors is to optimize swallowing function. However, the uncertainty in the outcomes of TORS including postoperative residual positive margin (PM) and extranodal extension (ENE), may necessitate adjuvant therapy, which may cause significant swallowing toxicity to survivors. Methods A secondary analysis was performed on a prospective registry data with low‐ to intermediate‐risk human papillomavirus–related OPSCC possibly resectable by TORS. Decision trees were developed to model the uncertainties in TORS compared with definitive radiation therapy (RT) and chemoradiation therapy (CRT). Swallowing toxicities were measured by Dynamic Imaging Grade of Swallowing Toxicity (DIGEST), MD Anderson Dysphagia Inventory (MDADI), and the MD Anderson Symptom Inventory–Head and Neck (MDASI‐HN) instruments. The likelihoods of PM/ENE were varied to determine the thresholds within which each therapy remains optimal. Results Compared with RT, TORS resulted in inferior swallowing function for moderate likelihoods of PM/ENE (>60% in short term for all instruments, >75% in long term for DIGEST and MDASI) leaving RT as the optimal treatment. Compared with CRT, TORS remained the optimal therapy based on MDADI and MDASI but showed inferior swallowing outcomes based on DIGEST for moderate‐to‐high likelihoods of PM/ENE (>75% for short‐term and >40% for long‐term outcomes). Conclusion In the absence of reliable estimation of postoperative PM/ENE concurrent with significant postoperative PM, the overall toxicity level in OPSCC patients undergoing TORS with adjuvant therapy may become more severe compared with patients receiving nonsurgical treatments thus advocating definitive (C)RT protocols.

Abstract Background This study investigated the association of hearing loss and tinnitus with overall health‐related quality of life (HRQoL) among long‐term oropharyngeal cancer (OPC) survivors. Methods This study included OPC survivors treated between 2000 and 2013 and surveyed from September 2015 to July 2016. Hearing loss and tinnitus were measured by asking survivors to rate their “difficulty with hearing loss and/or ringing in the ears” from 0 (not present) to 10 (as bad as you can imagine). Hearing loss and tinnitus scores were categorized as follows: 0 for none, 1–4 for mild, and 5–10 for moderate to severe. The primary outcome was the mean score of MD nderson Symptom Inventory Head & Neck module interference component as a HRQoL surrogate dichotomized as follows: 0 to 4 for none to mild and 5 to 10 for moderate to severe interference. Results Among 880 OPC survivors, 35.6% (314), reported none, 39.3% (347) reported mild, and 25.1% (221) reported moderate to severe hearing loss and tinnitus. On multivariable analysis, mild (OR, 5.83; 95% CI; 1.48–22.88; p = 0.012) and moderate (OR, 30.01; 95% CI; 7.96–113.10; p

Despite the significant evolution of radiation therapy (RT) techniques in recent years, many patients with head and neck cancer still experience significant toxicities during and after treatments. The increased soft tissue contrast and functional sequences of magnetic resonance imaging (MRI) are particularly attractive in head and neck cancer and have led to the increasing development of magnetic resonance-guided RT (MRgRT). This approach refers to the inclusion of the additional information acquired from a diagnostic or planning MRI in radiation treatment planning, and now extends to online high-quality daily imaging generated by the recently developed MR-Linac. MRgRT holds numerous potentials, including enhanced baseline and planning evaluations, anatomical and functional treatment adaptation, potential for hypofractionation, and multiparametric assessment of response. This article offers a structured review of the current literature on these established and upcoming roles of MRI for patients with head and neck cancer undergoing RT.

Abstract Background In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing the area under the receiver operating characteristic curve. For ordinal responses, the optimal predictor combination can similarly be obtained by maximization of the hypervolume under the manifold (HUM). Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases. Results We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem, which we refer to as Spherically Constrained Optimization Routine (SCOR). Through extensive simulation studies, we demonstrate that the proposed method achieves better performance than existing methods including the step-down algorithm. Finally, we illustrate the proposed method to predict the severity of swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck. Conclusions Our proposed method addresses an important challenge in combining multiple biomarkers to predict an ordinal outcome. This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various stages of progression or a toxicity with multiple grades of severity. We provide the implementation of our proposed SCOR method as an R package, available online at https://CRAN.R-project.org/package=SCOR .

Purpose: Segmentation of involved lymph nodes on head and neck computed tomography (HN-CT) scans is necessary for the radiotherapy planning of early-stage human papilloma virus (HPV) associated oropharynx cancers (OPC). We aimed to train a deep learning convolutional neural network (DL-CNN) to segment involved lymph nodes on HN-CT scans. Methods: Ground-truth segmentation of involved nodes was performed on pre-surgical HN-CT scans for 90 patients who underwent levels II-IV neck dissection for node-positive HPV-OPC (training/validation [n = 70] and testing [n = 20]). A 5-fold cross validation approach was used to train 5 DL-CNN sub-models based on a residual U-net architecture. Validation and testing segmentation masks were compared to ground-truth masks using predetermined metrics. A lymph auto-detection model to discriminate between “node-positive” and “node-negative” HN-CT scans was developed by thresholding segmentation model outputs and evaluated using the area under the receiver operating characteristic curve (AUC). Results: In the DL-CNN validation phase, all sub-models yielded segmentation masks with median Dice ≥ 0.90 and median volume similarity score of ≥ 0.95. In the testing phase, the DL-CNN produced consensus segmentation masks with median Dice of 0.92 (IQR, 0.89–0.95), median volume similarity of 0.97 (IQR, 0.94–0.99), and median Hausdorff distance of 4.52 mm (IQR, 1.22–8.38). The detection model achieved an AUC of 0.98. Conclusion: The results from this single-institution study demonstrate the successful automation of lymph node segmentation for patients with HPV-OPC using a DL-CNN. Future studies, including validation with an external dataset, are necessary to clarify its role in the larger radiation oncology treatment planning workflow.

PurposeIdentify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering.Material and methodsAll patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients’ treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient’s dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation.ResultsA total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p

Measurement(s) skeletal muscle • adipose tissue Technology Type(s) computed tomography

Background: The PREDICT-HN study aimed to systematically assess the kinetics of imaging MR biomarkers during head and neck radiotherapy. Methods: Patients with intact squamous cell carcinoma of the head and neck were enrolled. Pre-, during, and post-treatment MRI were obtained. Serial GTV and ADC measurements were recorded. The correlation between each feature and the GTV was calculated using Spearman’s correlation coefficient. The linear mixed model was used to evaluate the change in GTV over time. Results: A total of 41 patients completed the study. The majority (76%) had oropharyngeal cancer. A total of 36 patients had intact primary tumours that can be assessed on MRI, and 31 patients had nodal disease with 46 nodes assessed. Median primary GTV (GTVp) size was 14.1cc. The rate of GTVp shrinkage was highest between pre-treatment and week 4. Patients with T3-T4 tumours had a 3.8-fold decrease in GTVp compared to T1-T2 tumours. The ADC values correlated with residual GTVp. The median nodal volume (GTVn) was 12.4cc. No clinical features were found to correlate with GTVn reduction. The overall change in ADC for GTVn from pre-treatment was significant for 35th–95th percentiles in weeks 1–4 (p < 0.001). Conclusion: A discrepancy in the trajectory of ADC between primary and nodal sites suggested that they exhibit different treatment responses and should be analysed separately in future studies.

Abstract Genetic susceptibility for xerostomia, a common sequela of radiotherapy and chemoradiotherapy for head and neck cancer, is unknown. Therefore, to identify genetic variants associated with moderate to severe xerostomia, we conducted a GWAS of 359 long-term oropharyngeal cancer (OPC) survivors using 579,956 autosomal SNPs. Patient-reported cancer treatment-related xerostomia was assessed using the MD Anderson Symptom Inventory. Patient response was dichotomized as moderate to severe or none to mild symptoms. In our study, 39.2% of OPC survivors reported moderate to severe xerostomia. Our GWAS identified eight SNPs suggestively associated with higher risk of moderate to severe xerostomia in six genomic regions (2p13.3, rs6546481, Minor Allele (MA) = A, ANTXR1, P = 4.3 × 10–7; 5p13.2–p13.1, rs16903936, MA = G, EGFLAM, P = 5.1 × 10–6; 4q21.1, rs10518156, MA = G, SHROOM3, P = 7.1 × 10–6; 19q13.42, rs11882068, MA = G, NLRP9, P = 1.7 × 10–5; 12q24.33, rs4760542, MA = G, GLT1D1, P = 1.8 × 10–5; and 3q27.3, rs11714564, MA = G, RTP1, P = 2.9 × 10–5. Seven SNPs were associated with lower risk of moderate to severe xerostomia, of which only one mapped to specific genomic region (15q21.3, rs4776140, MA = G, LOC105370826, a ncRNA class RNA gene, P = 1.5 × 10–5). Although our small exploratory study did not reach genome-wide statistical significance, our study provides, for the first time, preliminary evidence of genetic susceptibility to xerostomia. Further studies are needed to elucidate the role of genetic susceptibility to xerostomia.

Introduction: Radiation dose-escalation for head and neck cancer (HNC) patients aiming to improve cure rates is challenging due to the increased risk of unacceptable treatment-induced toxicities. With “Proton Image-guided Radiation Assignment for Therapeutic Escalation via Selection of locally advanced head and neck cancer patients” (PIRATES), we present a novel treatment approach that is designed to facilitate dose-escalation while minimizing the risk of dose-limiting toxicities for locally advanced HPV-negative HNC patients. The aim of this Phase I trial is to assess the safety & feasibility of PIRATES approach. Methods: The PIRATES protocol employs a multi-faceted dose-escalation approach to minimize the risk of dose-limiting toxicities (DLTs): 1) sparing surrounding normal tissue from extraneous dose with intensity-modulated proton therapy, 2) mid-treatment hybrid hyper-fractionation for radiobiologic normal tissue sparing; 3) Magnetic Resonance Imaging (MRI) guided mid-treatment boost volume adaptation, and 4) iso-effective restricted organ-at-risk dosing to mucosa and bone tissues.The time-to-event Bayesian optimal interval (TITE-BOIN) design is employed to address the challenge of the long DLT window of 6 months and find the maximum tolerated dose. The primary endpoint is unacceptable radiation-induced toxicities (Grade 4, mucositis, dermatitis, or Grade 3 myelopathy, osteoradionecrosis) occurring within 6 months following radiotherapy. The second endpoint is any grade 3 toxicity occurring in 3–6 months after radiation. Discussion: The PIRATES dose-escalation approach is designed to provide a safe avenue to intensify local treatment for HNC patients for whom therapy with conventional radiation dose levels is likely to fail. PIRATES aims to minimize the radiation damage to the tissue surrounding the tumor volume with the combination of proton therapy and adaptive radiotherapy and within the high dose tumor volume with hybrid hyper-fractionation and not boosting mucosal and bone tissues. Ultimately, if successful, PIRATES has the potential to safety increase local control rates in HNC patients with high loco-regional failure risk.Trial registration: ClinicalTrials.gov ID: NCT04870840; Registration date: May 4, 2021.Netherlands Trial Register ID: NL9603; Registration date: July 15, 2021.

Background/Purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance. Materials/Methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, T2 + ADC, T2 + Ktrans, T2 + Ve, all five channels [ALL]) primarily using the Dice similarity coefficient (DSC). False-negative DSC (FND), false-positive DSC, sensitivity, positive predictive value, surface DSC, Hausdorff distance (HD), 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a blinded Turing test using three physician observers. Results: Models yielded mean DSCs from 0.71 ± 0.12 (ALL) to 0.73 ± 0.12 (T2 + T1). Compared to the T2 model, performance was significantly improved for FND, sensitivity, surface DSC, HD, and 95% HD for the T2 + T1 model (p 0.05). Most models demonstrated significant correlations between tumor size and HD or Surface DSC (p 0.05). On average, there were no significant differences between ground truth and DL-generated segmentations for all observers (p > 0.05). Conclusion: DL using mpMRI provides reasonably accurate segmentations of OPC GTVp that may be comparable to ground truth segmentations generated by clinical experts. Incorporating additional mpMRI channels may increase the performance of FND, sensitivity, surface DSC, HD, and 95% HD, and improve model robustness to tumor size.

BackgroundDemand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs).MethodsA publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC).ResultsMost defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively.ConclusionDefacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies—which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation—can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.

Background and Purpose: Conventional magnetic resonance imaging (MRI) poses challenges in quantitative analysis because voxel intensity values lack physical meaning. While intensity standardization methods exist, their effects on head and neck MRI have not been investigated. We developed a workflow based on healthy tissue region of interest (ROI) analysis to determine intensity consistency within a patient cohort. Through this workflow, we systematically evaluated intensity standardization methods for MRI of head and neck cancer (HNC) patients. Materials and Methods: Two HNC cohorts (30 patients total) were retrospectively analyzed. One cohort was imaged with heterogenous acquisition parameters (HET cohort), whereas the other was imaged with homogenous acquisition parameters (HOM cohort). The standard deviation of cohort-level normalized mean intensity (SD NMIc), a metric of intensity consistency, was calculated across ROIs to determine the effect of five intensity standardization methods on T2-weighted images. For each cohort, a Friedman test followed by a post-hoc Bonferroni-corrected Wilcoxon signed-rank test was conducted to compare SD NMIc among methods. Results: Consistency (SD NMIc across ROIs) between unstandardized images was substantially more impaired in the HET cohort (0.29 ± 0.08) than in the HOM cohort (0.15 ± 0.03). Consequently, corrected p-values for intensity standardization methods with lower SD NMIc compared to unstandardized images were significant in the HET cohort (p 0.05). In both cohorts, differences between methods were often minimal and nonsignificant. Conclusions: Our findings stress the importance of intensity standardization, either through the utilization of uniform acquisition parameters or specific intensity standardization methods, and the need for testing intensity consistency before performing quantitative analysis of HNC MRI.

Abstract Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.

BackgroundQuick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.MethodsWe used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and 6mMRI as output. Eighteen image sets were used to test model performance. Similarity metrics, including the mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were calculated between normalized synthetic 6mMRI and ground-truth 6mMRI for all test cases. In addition, a previously trained OAR DL auto-segmentation model was used to segment the right parotid gland, left parotid gland, and mandible on all test case images. Dice similarity coefficients (DSC) were calculated between 2mMRI and either ground-truth 6mMRI or synthetic 6mMRI for each OAR; two one-sided t-tests were applied between the ground-truth and synthetic 6mMRI to determine equivalence. Finally, a visual Turing test using paired ground-truth and synthetic 6mMRI was performed using three clinician observers; the percentage of images that were correctly identified was compared to random chance using proportion equivalence tests.ResultsThe median similarity metrics across the whole images were 0.19, 0.93, and 33.14 for MSE, SSIM, and PSNR, respectively. The median of DSCs comparing ground-truth vs. synthetic 6mMRI auto-segmented OARs were 0.86 vs. 0.85, 0.84 vs. 0.84, and 0.82 vs. 0.85 for the right parotid gland, left parotid gland, and mandible, respectively (equivalence p

Purpose: Head and neck cancers radiotherapy (RT) is associated with inevitable injury to parotid glands and subsequent xerostomia. We investigated the utility of SUV derived from 18FDG-PET to develop metabolic imaging biomarkers (MIBs) of RT-related parotid injury. Methods: Data for oropharyngeal cancer (OPC) patients treated with RT at our institution between 2005 and 2015 with available planning computed tomography (CT), dose grid, pre- & first post-RT 18FDG-PET-CT scans, and physician-reported xerostomia assessment at 3–6 months post-RT (Xero 3–6 ms) per CTCAE, was retrieved, following an IRB approval. A CT-CT deformable image co-registration followed by voxel-by-voxel resampling of pre & post-RT 18FDG activity and dose grid were performed. Ipsilateral (Ipsi) and contralateral (contra) parotid glands were sub-segmented based on the received dose in 5 Gy increments, i.e. 0–5 Gy, 5–10 Gy sub-volumes, etc. Median and dose-weighted SUV were extracted from whole parotid volumes and sub-volumes on pre- & post-RT PET scans, using in-house code that runs on MATLAB. Wilcoxon signed-rank and Kruskal-Wallis tests were used to test differences pre- and post-RT. Results: 432 parotid glands, belonging to 108 OPC patients treated with RT, were sub-segmented & analyzed. Xero 3–6 ms was reported as: non-severe (78.7%) and severe (21.3%). SUV- median values were significantly reduced post-RT, irrespective of laterality (p = 0.02). A similar pattern was observed in parotid sub-volumes, especially ipsi parotid gland sub-volumes receiving doses 10–50 Gy (p

Abstract To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan–Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val

Purpose: To report patient-reported outcomes (PROs) derived from the Functional Assessment of Cancer Therapy-Head and Neck (FACT-HN) tool, in patients with oropharynx cancer (OPC) treated with intensity-modulated proton therapy (IMPT) in the context of first-course irradiation. Materials and Methods: Patients with locally advanced OPC treated with radical IMPT between 2011 and 2018 were included in a prospective registry. FACT-HN scores were measured serially during and 24 months following IMPT. PRO changes in the FACT-HN scores over time were assessed with mixed-model analysis. Results: Fifty-seven patients met inclusion criteria. Median age was 60 years (range, 41-84), and 91% had human papillomavirus-associated disease. In total, 28% received induction chemotherapy and 68% had concurrent chemotherapy. Compliance to FACT-HN questionnaire completion was 59%, 48%, and 42% at 6, 12, and 24 months after treatment, respectively. The mean FACT-General (G), FACT-Total, and FACT-Trial Outcome Index (TOI) score changes were statistically and clinically significant relative to baseline from week 3 of treatment up to week 2 after treatment. Nadir was reached at week 6 of treatment for all scores, with maximum scores dropping by 15%, 20%, and 39% compared to baseline for FACT-G, FACT-Total, and FACT-TOI, respectively. Subdomain scores of physical well-being, functional well-being, and head and neck additional concerns decreased from baseline during treatment and returned to baseline at week 4 after treatment. Conclusions: IMPT was associated with a favorable PRO trajectory, characterized by an acute decline followed by rapid recovery to baseline. This study establishes the expected acute, subacute, and chronic trajectory of PROs for patients undergoing IMPT for OPC.

Purpose: We compared work outcomes in patients with oropharyngeal cancer (OPC), randomized to intensity-modulated proton (IMPT) versus intensity-modulated photon therapy (IMRT) for chemoradiation therapy (CRT). Patients and Methods: In 147 patients with stage II-IVB squamous cell OPC participating in patient-reported outcomes assessments, a prespecified secondary aim of a randomized phase II/III trial of IMPT (n = 69) versus IMRT (n = 78), we compared absenteeism, presenteeism (i.e., the extent to which an employee is not fully functional at work), and work productivity losses. We used the work productivity and activity impairment questionnaire at baseline (pre-CRT), at the end of CRT, and at 6 months, 1 year, and 2 years. A one-sided Cochran-Armitage test was used to analyze within-arm temporal trends, and a χ2 test was used to compare between-arm differences. Among working patients, at each follow-up point, a 1-sided Wilcoxon rank-sum test was used to compare work-productivity scores. Results: Patient characteristics in IMPT versus IMRT arms were similar. In the IMPT arm, within-arm analysis demonstrated that an increasing proportion of patients resumed working after IMPT, from 60% (40 of 67) pre-CRT and 71% (30 of 42) at 1 year to 78% (18 of 23) at 2 years (P = 0.025). In the IMRT arm, the proportion remained stable, with 57% (43 of 76) pre-CRT, 54% (21 of 39) at 1 year, and 52% (13 of 25) working at 2 years (P = 0.47). By 2 years after CRT, the between-arm difference between patients who had IMPT and those who had IMRT trended toward significance (P = 0.06). Regardless of treatment arm, among working patients, the most severe work impairments occurred from treatment initiation to the end of CRT, with significant recovery from absenteeism, presenteeism, and productivity impairments by the 2-year follow-up (P < 0.001 for all). Higher magnitudes of recovery from absenteeism (at 1 year, P = 0.05; and at 2 years, P = 0.04) and composite work impairment scores (at 1 year, P = 0.04; and at 2 years, P = 0.04) were seen in patients treated with IMPT versus those treated with IMRT. Conclusion: In patients with OPC receiving curative CRT, patients randomized to IMPT demonstrated increasing work and productivity recovery trends. Studies are needed to identify mechanisms underlying head and neck CRT treatment causing work disability and impairment.

Background/PurposeSarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.Materials/Methods390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the section was auto-selected, and a 2D ResUNet auto-segmented the auto-selected slice. Both the 3D and 2D approaches trained five sub-models (5-fold cross-validation) and combined sub-model predictions on the test set using majority vote ensembling. Model performance was primarily determined using the Dice similarity coefficient (DSC). Predicted SMI was calculated using the auto-segmented SM cross-sectional area. Finally, using established SMI cutoffs, we performed a Kaplan-Meier analysis to determine associations with overall survival.ResultsMean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. Predicted SMI had high correlation to the ground-truth SMI in males and females (r>0.96). Predicted SMI stratified patients for overall survival in males (log-rank p = 0.01) but not females (log-rank p = 0.07), consistent with ground-truth SMI.ConclusionWe developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making in patients with HNC.

Abstract Historically USPIO has been used to help with nodal staging but not in primary tumors. The ability to concentrate USPIO may help to differentiate BMT from other types of parotid tumors.

Purpose: To describe the baseline and serial tumor microbiome in HPV-associated oropharynx cancer (OPC) over the course of radiotherapy (RT). Methods: Patients with newly diagnosed HPV-associated OPC treated with definitive radiotherapy +/− concurrent chemotherapy were enrolled in this prospective study. Using 16S rRNA gene sequencing, dynamic changes in the tumor site microbiome during RT were investigated. Surface tumor samples were obtained before RT and at week 1, 3 and 5 of RT. Radiological primary tumor response at mid-treatment was categorized as complete (CR) or partial (PR). Results: Ten patients were enrolled, but 9 patients were included in the final analysis. Mean age was 62 years (range: 51–71). As per AJCC 8th Ed, 56%, 22% and 22% of patients had stage I, II and III, respectively. At 4-weeks, 6 patients had CR and 3 patients had PR; at follow-up imaging post treatment, all patients had CR. The baseline diversity of the tumoral versus buccal microbiome was not statistically different. For the entire cohort, alpha diversity was significantly decreased over the course of treatment (p = 0.04). There was a significant alteration in the bacterial community within the first week of radiation. Baseline tumor alpha diversity of patients with CR was significantly higher than those with PR (p = 0.03). While patients with CR had significant reduction in diversity over the course of radiation (p = 0.01), the diversity remained unchanged in patients with PR. Patients with history of smoking had significantly increased abundance of Kingella (0.05) and lower abundance of Stomatobaculum (p = 0.03) compared to never smokers. Conclusions: The tumor microbiome of HPV-associated OPC exhibits reduced alpha diversity and altered taxa abundance over the course of radiotherapy. The baseline bacterial profiles of smokers vs. non-smokers were inherently different. Baseline tumor alpha diversity of patients with CR was higher than patients with PR, suggesting that the microbiome deserves further investigation as a biomarker of radiation response.

Purpose: In this investigation, we aimed to describe trends in time to acceptance (TTA) and time to online publication (TTOP) of research published in leading radiation oncology journals from 2010 to 2019. We further sought to identify journal characteristics that might influence TTA and TTOP. Methods and Materials: We searched the publication history of 5 leading international radiation oncology journals. For all research articles accepted from January 1, 2010, to December 31, 2019, we tabulated the date of article receipt, the date of acceptance, and the date of online publication when available. The TTA was calculated as the number of elapsed days from article receipt to acceptance, and the TTOP was calculated as the number of elapsed days from article acceptance to online publication. Using the Mann-Kendall test, we assessed for monotonic trends over time and used the post hoc Theil-Sen method to estimate rates of change. We created a multiple regression model to identify journal characteristics associated with TTA and TTOP. Results: In total, 10,132 articles were included. Both the TTA and the TTOP decreased significantly from 2010 to 2019 (P = .005 and P < .001, respectively), with an estimated decrease of 1.5 days per year for the TTA and 7.0 days per year for the TTOP. Multiple regression modeling revealed that a higher journal impact factor was independently associated with an increased TTA (P < .001) and a decreased TTOP (P < .001). A higher number of accepted journal articles per year was associated with a decreased TTA (P < .001) and an increased TTOP (P < .001). Conclusions: Radiation oncology research has been accepted and published online at increasingly faster rates during the past decade. The TTA may be longer in higher-impact, more selective journals, possibly suggesting a need for comprehensive peer review and complex editorial decisions. However, these articles are also published online faster after article acceptance. Future work examining patterns of acceptance and publication speed is needed to encourage rapid dissemination of practice-guiding data.

Abstract Background The incidence of oropharyngeal squamous cell carcinoma (OPSCC) in the US is rapidly increasing, driven largely by the epidemic of human papillomavirus (HPV)-mediated OPSCC. Although survival for patients with HPV mediated OPSCC (HPV+ OPSCC) is generally better than that of patients with non-virally mediated OPSCC, this effect is not uniform. We hypothesized that tobacco exposure remains a critical modifier of survival for HPV+ OPSCC patients. Methods We conducted a retrospective analysis of 611 OPSCC patients with concordant p16 and HPV testing treated at a single institute (2002–2013). Survival analysis was performed using Kaplan-Meier analysis and Cox regression. Recursive partitioning analysis (RPA) was used to define tobacco exposure associated with survival (p 30 PY patients didn’t differ significantly from HPV- patients (p = 0.72, p = 0.27, respectively). HPV+ > 30 PY patients had substantially lower 5-year OS when compared to their ≤30 PYs counterparts: 78.4% vs 91.6%; p = 0.03, 76% vs 88.3%; p = 0.07, and 52.3% vs 74%; p = 0.05, for stages I, II, and III (AJCC 8th Edition Manual), respectively. Conclusions Tobacco exposure can eliminate the survival benefit associated with HPV+ status in OPSCC patients. Until this effect can be clearly quantified using prospective datasets, de-escalation of treatment for HPV + OPSCC smokers should be avoided.

In treatment planning, multiple imaging modalities can be employed to improve the accuracy of tumor delineation but this can be costly. This study aimed to compare the interobserver consistency of using dual energy computed tomography (DECT) versus magnetic resonance imaging (MRI) for delineating tumors in the head and neck cancer (HNC) re-irradiation scenario. Twenty-three patients with recurrent HNC and had planning DECT and MRI were identified. Contoured tumor volumes by seven radiation oncologists were compared. Overall, T1c MRI performed the best with median DSC of 0.58 (0–0.91) for T1c. T1c MRI provided higher interobserver agreement for skull base sites and 60 kV DECT provided higher interobserver agreement for non-skull base sites.

BackgroundSarcopenia is prognostic for survival in patients with head and neck cancer (HNC). However, identification of this high-risk feature remains challenging without computed tomography (CT) imaging of the abdomen or thorax. Herein, we establish sarcopenia thresholds at the C3 level and determine if C3 sarcopenia is associated with survival in patients with HNC.MethodsThis retrospective cohort study was conducted in consecutive patients with a squamous cell carcinoma of the head and neck with cross-sectional abdominal or neck imaging within 60 days prior to treatment. Measurement of cross-sectional muscle area at L3 and C3 levels was performed from CT imaging. Primary study outcome was overall survival.ResultsSkeletal muscle area at C3 was strongly correlated with the L3 level in both men (n = 188; r = 0.77; p < 0.001) and women (n = 65; r = 0.80; p < 0.001), and C3 sarcopenia thresholds of 14.0 cm2/m2 (men) and 11.1 cm2/m2 (women) were best predictive of L3 sarcopenia thresholds. Applying these C3 thresholds to a cohort of patients with neck imaging alone revealed that C3 sarcopenia was independently associated with reduced overall survival in men (HR = 2.63; 95% CI, 1.79, 3.85) but not women (HR = 1.18, 95% CI, 0.76, 1.85).ConclusionsThis study identifies sarcopenia thresholds at the C3 level that best predict L3 sarcopenia in men and women. In HNC, C3-defined sarcopenia is associated with poor survival outcomes in men, but not women, suggesting sarcopenia may differentially affect men and women with HNC.

Background: MRI-guided radiotherapy planning (MRIgRT) may be superior to CT-guided planning in some instances owing to its improved soft tissue contrast. However, MR images do not communicate tissue electron density information necessary for dose calculation and therefore must either be co-registered to CT or algorithmically converted to synthetic CT. No robust quality assessment of commercially available MR-CT registration algorithms is yet available; thus we sought to quantify MR-CT registration formally. Methods: Head and neck non-contrast CT and T2 MRI scans acquired with standard treatment immobilization techniques were prospectively acquired from 15 patients. Per scan, 35 anatomic regions of interest (ROIs) were manually segmented. MRIs were registered to CT rigidly (RIR) and by three commercially available deformable registration algorithms (DIR). Dice similarity coefficient (DSC), Hausdorff distance mean (HD mean) and Hausdorff distance max (HD max) metrics were calculated to assess concordance between MRI and CT segmentations. Each DIR algorithm was compared to DIR using the nonparametric Steel test with control for individual ROIs (n = 105 tests) and for all ROIs in aggregate (n = 3 tests). The influence of tissue type on registration fidelity was assessed using nonparametric Wilcoxon pairwise tests between ROIs grouped by tissue type (n = 12 tests). Bonferroni corrections were applied for multiple comparisons. Results: No DIR algorithm improved the segmentation quality over RIR for any ROI nor all ROIs in aggregate (all p values >0.05). Muscle and gland ROIs were significantly more concordant than vessel and bone, but DIR remained non-different from RIR. Conclusions: For MR-CT co-registration, our results question the utility and applicability of commercially available DIR over RIR alone. The poor overall performance also questions the feasibility of translating tissue electron density information to MRI by CT registration, rather than addressing this need with synthetic CT generation or bulk-density assignment. Keywords: MRI-guided radiotherapy, CT-MRI image registration, Deformable image registration, Rigid image registration, Quality assessment

Abstract Background To evaluate and establish a digital workflow for the custom designing and 3D printing of mouth opening tongue-depressing (MOTD) stents for patients receiving radiotherapy for head and neck cancer. Methods We retrospectively identified 3 patients who received radiation therapy (RT) for primary head and neck cancers with MOTD stents. We compared two methods for obtaining the digital impressions of patients’ teeth. The first method involved segmentation from computed tomography (CT) scans, as previously established by our group, and the second method used 3D scanning of the patients’ articulated stone models that were made during the conventional stent fabrication process. Three independent observers repeated the process to obtain digital impressions which provided data to design customized MOTD stents. For each method, we evaluated the time efficiency, dice similarity coefficient (DSC) for reproducibility, and the 3D printed stents’ accuracy. For the 3D scanning method, we evaluated the registration process using manual and automatic approaches. Results For all patients, the 3D scanning method demonstrated a significant advantage over the CT scanning method in terms of time efficiency with over 60% reduction in time consumed (p

Background and purpose: Minimizing radiation dose exposure to nearby organs is key to limiting clinical toxicities associated with radiotherapy. Several treatment modalities such as split- or whole-field intensity-modulated radiotherapy (SF-IMRT, WF-IMRT) and volumetric modulated arc therapy (VMAT) are being used to treat tonsillar cancer patients with unilateral neck radiotherapy. Herein, we provide a modern dosimetric comparison of all three techniques. Materials and methods: Forty patients with tonsillar cancer treated with definitive, ipsilateral neck SF-IMRT were evaluated. Each patient was re-planned with WF-IMRT and VMAT techniques, and doses to selected organs-at-risk (OARs) including the larynx, esophagus, and brainstem were compared. Results: No significant differences in target coverage existed between plans; however, the heterogeneity index improved using WF-IMRT and VMAT relative to SF-IMRT. Compared to SF-IMRT, WF-IMRT and VMAT plans had significantly lower mean doses to the supraglottic larynx (31 Gy, 18.5 Gy, 17 Gy; p

Introduction: Target delineation variability is a significant technical impediment in multi-institutional trials which employ intensity modulated radiotherapy (IMRT), as there is a real potential for clinically meaningful variances that can impact the outcomes in clinical trials. The goal of this study is to determine the variability of target delineation among participants from different institutions as part of Southwest Oncology Group (SWOG) Radiotherapy Committee’s multi-institutional in-silico quality assurance study in patients with Pancoast tumors as a “dry run” for trial implementation. Methods: CT simulation scans were acquired from four patients with Pancoast tumor. Two patients had simulation 4D-CT and FDG-FDG PET-CT while two patients had 3D-CT and FDG-FDG PET-CT. Seventeen SWOG-affiliated physicians independently delineated target volumes defined as gross primary and nodal tumor volumes (GTV_P & GTV_N), clinical target volume (CTV), and planning target volume (PTV).Six board-certified thoracic radiation oncologists were designated as the ‘Experts’ for this study. Their delineations were used to create a simultaneous truth and performance level estimation (STAPLE) contours using ADMIRE software (Elekta AB, Sweden 2017). Individual participants’ contours were then compared with Experts’ STAPLE contours. Results: When compared to the Experts’ STAPLE, GTV_P had the best agreement among all participants, while GTV_N showed the lowest agreement among all participants. There were no statistically significant differences in all studied parameters for all TVs for cases with 4D-CT versus cases with 3D-CT simulation scans. Conclusions: High degree of inter-observer variation was noted for all target volume except for GTV_P, unveiling potentials for protocol modification for subsequent clinically meaningful improvement in target definition. Various similarity indices exist that can be used to guide multi-institutional radiotherapy delineation QA credentialing. Keywords: Target volumes, Contouring, Quality assurance, QA, Pancoast tumor, Thoracic

Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61–0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.

Purpose: MR-guided Radiation Therapy (MRgRT) allows for high-precision radiotherapy under real-time MR visualization. This enables margin reduction and subsequent dose escalation which may lead to higher tumor control and less toxicity. The Unity MR-linac (Elekta AB, Stockholm, Sweden) integrates a linear accelerator with a 1.5T diagnostic quality MRI and an online adaptive workflow. A prospective international registry was established to facilitate the evidence-based implementation of the Unity MR-linac into clinical practice, to systemically evaluate long-term outcomes, and to aid further technical development of MR-linac-based MRgRT.Methods and Results: In February 2019, the Multi-OutcoMe EvaluatioN of radiation Therapy Using the MR-linac study (MOMENTUM) started within the MR-linac Consortium. The MOMENTUM study is an international academic-industrial partnership between several hospitals and industry partner Elekta. All patients treated on the MR-linac are eligible for inclusion in MOMENTUM. For participants, we collect clinical patient data (e.g., patient, tumor, and treatment characteristics) and technical patient data which is defined as information generated on the MR-linac during treatment. The data are captured, pseudonymized, and stored in an international registry at set time intervals up to two years after treatment. Patients can choose to provide patient-reported outcomes and consent to additional MRI scans acquired on the MR-linac. This registry will serve as a data platform that supports multicenter research investigating the MR-linac. Rules and regulations on data sharing, data access, and intellectual property rights are summarized in an academic-industrial collaboration agreement. Data access rules ensure secure data handling and research integrity for investigators and institutions. Separate data access rules exist for academic and industry partners. This study is registered at ClinicalTrials.gov with ID: NCT04075305 (https://clinicaltrials.gov/ct2/show/NCT04075305).Conclusion: The multi-institutional MOMENTUM study has been set up to collect clinical and technical patient data to advance technical development, and facilitate evidenced-based implementation of MR-linac technology with the ultimate purpose to improve tumor control, survival, and quality of life of patients with cancer.

Introduction: The highest concentration of military personnel in the United States is located in Hawaii where occupational exposures, such as to asbestos in the Pacific Fleet shipyards, predispose them to thoracic malignancies. For this reason, Veterans Affairs (VA) insurance outcomes for lung cancer in Hawaii are of interest. Methods: All cases of lung cancer in the Hawaii Tumor Registry from 2000 to 2015 were evaluated. The selection criterion included evidence of extensive-stage SCLC (ES-SCLC) or metastatic NSCLC. Overall survival was compared using the Kaplan-Meier log-rank method. Univariate analysis and multivariable analysis (MVA) were carried out to understand the variables associated with overall survival. Results: There were 434 cases of ES-SCLC and 2139 cases of metastatic NSCLC identified. VA insurance (median survival [MS], 2 mo), Medicaid (MS, 4 mo), and Medicare (MS, 4 mo) had worse survival (log-rank p < 0.001) than private insurance (MS, 8 mo). In ES-SCLC, VA insurance (hazard ratio [HR], 2.74; 95% confidence interval [CI]: 1.50–5.01; p = 0.001) and Medicaid (HR, 1.46; 95% CI: 1.04–2.03; p = 0.027) had significantly worse survival compared with private insurance on MVA. VA insurance (HR, 1.84; 95% CI: 1.34–2.53; p < 0.001) and Medicaid (HR, 1.40; 95% CI: 1.20–1.63; p < 0.001) also had worse survival compared with private insurance in metastatic NSCLC on MVA. Conclusions: VA insurance coverage was associated with dismal survival for metastatic lung cancer that was effectively similar to hospice or supportive care, compelling further investigation to identify reasons for this disparity.

Background: Current standard radiotherapy for oropharynx cancer (OPC) is associated with high rates of severe toxicities, shown to adversely impact patients’ quality of life. Given excellent outcomes of human papilloma virus (HPV)-associated OPC and long-term survival of these typically young patients, treatment de-intensification aimed at improving survivorship while maintaining excellent disease control is now a central concern. The recent implementation of magnetic resonance image – guided radiotherapy (MRgRT) systems allows for individual tumor response assessment during treatment and offers possibility of personalized dose-reduction. In this 2-stage Bayesian phase II study, we propose to examine weekly radiotherapy dose-adaptation based on magnetic resonance imaging (MRI) evaluated tumor response. Individual patient’s plan will be designed to optimize dose reduction to organs at risk and minimize locoregional failure probability based on serial MRI during RT. Our primary aim is to assess the non-inferiority of MRgRT dose adaptation for patients with low risk HPV-associated OPC compared to historical control, as measured by Bayesian posterior probability of locoregional control (LRC). Methods: Patients with T1-2 N0-2b (as per AJCC 7th Edition) HPV-positive OPC, with lymph node

Abstract Background Magnetic resonance imaging (MRI) has improved capacity to visualize tumor and soft tissue involvement in head and neck cancers. Using advanced MRI, we can interrogate cell density using diffusion weighted imaging, a quantitative imaging that can be used during radiotherapy, when diffuse inflammatory reaction precludes PET imaging, and can assist with target delineation as well. Correlation of circulating tumor cells (CTCs) measurements with 3D quantitative tumor characterization could potentially allow selective, patient-specific response-adapted escalation or de-escalation of local therapy, and improve the therapeutic ratio, curing the greatest number of patients with the least toxicity. Methods The proposed study is designed as a prospective observational study and will collect pretreatment CT, MRI and PET/CT images, weekly serial MR imaging during RT and post treatment CT, MRI and PET/CT images. In addition, blood sample will be collected for biomarker analysis at those time intervals. CTC assessments will be performed on the CellSave tube using the FDA-approved CellSearch® Circulating Tumor Cell Kit (Janssen Diagnostics), and plasma from the EDTA blood samples will be collected, labeled with a de-identifying number, and stored at − 80 °C for future analyses. Discussion The primary objective of the study is to evaluate the prognostic value and correlation of weekly tumor response kinetics (gross tumor volume and MR signal changes) and circulating tumor cells of mucosal head and neck cancers during radiation therapy using MRI in predicting treatment response and clinical outcomes. This study will provide landmark information as to the utility of CTCs (‘liquid biopsy) and tumor-specific functional quantitative imaging changes during treatment to guide personalization of treatment for future patients. Combining the biological information from CTCs and the structural information from MRI may provide more information than either modality alone. In addition, this study could potentially allow us to determine the optimal time to obtain MR imaging and/ or CTCs during radiotherapy to assess tumor response and provide guidance for patient selection and stratification for future dose escalation or de-escalation strategies. Trial registration Clinicaltrials.gov (NCT03491176). Date of registration: 9th April 2018. (retrospectively registered). Date of enrolment of the first participant: 30th May 2017.

Purpose: We aim to determine the feasibility and dosimetric benefits of a novel MRI-guided IMRT dose-adaption strategy for human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPC). Materials/methods: Patients with locally advanced HPV+ OPC underwent pre-treatment and in-treatment MRIs every two weeks using RT immobilization setup. For each patient, two IMRT plans were created (i.e. standard and adaptive). The prescription dose for the standard plans was 2.12 Gy/fx for 33 fractions to the initial PTV. For adaptive plans, a new PTVadaptive was generated based on serial MRIs in case of detectable tumor shrinkage. Prescription dose to PTVadaptive was 2.12 Gy/fx to allow for maximum dose to the residual disease. Any previously involved volumes received minimally a floor dose of 50.16 Gy. Uninvolved elective nodal volumes were prescribed 50.16 Gy in 1.52 Gy/fx. Dosimetric parameters of organs at risk (OARs) were recorded for standard vs. adaptive plans. Normal tissue complication probability (NTCP) for toxicity endpoints was calculated using literature-derived multivariate logistic regression models. Results: Five patients were included in this pilot study, 3 men and 2 women. Median age was 58 years (range 45–69). Three tumors originated at the tonsillar fossa and two at the base of tongue. The average dose to 95% of initial PTV volume was 70.7 Gy (SD,0.3) for standard plans vs. 58.5 Gy (SD,2.0) for adaptive plans. The majority of OARs showed decrease in dosimetric parameters using adaptive plans vs. standard plans, particularly swallowing related structures. The average reduction in the probability of developing dysphagia ≥ grade2, feeding tube persistence at 6-month post-treatment and hypothyroidism at 1-year post-treatment was 11%, 4%, and 5%, respectively. The probability of xerostomia at 6-month was only reduced by 1% for adaptive plans vs. standard IMRT. Conclusion: These in silico results showed that the proposed MRI-guided adaptive approach is technically feasible and advantageous in reducing dose to OARs, especially swallowing musculature. Keywords: MRI-guided radiotherapy, Dose adaptation, IMRT, Human papillomavirus positive oropharyngeal cancer, Head and neck cancer, Dysphagia, Normal tissue complication probability

Purpose: Uncertainties related to geometric distortion are a major obstacle for effectively utilizing MRI in radiation oncology. We aim to quantify the geometric distortion in patient images by comparing their in-treatment position MRIs with the corresponding planning CTs, using CT as the non-distorted gold standard. Methods: Twenty-one head and neck cancer patients were imaged with MRI as part of a prospective Institutional Review Board approved study. MR images were acquired with a T2 SE sequence (0.5 Ã 0.5 Ã 2.5 mm voxel size) in the same immobilization position as in the CTs. MRI to CT rigid registration was then done and geometric distortion comparison was assessed by measuring the corresponding anatomical landmarks on both the MRI and the CT images. Several landmark measurements were obtained including; skin to skin (STS), bone to bone, and soft tissue to soft tissue at specific levels in horizontal and vertical planes of both scans. Inter-observer variability was assessed and interclass correlation (ICC) was calculated. Results: A total of 430 landmark measurements were obtained. The median distortion for all landmarks in all scans was 1.06 mm (IQR 0.6â1.98). For each patient 48% of the measurements were done in the right-left direction and 52% were done in the anteroposterior direction. The measured geometric distortion was not statistically different in the right-left direction compared to the anteroposterior direction (1.5 ± 1.6 vs. 1.6 ± 1.7 mm, respectively, p = 0.4). The magnitude of distortion was higher in the STS peripheral landmarks compared to the more central landmarks (2.0 ± 1.9 vs. 1.2 ± 1.3 mm, pÂ