Radiotherapy and Oncology

INTRODUCTIONWe prospectively evaluated morphologic and functional changes in the carotid arteries of patients treated with unilateral neck radiation therapy (RT) for head and neck cancer. METHODSBilateral carotid artery duplex studies were performed at 0, 3, 6, 12, 18 months and 2, 3, 4, and 5 years following RT. Intima media thickness (IMT); global and regional circumferential, as well as radial strain, arterial elasticity, stiffness, and distensibility were calculated. RESULTSThirty-eight patients were included. A significant difference in the IMT from baseline between irradiated and unirradiated carotid arteries was detected at 18 months (median, 0.073mm vs -0.003mm; P=0.014), which increased at 3 and 4 years (0.128mm vs 0.013mm, P=0.016, and 0.177mm vs 0.023mm, P=0.0002, respectively). A > 0.073mm increase at 18 months was significantly more common in patients who received concurrent chemotherapy (67% vs 25%; P=0.03). A significant transient change was noted in global circumferential strain between the irradiated and unirradiated arteries at 6 months (median difference, -0.89, P=0.023), which did not persist. No significant differences were detected in the other measures of elasticity, stiffness, and distensibility. CONCLUSIONSFunctional and morphologic changes of the carotid arteries detected by carotid ultrasound, such as changes in global circumferential strain at 6 months and carotid IMT at 18 months, may be useful for the early detection of radiation-induced carotid artery injury, can guide future research aiming to mitigate carotid artery stenosis, and should be considered for clinical surveillance survivorship recommendations after head and neck RT.

IntroductionThe urethra is a recommended avoidance structure for prostate cancer treatment. However, even subspecialist physicians often struggle to accurately identify the urethra on available imaging. Automated segmentation tools show promise, but a lack of reliable ground truth or appropriate evaluation standards has hindered validation and clinical adoption. This study aims to establish a reference-standard dataset with expert consensus contours, define clinically meaningful evaluation metrics, and assess the performance and generalizability of a deep-learning-based segmentation model. Materials and MethodsA multidisciplinary panel of four experienced subspecialists in prostate MRI generated consensus contours of the male urethra for 71 patients across six imaging centers. Four of those cases were previously used in an international study (PURE-MRI), wherein 62 physicians attempted to contour the prostate and urethra on the patient images. Separately, we developed a deep-learning AI model for urethra segmentation using another 151 cases from one center and evaluated it against the consensus reference standard and compared to human performance using Dice Score, percent urethra Coverage, and Maximum 2D (axial, in-plane) Hausdorff Distance (HD) from the reference standard. ResultsIn the PURE-MRI dataset, the AI model outperformed most physicians, achieving a median Dice of 0.41 (vs. 0.33 for physicians), Coverage of 81% (vs. 36%), and Max 2D HD of 1.8 mm (vs. 1.6 mm). In the larger dataset, performance remained consistent, with a Dice of 0.40, Coverage of 89%, and Max 2D HD of 2.0 mm, indicating strong generalizability across a broader patient population and more varied imaging conditions. ConclusionWe established a multidisciplinary consensus benchmark for segmentation of the urethra. The deep-learning model performs comparably to specialist physicians and demonstrates consistent results across multiple institutions. It shows promise as a clinical decision-support tool for accurate and reliable urethra segmentation in prostate cancer radiotherapy planning and studies of dose-toxicity associations.

PURPOSERecent studies demonstrate deep learning dose prediction algorithms may produce results like those of traditional knowledge-based planning tools. In this exploratory study, we compared 2D DVH-based knowledge-based planning tools and 3D deep learning-based approaches to assessing radiotherapy plan quality. METHODSPre-validated 2D and 3D dose prediction models were applied to 58 patients with head and neck cancer treated under RTOG 0522 obtained from The Cancer Imaging Archive (TCIA). The 2D model was used to predict dose-volume histogram bands for seven organs at risk (OARs; brainstem, spinal cord, oral cavity, larynx, mandible, right parotid, and left parotid). A 3D dose prediction model was used to predict 3D dose distributions, based on computed tomography images, OAR contours, planning target volumes and prescriptions. The mean and D1% to the seven OARs for the 2D and 3D dose prediction models were compared. Further post predictive analysis was done to quantify the predicted 3D dose sparing for all normal tissues. RESULTSThe two models predicted similar dose sparing to the OARs, with a mean difference of 1.4{+/-}5.5 Gy across all evaluated dose metrics. When looking at the sparing of non-OAR normal tissue regions, the 3D model predicted a mean dose reduction to normal tissue regions of 6.4{+/-}3.0 Gy when compared with the clinical dose. CONCLUSION2D and 3D dose predictions are comparable at predicting dose reductions to OARs. The 3D approach allows for dose visualization, which may support further sparing of normal tissues not typically drawn as OARs on head and neck plans.

PurposeRadiotherapy-induced dysphagia can significantly impair head and neck (H&N) cancer patients quality of life. Despite the dose-dependent relationship between radiotherapy and dysphagia, swallowing structures are not routinely contoured due to time and labor demands. We evaluated atlas-based autosegmentation (ABAS) on MRI, identifying the optimal number of atlases required to efficiently and accurately delineate swallowing structures. MethodsThis study included pre-radiotherapy simulation T2-weighted MRIs from 60 H&N cancer patients enrolled in an IRB-approved observational trial. Scans were acquired on a 1.5T Siemens Aera scanner with H&N immobilization. Swallowing structures, including epiglottis, constrictors, digastric muscles, genioglossus, and others, were manually contoured for 25 atlas patients and 35 test patients. GTV-involved structures were excluded. ABAS was performed with increasing numbers of atlases (1-25) using a random-forest algorithm (ABAS-ADMIRE; Elekta) to determine the optimal atlas count. To mitigate variability from atlas selection, bootstrap resampling was implemented. Dice similarity coefficient (DSC), surface DSC (SDSC), average surface distance (ASD), and 95% Hausdorff distance (HD95) were calculated for each structure. Median computation times were calculated for each atlas count. Hsus MCB analysis identified the minimum atlas number statistically equivalent to the best-performing atlas range. ResultsAcross all structures and metrics, Hsus analysis demonstrated that 2-4 atlases performed similarly to the best-performing atlas count. All structures except constrictors achieved median DSC>0.75 with [≥]2 atlases. Computation times increased linearly with atlas count (range: [~]22-950 seconds for 1-25 atlases). These findings highlight that smaller atlas counts achieve comparable accuracy while significantly improving time efficiency. ConclusionAtlas-based autosegmentation is useful for delineating swallowing muscles in radiotherapy, especially with limited available contoured datasets. Utilizing 2-4 atlases achieves similar geometric accuracy to larger atlas counts, significantly reducing computational time without compromising clinical quality. This balance between efficiency and accuracy supports integration into workflows for better dysphagia prediction and treatment planning.

Purpose/ObjectiveCurrent radiotherapy (RT) planning workflows rely on pre-treatment simulation CT (sCT), which can significantly delay treatment initiation, particularly in resource-constrained settings. While diagnostic CT (dCT) offers a potential alternative for expedited planning, inherent geometric discrepancies from sCT in patient positioning and table curvature limit its direct use for accurate RT planning. This study presents a novel AI-based method designed to overcome these limitations by generating synthetic simulation CT (ssCT) directly from standard dCT for spinal palliative RT, aiming to eliminate the need for sCT and accelerate the treatment workflow. Materials/MethodsssCTs were generated using two neural network models to adjust spine position and correct table curvature. The neural networks use a three-layer structure (ReLU activation), optimized by Adam with MSE loss and MAE metrics. The models were trained on paired dCT and sCT images from 30 patients undergoing palliative spine radiotherapy from a safety-net hospital, with 22 cases used for training and 8 for testing. To explore institutional dependence, the models were also tested on 7 patients from an academic medical center (AMC). To evaluate ssCT accuracy, both ssCT and dCT were aligned with sCT using the same frame of reference rigid registration on bone windows. Dosimetric differences were assessed by comparing dCT vs. sCT and ssCT vs. sCT, quantifying deviations in dose-volume histogram (DVH) metrics, including Dmean, Dmax, D95, D99, V100, V107, and root-mean-square (RMS) differences. The imaging and plan quality was assessed by four radiation oncologists using a Likert score. The Wilcoxon signed-rank test was used to determine whether there is a significant difference between the two methods. ResultsFor the safety-net hospital cases, the generated ssCT demonstrated significantly improved geometric and dosimetric accuracy compared to dCT. ssCT reduced the mean difference in key dosimetric parameters (e.g., Dmean difference decreased from 2.0% for dCT vs. sCT to 0.57% for ssCT vs. sCT with significant improvement under the Wilcoxon signed-rank test) and achieved a significant reduction in the RMS difference of DVH curves (from 6.4% to 2.2%). Furthermore, physician evaluations showed that ssCT was consistently rated as significantly superior for treatment planning images (mean scores improving from "Acceptable" for dCT to "Good to Perfect" for ssCT), reflecting improved confidence in target and tissue positioning. In the academic medical-center cohort--where technologists already apply meticulous pre-scan alignment--ssCT still yielded statistically significant, though smaller, improvements in several dosimetric endpoints and in observer ratings. ConclusionOur AI-driven approach successfully generates ssCT from dCT that achieves geometric and dosimetric accuracy comparable to sCT for spinal palliative RT planning. By specifically addressing critical discrepancies like spine position and table curvature, this method offers a robust approach to bypass the need for dedicated sCT simulations. This advancement has the potential to significantly streamline the RT workflow, reduce treatment uncertainties, and accelerate time to treatment, offering a highly promising solution for improving access to timely and accurate radiotherapy, especially in limited-resource environments.

PurposeThis study aims to characterize dose variations from the original plan for a cohort of head-and-neck cancer (HNC) patients using high-quality computed tomography on rails (CTOR) datasets and evaluate a predictive model for identifying patients needing re-planning. Material and methods74 HNC patients treated on our CTOR equipped machine were evaluated in this retrospective study. Patients were treated at our facility using in-room, CTOR Image Guidance -- acquiring CTOR kV fan beam CT (FBCT) images on a weekly to near-daily basis. For each patient, a particular days delivered treatment dose was calculated by applying the approved, planned beam set to the post image-guided alignment CT image-of-the-day. Total accumulated delivered dose distributions were calculated and compared to the planned dose distribution and differences were characterized by comparison of dose and biological response statistics. ResultsThe majority of patients in the study saw excellent agreement between planned and delivered dose distribution in targets -- the mean deviations of D95 and D98 of the planning target volumes (PTVs) of the cohort are -0.7% and -1.3%, respectively. In critical organs, we saw a +6.5% mean deviation of mean dose in parotid glands, -2.3% mean deviation of maximum dose in brainstem, and +0.7% mean deviation of maximum dose in spinal cord. 10 of 74 patients experienced nontrivial variation of delivered parotid dose which resulted in a normal tissue complication probability (NTCP) increase compared to the anticipated NTCP in the original plan, ranging from 11% to 44%. ConclusionWe determined that a mid-course evaluation of dose deviation was not effective in predicting the need of re-planning for our patient cohorts. The observed non-trivial dose difference to parotid gland delivered dose suggest that even when rigorous, high quality image guidance is performed, clinically concerning variations to predicted dose delivery can still occur.

Background & purposeLate radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patients health and quality of life. Conventional normal tissue complication probability (NTCP) models use discrete dose parameters to predict toxicity risk but fail to fully capture the complexity of this side effect. Deep learning (DL) offers potential improvements by incorporating 3D dose data for all anatomical structures involved in swallowing. This study aims to enhance dysphagia prediction with 3D DL NTCP models compared to conventional NTCP models. Materials & methodsA multi-institutional cohort of 1484 HNC patients was used to train and validate a 3D DL model (Residual Network) incorporating 3D dose distributions, organ-at-risk segmentations, and CT scans, with or without patient- or treatment-related data. Predictions of grade [≥]2 dysphagia (CTCAEv4) at six months post-treatment were evaluated using area under the curve (AUC) and calibration curves. Results were compared to a conventional NTCP model based on pre-treatment dysphagia, tumour location, and mean dose to swallowing organs. Attention maps highlighting regions of interest for individual patients were assessed. ResultsDL models outperformed the conventional NTCP model in both the independent test set (AUC=0.80-0.84 versus 0.76) and external test set (AUC=0.73-0.74 versus 0.63) in AUC and calibration. Attention maps showed a focus on the oral cavity and superior pharyngeal constrictor muscle. ConclusionDL NTCP models performed better than the conventional NTCP model, suggesting the benefit of using 3D-input over the conventional discrete dose parameters. Attention maps highlighted relevant regions linked to dysphagia, supporting the utility of DL for improved predictions.

PurposeHead and neck cancers (HNC) radiotherapy (RT) is associated with inevitable injury to parotid glands and subsequent xerostomia. We investigated the utility of standardized uptake values (SUV) derived from routinely performed 18-fluorodeoxygluocose positron-emission tomography (18FDG-PET) to develop metabolic imaging biomarkers (MIBs) of RT-related parotid injury. MethodsData for oropharyngeal cancer (OPC) patients treated with RT at our institution between 2005-2015 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 3-6ms) 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. Results432 parotid glands, belonging to 108 OPC patients treated with RT, were sub-segmented & analyzed. Xero 3-6ms 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<0.05). A Kruskal-Wallis test showed a significantly higher mean planned RT dose in the contra parotid in the patients with more severe Xero 3-6mo (p= 0.03). Multiple logistic regression showed a combined clinical-dosimetric-metabolic imaging model could predict the severity of Xero 3-6mo; AUC=0.78 (95%CI:0.66-0.85;p<0.0001) ConclusionWe sought to quantify pre- and post-RT 18FDG-PET metrics of parotid glands in patients with OPC. Temporal dynamics of PET-derived metrics can potentially serve as MIBs of RT-related xerostomia in concert with clinical and dosimetric variables.

PurposeUsing a cohort of 582 head and neck cancer patients with nodal disease, we employed clustering over a novel graph-based geometrical representation of lymph node spread in order to identify groups of similar patients. We show that these groups are significantly correlated with radiation-associated dysphagia (RAD), and predictive of late aspiration and feeding tube toxicity. Materials and methodsAll patients received radiotherapy for oropharyngeal cancer (OPC) and had non-metastatic affected lymph nodes in the head and neck. Affected lymph nodes were segmented from pretreatment contrast-enhanced tomography scans and categorized according to consensus guidelines. Similar patients were clustered into 4 groups according to a graph-based representation of affected lymph nodes. Correlation between dysphagia associated symptoms and patient groups was calculated. ResultsOut of 582 patients, 26% (152) experienced toxicity during a follow up evaluation 6 months after completion of radiotherapy treatment. Patient groups identified by our approach were significantly correlated with dysphagia, feeding tube, and aspiration toxicity (p <.0005). Conclusion: Our work successfully stratified a patient cohort into similar groups using a structural geometry, graph-encoding of affected lymph nodes in OPC patients, that were predictive of late radiation-associated dysphagia. Our results suggest that structural geometry-aware characterization of affected lymph nodes can be used to better predict radiation-associated dysphagia at time of diagnosis, and better inform treatment guidelines.

BackgroundStereotactic radiosurgery is a form of radiotherapy that is performed in a single session and focuses high-dose ionizing radiation beams from a collimated radiation source to a small, localized area of the body. Recently, stereotactic radiosurgery has been applied to arrhythmias (stereotactic arrhythmia radioablation - STAR), with promising results reported in patients with refractory, scar-related ventricular tachycardia (VT), a cohort with known high morbidity and mortality. ObjectiveHerein, we describe our experience with the use of CyberKnife, a frameless image-guided linear accelerator stereotactic radiosurgery system, in conjunction with CardioPlan, a cardiac specific radiotherapy planning software, to treat patients with scar-related VT, detailing its early and mid-to long-term results. MethodsThis is a pilot, prospective study of patients undergoing STAR for refractory VT. The anatomical target for radioablation was defined based on the clinical VT morphology, electroanatomical mapping, and study-specific pre-procedural imaging with cardiac computed tomography. The target volume delineated with the aid of CardioPlan was treated with a prescription radiation dose of 25 Gy delivered in a single fraction by CyberKnife in an outpatient setting. Ventricular arrhythmias and radiation-related adverse events were monitored at follow-up to determine STAR efficacy and safety. ResultsFive patients (100 % male, 63 {+/-} 12 years old, 80 % ischemic cardiomyopathy, left ventricular ejection fraction 34 {+/-} 15 %) with refractory VT underwent STAR between January and June 2018. Radioablation was delivered in 82 {+/-} 11 minutes without acute complications. During a mean follow-up of 12 {+/-} 2 months, all patients experienced clinically significant mid-to late-term ventricular arrhythmia recurrence; two patients died of complications associated with their advanced heart failure. There were no clinical or imaging evidence of radiation necrosis or other radiation-induced complications in the organs at risk surrounding the scar targeted by radioablation. ConclusionDespite good initial results, STAR did not result in effective ventricular arrhythmia control in the long term in a selected, high-risk population of patients with scar-related VT. The safety profile was confirmed to be favorable, with no radiation-related complications observed during follow-up. Further studies are needed to explain these disappointing results.

Background and PurposeTo detect changes in apparent diffusion coefficient (ADC) values during radiation therapy for biological image-guided adaptive radiation therapy, the variability in ADC must be characterized. We evaluated the reproducibility of ADC values in head and neck cancers on a 1.5T MR-linac. Methods39 head and neck cancer patients (36 primary tumors, 55 lymph nodes) were imaged with echo-planar imaging diffusion-weighted MRI on a 1.5T MR-linac at two time points before the start of radiation therapy. Mean and median ADC values and volume were measured for each lesion. Absolute and percent reproducibility coefficients (RC) were calculated. Linear regression analyses and F-tests were performed to determine whether lesion volume or time between scans impacted reproducibility. ResultsFor primary tumors & lymph nodes: mean ADC, median ADC, and volume were 1.27 {+/-} 0.33 mm2/s & 1.17 {+/-} 0.34 mm2/s, 1.25 {+/-} 0.35 & 1.16 {+/-} 0.37 mm2/s, and 8.8 {+/-} 12.3 cm3 & 6.5 {+/-} 7.2 cm3, respectively. RC values of mean ADC were 0.355 mm2/s & 0.355 mm2/s for tumors & nodes, and %RC values were 29.1% & 31.1%; values were very similar for median ADC. Reproducibility was not significantly correlated with either volume or scan interval, but a trend of poorer reproducibility in smaller volumes was observed. ConclusionConsidering previous reports that the optimal %{Delta}ADC threshold for response prediction in head and neck cancers is around 15-30%, this sequence on the MR-linac has acceptable reproducibility for detecting larger ADC changes but may still miss some clinically significant changes.

Background and purposeTo characterize patterns and outcomes of brain MR image changes after proton therapy (PT) for skull base head and neck cancer (HNC). Material and methods127 patients treated with PT for HNC who had received at least 40 Gy(RBE) to the brain and had [&ge;] 1 follow-up MRI > 6 months after PT were analyzed. MRIs were reviewed for radiation- associated image changes (RAIC). MRIs were rigidly registered to planning CTs, and RAIC were contoured on T1 (post-contrast) and T2 weighted sequences, and dose-volume parameters extracted. Probability of RAIC over time was calculated using multistate analysis. Univariate/multivariate analyses were performed using Cox Regression. Recursive partitioning analysis was used to investigate dose-volume correlates of RAIC development. Results17.3% developed RAIC. All RAIC events were asymptomatic and occurred in the temporal lobe (14), frontal lobe (6) and cerebellum (2). The median volume of the RAIC on post-contrast T1 was 0.5 cc at their maximum size. The RAIC spontaneously resolved in 27.3%, progressed in 27.3% and improved or were stable in 29.6% of patients. The 3-year actuarial rate of developing RAIC was 14.3%. Brain and RAIC lesion doses were generally higher for temporal lobe RAIC compared to frontal lobe RAIC. RAIC was observed in 63% of patients when V67 Gy(RBE) of the brain [&ge;] 0.17 cc. ConclusionRAIC lesions after PT were asymptomatic and either resolved or regressed in the majority of the patients. The estimated dose-volume correlations confirm the importance of minimizing focal high doses to brain when achievable.

BackgroundMandibular osteoradionecrosis (ORN) is a severe late complication affecting patients with head and neck cancer (HNC) treated with radiotherapy that significantly impacts patients quality of life and can require costly interventions. While radiation dose is a key factor, other clinical and demographic risk factors influence ORN development. Previous predictive models have primarily been single-institutional, limiting their generalizability. The PREDMORN Consortium was established to address these limitations. In this first analysis, we have aimed to reproduce existing statistical association and modelling analyses on the largest and most diverse mandibular ORN cohort worldwide to allow comparison with previous studies. As such, we have developed, tested and externally validated a multi-institutional normal tissue complication probability (NTCP) model for mandibular ORN. MethodsThis retrospective multi-institutional study included 1,184 HNC patients (389 ORN cases) from seven institutions. Clinical, demographic, and dosimetric (DVH) variables were analysed to develop a prediction model (any grade of ORN vs. no ORN) using forward stepwise logistic regression with correlation-based variable pre-selection. The ORN NTCP model was developed on 80% of data from six institutions, tested on the remaining unseen 20%, and externally validated on the seventh institutions dataset. ResultsKey predictors of ORN were D30%, V70Gy, pre-RT dental extractions, and smoking status. The ORN NTCP model demonstrated good calibration and predictive performance, with AUCs of 0.69 for internal testing and external validation, which improved when tested on a sub-cohort of oropharyngeal and locally advanced larynx/hypopharynx cancer cases (AUCs of 0.75). ConclusionThe PREDMORN NTCP model is the largest multi-institutional effort to predict ORN risk in HNC patients. We provide guidance on how to adjust the NTCP predicted probabilities for differences in target population baseline ORN risk to facilitate application of the model. Future research will focus on incorporating imaging-based spatial data and further external validation to enhance clinical applicability.

BackgroundAccurate contouring of target volumes and organs at risk is critical for radiotherapy. While deep learning (DL) models offer efficient automation, their generalizability to real-world clinical cases containing anatomical variations and artifacts requires rigorous validation. PurposeTo evaluate the clinical accuracy and robustness of RatoGuide, a novel DL-based auto-segmentation software, using a dataset derived from routine clinical practice including atypical cases. MethodsThis single-center retrospective study included 36 patients treated for head and neck, thoracic, abdominal, and pelvic cancers. The cohort was intentionally selected to encompass diverse anatomies and artifacts (e.g., pacemakers, artificial femoral head replacement). Auto-contours generated by RatoGuide were compared with expert-approved manual contours. Performance was evaluated quantitatively using the Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95), and qualitatively via a 5-point visual assessment scale (higher is better) by four independent reviewers. A score of [&le;]2 by multiple reviewers was defined as failure. ResultsOverall, the mean DSC, HD95, and visual assessment score were 0.79 {+/-} 0.19, 6.35 {+/-} 12.2 mm, and 3.65 {+/-} 0.88, respectively. The mean DSC exceeded 0.8 in 62% (23/37 organ structures) of the evaluated structure types, and a total of 93.5% (315/337) of all contours were considered clinically acceptable based on visual evaluation . However, lower performance was observed in small structures (e.g., optic chiasm) and low-contrast organs (e.g., esophagus). ConclusionsRatoGuide demonstrated favorable performance for major organs across various anatomical regions, consistent with benchmarks reported in the literature. However, performance variability in atypical cases underscores the necessity of rigorous visual verification by experts for clinical implementation.

PurposeIn order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. In this study, our goal is to evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. MethodsSeven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. 20 autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patients 1-4 prior fractions (individualized patient prior (IPP)) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance, Hausdorff distance, and Jaccard index. For each metric and OAR, performance was compared to the inter-observer variability using Dunns test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions (IPP_RF_4), IPP with 1 fraction (IPP_1)), and one low-performing (PAL with STAPLE and 5 atlases (PAL_ST_5)). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. ResultsDL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 seconds per case) and PAL methods the slowest (3.7 - 13.8 minutes per case). Execution time increased with number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within {+/-}250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). ConclusionsThe autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

ImportanceDepleted skeletal muscle mass (sarcopenia) is associated with decreased survival and cancer control in head and neck cancer patients treated with radiotherapy. There is a need for validated measures of body composition that can be implemented in routine clinical workflow. ObjectiveTo validate the use of bioelectrical impedance analysis (BIA) for body composition analysis and diagnosis of sarcopenia in head and neck cancer patients. DesignIn this prospective observational cohort study, baseline 50 patients with head and neck cancer undergoing radiation therapy (RT) were enrolled between February 2016 and March 2017. Baseline BIA measures of skeletal muscle (SM) mass, fat-free mass (FMM), and fat mass (FM) were compared to CT-based estimates of body composition using linear regression. Sex-specific BIA-derived thresholds for sarcopenia were defined by the maximum Youden Index on receiver operator characteristic (ROC) curves of BIA against CT-defined sarcopenia. Changes in body composition across treatment were compared against changes in body weight using linear regression. ParticipantsIn total, 50 patients with pathologically confirmed stage I to IVB non-metastatic head and neck cancer treated with definitive radiation therapy were enrolled. SettingSingle academic referral center. Main Outcome and MeasureThe primary outcome was relative agreement between baseline lean body mass and fat body mass predicted from BIA measurement and CT imaging. ResultsOf the 48 evaluable patients 16 (33.3%) were sarcopenic at baseline based on CT analysis. BIA measures of body composition were strongly correlated with CT measures: SM mass (r = 0.97; R2 = 0.94; p < 0.0001), FFM (r = 0.97; R2 = 0.94; p < 0.0001) and FM (r = 0.95; R2 = 0.90; p < 0.0001). Relationship with normalized indices of SM mass, FFM, and FM was similar between BIA and CT, but not BIA and body mass index (BMI). Patients lost a mean of 5.7 {+/-} 5.8 kg during treatment, of which 1.5 {+/-} 1.9 kg was SM, 2.6 {+/-} 3.3 kg was FFM, and 2.2 {+/-} 2.6 kg was FM. Eight additional patients developed sarcopenia by the end of RT. ConclusionsBIA provides accurate estimates of body composition in head and neck cancer patients. Implementation of BIA in clinical practice may identify patients with sarcopenia. Trial RegistrationClinicalTrials.gov identifier: NCT02615275

Purpose/Objective(s)Pulsed reduced dose rate (PRDR) radiation (RT) is a re-irradiation (Re-RT) technique that potentially overcomes dose/volume constraints in the setting of previous radiation therapy. While the use of this Re-RT technique has been reported for other disease sites, there is minimal data for its use for recurrent or secondary primary squamous cell carcinoma of the head and neck (HNSCC). In this study, we report preliminary data from our institution of a consecutive cohort of patients with HNSCC who received PRDR Re-RT. Materials/MethodsOut of 11 patients who received PRDR Re-RT from August 2020 to January 2023, 9 had analyzable data. Intensity modulated RT was used for treatment delivery using either volume modulated arc therapy or helical tomotherapy. A wait time between 20cGy arc/helical deliveries was used to achieve the effective low dose rate. Data collected included patient demographic information, prior interventions, diagnosis, radiation therapy dose and fractionation, progression free survival, overall survival, and toxicity rates. ResultsThe median time to PRDR from completion of initial RT was 13 months (range, 6-50 months). All but one patient underwent salvage surgery prior to PRDR. In total, 4 patients received systemic therapy as part of their re-treatment courses. The median follow-up after Re-RT was 7 months. The median OS from PRDR was 7 months (range, 1-32 months). Median PFS was 7 months (range, 1-32 months). One patient (11.1%) had acute grade 3 toxicity, and two patients (22.2%) had late grade 3 toxicities. There were no acute or late grade 4 or 5 toxicities. ConclusionPRDR for Re-RT is a feasible treatment strategy for patients with recurrent or second primary HNSCC. Initial findings from this retrospective review suggest reasonable survival outcomes and potentially improved toxicity; prospective data is needed to establish the safety and efficacy of this technique.

PurposeThis study evaluated the safety and effectiveness of an intraoral light-emitting diode (LED)-based photobiomodulation (PBM) device to reduce the incidence and severity of oral mucositis (OM) from intensity modulated radiation therapy (IMRT) for head and neck cancer (HNC). MethodsThis randomized, double-blind, sham-controlled trial enrolled patients with HNC undergoing high-dose IMRT over 6-8 weeks, with or without concurrent chemotherapy. Participants received daily 10-minute PBM or sham treatments immediately before IMRT sessions. Assessments were conducted at baseline, daily and weekly during IMRT, and two weeks post-IMRT. ResultsEighty-five participants (42 PBM; 43 sham) were enrolled across 12 US sites. No device-related adverse events were observed, and 99.5% of initiated sessions were completed. In the intent-to-treat population, severe OM (WHO Grade [&ge;]3) incidence was significantly lower with PBM across six weeks of IMRT (36.8% vs 57.1%; p = 0.046) and at two weeks post-treatment (10.8% vs 36.4%; p = 0.042). In the per-protocol population, the PBM arm reported significantly greater taste preservation (p = 0.034), lower increases in mouth/throat soreness (p = 0.029) and throat pain (p = 0.028) and needed fewer feeding tube placements (p = 0.073) than the control arm. ConclusionDaily intraoral PBM therapy using an LED-based device was safe, well tolerated, and significantly reduced the incidence of severe OM and associated complications in HNC patients undergoing IMRT with or without concurrent chemotherapy. These findings align with guidelines recommending daily intraoral PBM therapy for preventing cancer therapy-related OM, a dose-limiting toxicity for which effective preventive interventions are needed. Trial RegistrationClinicalTrials.gov Registration Number NCT03972527. Registered on June 3, 2019. Concise SummaryDaily intraoral PBM therapy using an LED-based device was safe, well tolerated, and significantly reduced the incidence of severe OM and associated complications in HNC patients undergoing IMRT with or without concurrent chemotherapy. These findings align with guidelines recommending daily intraoral PBM therapy for preventing cancer therapy-related OM, a dose-limiting toxicity for which effective preventive interventions are needed.

Background and purposeRadiation-induced changes in brain tissue may relate to post-radiotherapy (RT) cognitive decline. Our aim is to investigate changes of the brain microstructural properties after exposure to radiation during clinical protocols of RT using diffusion MRI (dMRI). Methods and MaterialsThe susceptibility of tissue changes to radiation was investigated in a clinically heterogenic cohort (age, pathology, tumor location, type of surgery) consisting of 121 scans of 18 patients (10 females). The imaging dataset included 18 planning CTs and 103 dMRI scans (range 2-14, median = 6 per patient) assessing pre-operative, post-operative pre-RT and post-RT states. Diffusion tensor imaging (DTI) metrics were estimated from all scans for a region-of-interest based linear relation analysis between mean dose and change in DTI metrics, while partial volume effects were regressed out. ResultsThe largest regional dose dependency with mean diffusivity appear in the white matter of the frontal pole in the left hemisphere by an increase of 2.61 %/(Gy x year). Full brain-wise, pooled results for white matter show fractional anisotropy to decrease by 0.85 %/(30Gy x year); mean diffusivity increase by 9.17 %/(30Gy x year); axial diffusivity increase by 7.30%/(30Gy x year) and radial diffusivity increases by 10.63%/(30Gy x year). ConclusionsWhite matter is susceptible to radiation with some regional variability where diffusivity metrics demonstrate the largest relative sensitivity. This suggests that dMRI is a promising tool in assessing microstructural changes after RT, which can help in understanding treatment-induced cognitive decline.

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that are able to demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient (DSC) up to 0.771 and median 95% Hausdorff distance (95% HD) as low as 2.919 mm. We employ label fusion ensemble approaches, including Simultaneous Truth and Performance Level Estimation (STAPLE) and a voxel-level threshold approach based on majority voting (AVERAGE), to generate consensus segmentations on the test data by combining the segmentations produced through different trained cross-validation models. We demonstrate that our best performing ensembling approach (256 channels AVERAGE) achieves a mean DSC of 0.770 and median 95% HD of 3.143 mm through independent external validation on the test set. Concordance of internal and external validation results suggests our models are robust and can generalize well to unseen PET/CT data. We advocate that ResUNet models coupled to label fusion ensembling approaches are promising candidates for PET/CT oropharyngeal primary tumors auto-segmentation, with future investigations targeting the ideal combination of channel combinations and label fusion strategies to maximize segmentation performance.