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BACKGROUND: Temporal lobe necrosis (TLN) is a potential late effect after radiotherapy for skull base head and neck cancer (HNC). Several photon-derived dose constraints and normal tissue complication probability (NTCP) models have been proposed, however variation in relative biological effectiveness (RBE) may challenge the applicability of these dose constraints and models in proton therapy. The purpose of this study was therefore to investigate the influence of RBE variations on risk estimates of TLN after Intensity Modulated Proton Therapy for HNC. MATERIAL AND METHODS: 75 temporal lobes from 45 previously treated patients were included in the analysis. Sixteen temporal lobes had radiation associated Magnetic Resonance image changes (TLIC) suspected to be early signs of TLN. Fixed (RWD(Fix)) and variable RBE-weighed doses (RWD(Var)) were calculated using RBE=1.1 and two RBE models, respectively. RWD(Fix) and RWD(Var) for temporal lobes were compared using Friedman’s test. Based on RWD(Fix), six NTCP models were fitted and internally validated through bootstrapping. Estimated probabilities from RWD(Fix) and RWD(Var) were compared using paired Wilcoxon test. Seven dose constraints were evaluated separately for RWD(Fix) and RWD(Var) by calculating the observed proportion of TLIC in temporal lobes meeting the specific dose constraints. RESULTS: RWD(Var) were significantly higher than RWD(Fix) (p

Background and purpose To characterize patterns and outcomes of brain MR image changes after proton therapy (PT) for skull base head and neck cancer (HNC). Material and methods Post-treatment MRIs ≥6 months were reviewed for radiation-associated image changes (RAIC) in 127 patients. All patients had received at least a point dose of 40 Gy(RBE) to the brain. The MRIs were rigidly registered to planning CTs and RAIC lesions were contoured both on T1 weighted (post-contrast) and T2 weighted sequences, and dose–volume parameters extracted. Probability of RAIC was calculated using multistate survival analysis. Univariate/multivariate analyses were performed using Cox Regression. Recursive partitioning analysis was used to investigate dose–volume correlates of RAIC development. Results 17.3% 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 contrast enhanced RAIC lesion was 0.5 cc at their maximum size. The RAIC resolved or improved in 45.5% of the patients and were stable or progressed in 36.4%. The 3-year actuarial rate of developing RAIC was 14.3%. RAIC was observed in 63% of patients when V67 Gy(RBE) of the brain ≥0.17 cc. Conclusion Small RAIC lesions after PT occurred in 17.3% of the patients; the majority in nasopharyngeal or sinonasal cancer. The estimated dose–volume correlations confirm the importance of minimizing focal high doses to brain when achievable.

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 patient’s 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 Dunn’s 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.

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Purpose/Objective(s) The investigators’ 2013 use it or lose it study suggested functional benefit of two pharyngeal activities during head and neck radiotherapy (RT) – maintenance of oral intake (EAT) and swallowing exercise. EAT and EXERCISE independently associated with better odds of resuming a regular diet in long term survivorship and shorter duration of gastrostomy (FT) dependence. The prior work is limited by the retrospective nature of the dataset and historically far higher FT utilization. Our aim was to validate the previous work in a contemporary cohort of oropharyngeal cancer (OPC) survivors treated with RT using prospectively acquired validated outcome measures. Materials/Methods Endpoints included subacute diet after RT per the performance status scale for head and neck cancer (PSS-HN; solid food diet coded as ≥60 and no FT) and length of FT-dependence in days. Primary independent variables included oral intake (PO) at the end of RT (nothing per oral/NPO; partial PO; full PO) and swallow exercise adherence. Multiple linear regression and logistic regression models were analyzed adjusting for tumor location, baseline diet, chemotherapy and N and T stage. Results Analysis included 595 patients treated with primary radiotherapy (RT; 19% 111) /chemoradiation (CRT; 73% 434) or primary TORS + CRT (8% 50) for OPC (base of tongue/glossopharyngeal sulcus [46% 276]; tonsil [44% 263]; other [9% 56]). At the end of RT 9% of patients were NPO (55), 19% partial PO (115), 71% full PO (425). Statistically significant (P Conclusion These prospective registry data validate prior work that indicate independent benefit of EAT and swallowing EXERCISE adherence during RT on subacute functional outcomes. Patients who maintained full PO and/or exercise were more likely to eat solid foods by 3-6 months after treatment, while patients who EAT during treatment expectedly have the shortest feeding tube dependence.

Purpose/Objective(s) Stereotactic body radiotherapy (SBRT) is an emerging modality for the reirradiation of head and neck cancers (HNC). The need to use smaller reirradiation volumes to limit severe toxicity concerns represents a challenge in determining optimal target coverage. Herein, we investigate the associated patterns of failure after SBRT using previously defined spatial and dosimetric analysis (Mohamed et al, Radiat Oncol. 2017). Materials/Methods SBRT reirradiation HNC cases treated between 2014-2019 and surveilled by CT, PET/CT, and/or MRI were reviewed retrospectively in this IRB approved study. Patients with radiographic evidence of recurrence within the head and neck region after SBRT were identified. Multifocal disease was defined as any non-contiguous FDG-avid or biopsy positive disease seen on imaging. Diagnostic scans were co-registered with planning CT scans (pCT) for manual segmentation of recurrent gross volumes (rGTV). rGTVs were then deformed to co-registered pCTs for failure classification. The classification followed a granular typology of 5 failure categories: central high-dose (type A or “in-field”), peripheral high-dose (type B), central low-dose (type C), peripheral low-dose (type D or “marginal”), and extraneous-dose (type E or “out of field”). Each type was determined based on the centroid location of rGTV relative to the planning target volumes plus the dose received by 95% of rGTV volume. Results A total of 106 SBRT cases were reviewed out of which 34 patients (32%) recurred after SBRT; 27 (79%) patients had squamous cell carcinomas, 7 (21%) received postoperative SBRT, and 24 (71%) received concurrent systemic therapy. Twenty-three (68%) patients received 42.5-45 Gy in 5 fractions (Fx) and 7 (21%) patients received 36 Gy in 6 Fx. Median time to failure was 5.5 months (range, 1-22 months). Twenty-two recurrences (65%) were out-of-field type E failures, 6 (18%) type A, 3 (9%) type C, and 3 (9%) type D. Fourteen of 16 (88%) multifocal SBRT reirradiation cases had Type E failures compared to 8 of 18 (44%) unifocal SBRT cases. Among the 6 in-field failures, 3 received 36 Gy in 6 Fx and 3 received 42.5-45 Gy in 5 Fx. Conclusion Post SBRT reirradiation recurrences were seen in one-third of cases with the majority of failures being Type E (outside the treatment field), particularly in patients who were treated for multifocal disease. The use of 42.5-45 Gy in 5 Fx appears to be an effective biological dose for local tumor control. Marginal (Type C and D) failures may be reduced with larger margins, but limit feasibility of SBRT. Therefore, future reirradiation strategies should aim to improve patient selection, multimodal therapy, and margin definition in the HNC SBRT reirradiation setting.

PURPOSE/OBJECTIVE(S) Post-treatment symptoms are a focal point of follow-up visits for head and neck cancer patients. While symptoms such as dysphagia and shortness of breath may empirically guide earlier detection of disease recurrence, their association with patient outcomes is less well studied, especially for HPV-positive disease. This study aimed to investigate the association between patient reported dysphagia and shortness of breath at 3-to-6 months following radiotherapy (RT) and overall survival (OS). MATERIALS/METHODS Data were collected from oropharyngeal cancer patients that received RT with curative intent from 2015-2019. Included patients had self-reported scores for shortness of breath, choking/coughing ("choke"), and difficulty swallowing/chewing ("swallow"), which were surveyed via the MD Anderson Symptom Inventory-Head and Neck Module (MDASI-HN) questionnaire at 3-to-6 months after RT. Symptom severity was scored from 0-10. Significant predictors of OS were analyzed via Cox regression. Symptom scores were also discretized for Kaplan-Meier (KM) analysis, with more severe symptoms being 6+ for "swallow" and "choke" and 2+ for shortness of breath. RESULTS Symptom scores were collected at 3-to-6 months from 470 patients. The majority (91.3%) were HPV-positive. Median follow-up time was 31.7 months (IQR: 21.9-42.1). Univariable Cox regression (Table 1) showed significant associations between OS and MDASI scores for shortness of breath, "choke," and "swallow." Furthermore, a composite variable integrating scores of all three symptoms had the best predictive value for OS (c-index = 0.748). Symptom severity stratified patients based on OS for all three symptoms, with more severe symptoms predicting worse OS on KM analysis (P < 0.001 for all symptoms). On subset analysis more severe symptoms remained significantly associated with worse OS among HPV-positive patients (P < 0.01 for all symptoms). CONCLUSION Quantitative early patient-reported measures of dysphagia and shortness of breath are significant predictors of OS in head and neck cancer, in particular for HPV-positive disease. Integrating quantitative symptom surveys into post-treatment surveillance may be beneficial for individualizing follow-up strategies.

Purpose/Objective(s) As current radiotherapy (RT) treatment schedules are not personalized for individual patients, with the prescribed dose being uniform for particular subtypes and stages of cancer, despite highly variable responses between patients, we performed an in silico trial to determine optimal personalized RT dose for head and neck cancer patients in order to minimize excess dose with the objective of minimizing toxicity and improving QOL without sacrificing tumor control. Materials/Methods Weekly tumor volume data were collected from two cancer centers for n = 39 head and neck cancer patients that received 66-70 Gy in 2-2.12 Gy daily fractions or with accelerated fractionation. Clinical outcome data, i.e., locoregional control (LRC) and disease-free survival (DFS), were also collected. Tumor volume reduction was connected to LRC by means of a volume reduction threshold associated with LRC. Due to limited patient numbers, the in silico trial was performed in an iterative leave-one-out fashion as follows: (1) a simple 2 parameter model of tumor growth and response to RT was calibrated to tumor volume data from 38 patients, (2) the calibrated model parameters were combined with the left-out patient's tumor volume data from weeks 1-4 of RT to simulate tumor volumes forward sampling from a patient-specific distribution of radiosensitivity parameters, (3) minimum cumulative radiation dose required for local tumor control was estimated by calculating the dose at which > 95% the simulated tumor volume trajectories were below the volume reduction threshold associated with LRC. Results The optimal minimum radiation dose required for LRC was calculated for each patient. We found that 87% of the patients (34/39) received a higher total dose than estimated as necessary by our model with an average overdose of 37 Gy/patient, while the remaining patients were estimated to have received too little dose, with an average underdose of 47 Gy/patient. Notably, our results showed that the current one-size-fits-all approach results in no patient receiving their optimal RT dose. Conclusion Here we show a method to determine a personalized minimum RT dose to achieve LRC. Such simulations and estimates may allow radiation oncologists to identify candidates for dose de-escalation, dose escalation, or additional concurrent treatments, such as chemotherapy.

Purpose/objective(s) Due to the differences in patients' lung conditions, radiation pneumonia (RP) sometimes occurs even if the lung's dose limitation is met. We are aiming to further predict the occurrence odd of radiation pneumonitis in NSCLC after receiving radiotherapy. Consider based on the dose-volume level to segment the lungs receiving radiation, use radiomic modeling, and evaluate its predictive effectiveness. Materials/methods The study retrospectively enrolled 306 CT scans from 102 NSCLC patients who received IMRT or VMAT from Oct 2015 to Aug 2016. A 0-5 grade RP occurred within 12 months after radiotherapy, considered as the endpoint event. The evaluation of radiation pneumonia in this study case was based on CTCAE 5.0. The whole cohort was divided into two groups, the RP and non-RP. The lung was segmented based on the dose-volume line as V5-10, V10-20, V20-30, V30-40, V40-50, and V50-60 as the region of interest (ROI). Then we register the ROI to the lung of the CT images in the middle and after the radiotherapy. We utilize open source software to extract the image features for each ROI to develop and validate the radiomics model to predict radiation pneumonitis. Two classifier models of random forest (Random forest, RF) and support vector machine (SVM) were used for classifying and learning samples' characteristics to distinguish between the pneumonia-occurring group and the non-occurring group effectively. We randomly divided the sample size into a training and validation group at a 4:1 ratio, and the five-fold cross-validation model (K fold cross-validation) was used for verification. The predictive performance was evaluated using overall accuracy for this triple classification task. Results The overall incidence of radiation pneumonia was 36%, and the incidence of severe radiation pneumonia (grade 3 and above) was 4%. The support vector machine (SVM) model has the best prediction performance compared to random forest, with an average accuracy rate of 0.72 and an average AUC value of 0.66. Under different dose line volume segmentation, the accuracy of the prediction models in pre-, mid-, and after treatment are 0.724, 0.744, 0.775 (V5-10), 0.736, 0.719, 0.737, (V10-20), 0.728, 0.696, 0.693 (V20-30), 0.671, 0.678, 0.684 (V30-40), 0.747, 0.724, 0.705 (v40-50), 0.799, 0.743, 0.726 (V50-60), respectively. The model constructed in the 50-60Gy area of CT before radiotherapy has the best performance in predicting radiation pneumonia. Conclusion Radiation pneumonia is a side effect that needs to be paid attention to in thoracic radiotherapy, and its incidence and severity directly affect patients' survival. Traditional radiation pneumonia prediction methods are relatively general. With the development of precise radiotherapy, further accurate radiation pneumonia prediction models are required. Radiomics can become a reliable prediction method for radiation pneumonia.

Purpose Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT. Methods and Materials For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LETd) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve. Results An overall statistically significant RAIC association with dose and LETd was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LETd, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LETd effect demonstrated increased risk of RAIC with increasing LETd for the majority of patients. Estimated probability of RAIC with LETd = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD15 were estimated to be 63.6 and 50.1 Gy with LETd equal to 2 and 5 keV/µm, respectively. Conclusions Our results suggest that the LETd effect could be of clinical significance for some patients; LETd assessment in clinical treatment plans should therefore be taken into consideration. publishedVersion

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 ≥ 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 ≥ 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.

Purpose/Objective(s) This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer patients treated with radiotherapy. Materials/Methods Contrast-enhanced CT images were collected for patients with confirmed ORN diagnosis at MD Anderson Cancer Center between 2008 and 2018. The ORN region of interest (ROI) was segmented manually in each image. The control ROIs of the contralateral health mandible were generated by a Python script then adjusted manually in each image. An open-source software was then used to extract the radiomic features from both ORN and control ROIs after the application of intrinsic filters. The pairwise correlation filter was used to remove radiomic features whose pairwise correlation was ≥0.99. Filter algorithms were then used to further reduce the number of radiomic features. After that, wrapper and embedded methods were applied on the resulting radiomic features. Finally, Gini importance and Recursive Feature Elimination (RFE) were used to select the final radiomic features for the predictive model. The support vector machine (SVM) with linear kernel was used for the binary classification of ORN and normal mandibular bone. The performance of the model was evaluated using the Area Under Curve (AUC). Results 150 patients with radiologically established ORN were included. The mean age was 62.3 years (range 27-82). The mean time between the end of RT and ORN detection was 32.6 months. A total of Initial 1316 radiomic features were considered. The pairwise correlation omitted 432 features with a correlation ≥ 0.99. The RFE based on the Gini index selected 5 radiomics features in our HNC cohort. We validated this binary classification model using 5-fold cross-validation. During this validation, the range of AUC was (0.84–0.95) & the average AUC was 0.90. This AUC range reflect the high performance of the final classifier in the differentiation between ORN and normal mandible using CECT images. Conclusion Radiomic features were successfully used to build a model to discriminate ORN and normal mandibular bone in head and neck cancer patients. Our statistical model achieved satisfying prediction accuracy and can be potentially used for ORN prediction purpose upon external validation.

PURPOSE/OBJECTIVE(S) Xerostomia is a common sequela of radiation therapy (RT) and chemoradiotherapy (CRT). Head and neck cancer treatment can contribute to salivary glands injury with diminished salivary production and changes in consistency, volume, and pH of saliva. Previous studies examining xerostomia have investigated RT regimens, dosimetric predictors, and quality of life (QoL) associations but few have identified clinicodemographic risk factors for xerostomia and quantified their associations among oropharyngeal carcinoma (OPC) survivors. The objective of this study was to identify risk factors for xerostomia among long-term OPC survivors. MATERIALS/METHODS This was a cross-sectional study conducted at a comprehensive cancer center. Study participants included disease-free, adult OPC survivors who completed curative definitive radiotherapy treatment between January 2000 and December 2013, and responded to a survey administered from September 2015 to July 2016. This study included 881 OPC survivors with a median survival duration at time of survey of 7 years (range, 1-16 years) of which self-reported xerostomia scores were available for 853 participants. The primary outcome variable was patient-reported xerostomia measured using the MD Anderson Symptom Inventory Head and Neck Cancer Module. Clinicodemographic risk factors for moderate to severe xerostomia were identified using multivariable logistic regression. RESULTS Of 853 OPC survivors who responded to the xerostomia question, 337 (39.5%) reported moderate to severe xerostomia. Multivariable logistic regression identified current smoking at time of survey (OR:2.57, 95% CI: 1.20-5.53, P = 0.016) as a risk factor for moderate to severe xerostomia and concurrent cetuximab chemotherapy (OR:0.63; 95% CI: 0.41-0.97, P = 0.038) as a protective factor. Additionally, female sex (OR:1.81, 95% CI: 1.21-2.71, P = 0.004) and less than high school education (OR:1.68, 95% CI: 1.15-2.45, P = 0.008) were risk factors for moderate to severe xerostomia and bilateral intensity modulated radiotherapy (IMRT) combined with proton therapy (OR:0.34, 95% CI: 1.16-0.73, P = 0.005) and ipsilateral IMRT (OR:0.18, 95% CI: 0.07-0.46, P < 0.001) were protective. CONCLUSION In this large xerostomia study, about 4 out of 10 OPC survivors reported moderate to severe xerostomia over long-term follow-up. Continued smoking after cancer diagnosis, sex, and education were identified as important risk factors of moderate to severe xerostomia and concurrent cetuximab therapy and more modern RT regimens had a protective association. Sustained smoking post-diagnosis is a modifiable risk factor which can be addressed with continued smoking cessation efforts. Lastly, these results can inform future research and targeted patient-centered interventions to monitor and manage RT-associated xerostomia and preserve QoL among OPC patients.

Purpose/Objective(s) In the setting of head and neck (HN) re-irradiation (reRT), the goal is to adequately cover disease with as small a volume as appropriate to minimize normal tissue toxicity without compromising local control. However, it can be a challenge to gauge the extent microscopic disease beyond the gross tumor to be included in the reRT volume. Here, we aim to determine the patterns of failure after HN reRT. Materials/Methods Patients treated with curative intent intensity modulated radiation therapy (IMRT) or proton reRT from September 1999 – June 2015 were evaluated. A total of 148 patients with inoperable tumors received definitive reRT (intact) and 61 who underwent salvage surgery received post-operative reRT (postop). Those who had locoregional recurrence (LRR) and intact imaging data were analyzed. Using deformable imaging registration, diagnostic images at time of recurrence were overlaid on reRT treatment plans. The site of recurrence was delineated and evaluated by at least 2 radiation oncologists/radiologist with HN expertise. The patterns of failure were classified into five types based on spatial and dosimetric criteria: A (central high dose, or “in-field”), B (peripheral high dose), C (central elective dose), D (peripheral elective dose), and E (extraneous dose, or “out-of-field”). For patients who had multiple sites of recurrences, each site was classified independently. The kappa statistic was used to assess the inter-observer agreement on failure classification. Results Of the 209 patients treated, 67 developed LRR. This represented 44 of 148 (30%) intact patients (median dose 66 Gy) and 23 of 61 (38%) postop patients (median dose 60 Gy). Of these, 41 (61%) had imaging data for analysis, representing 47 sites of failures. Median time to failure was 21 weeks (range: 4 – 220 weeks). Patterns of failure among intact reRT were as follows: 13 Type A (39%), 6 Type B (18%), 1 Type C (3%), and 13 Type E (39%). The majority (79%) of failures were either in the high dose field or completely out of field. Interobserver agreement was 0.92. Patterns of failure among postop reRT were as follows: 4 type A (17%), 5 type B (22%), 5 type C (22%) and 5 type D (22%). The majority (56%) of recurrences were within 1 cm of the reconstructive flap bed, 40% recurred in the same primary subsite, and 17% recurred in the 1st echelon nodal level. Interobserver agreement was 0.68. Conclusion In patients who recurred after reRT, the majority was either within the central high-dose region, or completely outside the high-dose region suggesting poor disease biology. Biologic dose-escalation and or the addition of systemic therapies may offer improved disease control.

11032 Background: The open access (OA) publishing model represents an exciting opportunity to facilitate dissemination of scientific information to global audiences. In contrast to many traditional models, which require readers to pay subscription fees or rely upon institutional subscriptions for article access, the OA model grants free access to all consumers. However, OA publication is often associated with significant article processing charges (APCs) for authors, which may thus serve as a barrier to publication. In this investigation, we aimed to identify journal-level factors associated with OA publication costs in oncology journals. Methods: We identified oncology journals using the SCImago Journal & Country Rank database. All journals under the “Oncology” category that offer an OA publishing option with APC data openly available were included. For all journals, we searched journal websites and tabulated journal characteristics, including APC amount (USD), OA model (hybrid vs full), journal 2-year impact factor (IF), H-index, number of citable documents, primary treatment modality (surgery, radiation, medical, non-specific), treatment site (e.g. breast, etc), and continent of origin. Pearson correlation was used to evaluate univariate linear relationships between variables; for variables with significant correlation, we generated a multiple regression model to identify journal characteristics independently associated with OA APC amount. Results: Of 367 oncology journals screened, 266 met final inclusion criteria. The median APC was 2810 USD (range 0 – 5200). On univariate linear correlation regression testing, journals with the full OA model (p < 0.001), higher journal IF (p < 0.001), higher H-index (p < 0.001), greater number of published articles (p < 0.001), and those from North America or Europe (p < 0.001) tended to have higher OA publishing costs. When these co-variates were analyzed in a multiple regression model, only full OA status (p < 0.001), higher IF (p < 0.001), and North American or European origin (p < 0.001) persisted as independently associated with greater OA APC. Conclusions: Large APCs may serve as a barrier to OA publication and therefore create or exacerbate disparities among scientific investigators seeking to share their research. In this investigation, we find that OA publication costs are greater in oncology journals that utilize the hybrid OA model, have higher IF, and are based in North America or Europe. These findings may inform targeted action to help the oncology community fully appreciate the benefits of open science.

e18826 Background: New cancer treatments have the potential to cause economic stress at the personal and societal level. Little is known on the evaluation of financial outcomes in clinical trials. We hypothesized that randomized controlled trials (RCTs) are unlikely to have pre-planned analyses to look at economic outcomes that address financial toxicity to patients and cost-effectiveness to society. Methods: Interventional therapeutic-intent phase 3 cancer-specific RCTs were identified through ClinicalTrials.gov. Pre-planned economic outcomes addressing financial toxicity or cost-effectiveness were identified. Results: We identified 1,069 interventional phase 3 oncology RCTs. Overall, 101 (9.4%) had pre-planned to evaluate quality of life using the European Organization for Research and Treatment of Cancer Quality of Life (QOL) Questionnaire (EORTC QLQ-C30) instrument, of which a single question (Q28) relates to financial toxicity. However, only ten (0.94%) trials included pre-planned financial/economic endpoints; all were secondary endpoints. Among those planning to evaluate economic outcomes, 6/10 planned to collect data and report on financial distress using Q28 on EORTC QLQ-C30 and 3/10 pre-planned performing cost-effectiveness analyses. One study planned to perform an economic evaluation, including health utilities, as measured by the EuroQol-Five Dimension (EQ-5D) Questionnaire. All ten trials were industry–sponsored, and two were co-sponsored by a National Cancer Institute (NCI) cooperative group. The majority (8/10) were superiority-designed trials, and 6/10 included progression-free survival as the primary endpoint. The majority (6/10) assessed targeted therapy as the primary intervention. Multinational enrollment was predominant (8/10). Of the 10 trials, nine had a published manuscript. One was closed because of accrual issues. Among the six trials that had pre-specified reporting on financial distress, two published on financial toxicity. Of the two trials that had a priori planned to do a cost-effective analysis, one was published. However, overall 3/9 had published a cost-effective analysis. Conclusions: Less than one percent of oncology RCTs have pre-specified plans to evaluate economic outcomes associated with personal financial distress or cost-effectiveness. For the trials that had pre-planned reporting on financial toxicity, the instrument used to evaluate financial toxicity is a single question which has not been validated as a standalone assessment. Cost-effectiveness analyses were more likely to be published than on financial toxicity and were often completed as a post-hoc analysis. Given that many newly investigated therapies increase the financial burden on patients and add cost to the healthcare system more broadly, pre-planned evaluation of economic outcomes in RCTs is imperative in order to create a value-based framework in assessing new therapies.