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The study analysis plan, Figures S1-S6, Table S1

Although concurrent chemoradiation (CRT) and durvalumab consolidation has become a standard treatment for stage III non-small cell lung cancer (NSCLC), clinicopathologic and genomic factors associated with its efficacy remain poorly characterized. In this multi-institutional cohort study of 328 patients treated with CRT and durvalumab, we are the first to identify that very high PD-L1 tumor proportion score (TPS) expression (≥90%) and increased tumor mutation burden (TMB) are independently associated with prolonged disease control. Additionally, we identify the impact of pneumonitis and its timing on disease outcomes among patients who discontinue durvalumab: compared to patients who experienced early-onset pneumonitis (

Pulmonary toxicity is dose-limiting in stereotactic body radiation therapy (SBRT) for tumors that abut the proximal bronchial tree (PBT), esophagus, or other mediastinal structures. In this work we explored published models of pulmonary toxicity following SBRT for such ultracentral tumors in an independent cohort of patients.The PubMed database was searched for pulmonary toxicity models. Identified models were tested in a cohort of patients with ultracentral lung tumors treated between 2008 and 2017 at one large center (N = 88). This cohort included 60 % primary and 40 % metastatic tumors treated to 45 Gy in 5 fractions (fx), 50 Gy in 5 fx, 60 Gy in 8 fx, or 60 Gy in 15 fx prescribed as 100 % dose to PTV.Seven published NTCP models from two studies were identified. The NTCP models utilized PBT max point dose (Dmax), D0.2 cmPulmonary toxicity models were identified from two studies and explored within an independent ultracentral lung tumor cohort. A modified Dmax to 0.1 cm

Based on an analysis of published literature, our department recently lowered the preferred mean esophagus dose (MED) constraint for conventionally fractionated (2 Gy/fraction in approximately 30 fractions) treatment of locally advanced non‐small cell lung cancer (LA‐NSCLC) with the goal of reducing the incidence of symptomatic acute esophagitis (AE). The goal of the change was to encourage treatment planners to achieve a MED close to 21 Gy while still permitting MED to go up to the previous guideline of 34 Gy in difficult cases. We compared all our suitable LA‐NSCLC patients treated with plans from one year before through one year after the constraint change. The primary endpoint for this study was achievability of the new constraint by the planners; the secondary endpoint was reduction in symptomatic AE. Planners were able to achieve the new constraint in statistically significantly more cases during the year following its explicit implementation than in the year before (P = 0.0025). Furthermore, 38% of patients treated after the new constraint developed symptomatic AE during their treatment as opposed to 48% of the patients treated before. This is a clinically desirable endpoint although the observed difference was not statistically significant. A subsequent power calculation suggests that this is due to the relatively small number of patients in the study.

Background Immune checkpoint inhibitors (ICIs) and thoracic radiotherapy are increasingly used to treat advanced cancers. Despite data indicating exaggerated radiation toxicities in patients with autoimmune disease, the safety of thoracic radiotherapy in patients with prior ICI-associated immune-related adverse events (irAEs) is undefined. Patients and methods Patients treated from 2014 to 2020 with ICIs were queried for receipt of corticosteroids and radiotherapy. Patients who received thoracic radiation after symptomatic irAEs were assessed for ≥grade 2 radiation pneumonitis (RP). Characteristics predictive of RP were assessed using logistic regression and response relationships were modeled. Results Among 496 assessed patients, 41 with irAE history subsequently treated with thoracic radiotherapy were analyzed. Most irAEs were grade 2 (n = 21) and 3 (n = 19). Median time from irAE onset to radiotherapy was 8.1 months. Most patients received stereotactic body radiation therapy (n = 20) or hypofractionated radiotherapy (n = 18). In total, 25 patients (61%) developed ≥grade 2 RP at a median of 4 months from radiotherapy and 11 months from onset of irAEs. Three months from RP onset, 16 of 24 (67%) assessable patients had persistent symptoms. Among patients with prior ICI pneumonitis (n = 6), five patients (83%) developed ≥grade 2 RP (grade 2, n = 3; grade ≥3, n = 2). The mean lung radiation dose (MLD) predicted for RP (odds ratio: 1.60, P = 0.00002). The relationship between MLD and RP was strong (area under the receiver-operating characteristic curve: 0.85) and showed an exaggerated dose-response. Among patients with an MLD >5 Gy (n = 26), 21 patients (81%) developed ≥grade 2 RP. Conclusion This is the first study assessing the toxicity of radiotherapy among patients with prior irAEs from ICIs. Patients with prior irAEs were found to be at very high risk for clinically significant and persistent RP from thoracic radiotherapy. Careful consideration should be given to the possibility of an increased risk of RP, and close monitoring is recommended in these patients.

Introduction Durvalumab after concurrent chemoradiation (cCRT) is now standard of care for unresected stage III non–small cell lung cancer (NSCLC). However, there is limited data on radiation pneumonitis (RP) with this regimen. Therefore, we assessed RP and evaluated previously validated toxicity models in predicting for RP in patients treated with cCRT and durvalumab. Methods Patients treated with cCRT and ≥ 1 dose of durvalumab were evaluated to identify cases of ≥ grade 2 RP. The validity of previously published RP models was assessed in this cohort as well a reference cohort treated with cCRT alone. The timing and incidence of RP was compared between cohorts. Results In total, 11 (18%) of the 62 patients who received cCRT and durvalumab developed ≥ grade 2 RP a median of 3.4 months after cCRT. The onset of RP among patients treated with cCRT and durvalumab was significantly longer vs the reference cohort (3.4 vs 2.1 months; P = .01). Numerically more patients treated with cCRT and durvalumab developed RP than patients in the reference cohort (18% vs 9%, P = .09). Among patients treated with cCRT and durvalumab, 82% (n = 9) were responsive to treatment with high‐dose glucocorticoids. Previously published RP models widely underestimated the rate of RP in patients treated with cCRT and durvalumab [AUC ~ 0.50; p(Hosmer‐Lemeshow): 0.98‐1.00]. Conclusions Our data suggest a delayed onset of RP in patients treated with cCRT and durvalumab vs cCRT alone, and for RP to develop in a greater number of patients treated with cCRT and durvalumab. Previously published RP models significantly underestimate the rate of symptomatic RP among patients treated with cCRT and durvalumab., Radiation pneumonitis (RP) is a significant toxicity of thoracic radiation, yet little is known regarding RP in lung cancer patients treated with definitive chemoradiation (cCRT) and durvalumab. We found patients treated with cCRT and durvalumab to have a significantly later onset of RP and a greater frequency of RP compared to patients treated with cCRT alone, we also found previously published toxicity models to be unsuccessful in predicting for RP among patients treated with cCRT and durvalumab.

Background and purpose Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardio-pulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures. Materials and methods The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability. Results The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95–0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value ⩾ 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring. Conclusion The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.

BACKGROUND AND PURPOSE: We test the hypothesis that unsatisfactory outcomes after concurrent chemoradiotherapy (RT) for locally advanced non-small cell lung cancer (LA-NSCLC) are due to treatment-related immunosuppression. MATERIALS AND METHODS: White blood cells (WBCs) data were retrospectively collected for all stage IIIA/B LA-NSCLC patients before and after (after RT: two weeks, two months, four months) concurrent chemotherapy and intensity-modulated RT in which patients were treated to a median of 63Gy (1.8–2.0 Gy/fractions) in 2004–2014 (N=155). Nine WBC variables were generated from pre-RT normalized absolute number of lymphocytes and neutrophils (L, N) and the N/L thereof. A WBC variable was considered a predictor for overall survival and recurrence (distant/local/nodal/regional) if p≤0.006 (corrected for 9 variables) from Cox regression and competing risk analyses, respectively; both conducted using bootstrap resampling. Finally, a WBC variable predicting any of the outcomes was linearly associated with each of eleven disease/patient/treatment characteristics (p≤0.005; corrected for 11 characteristics). RESULTS: At the three post-RT time points both L and N significantly decreased (p

Background and purpose: Minimizing acute esophagitis (AE) in locally advanced non-small cell lung cancer (LA-NSCLC) is critical given the proximity between the esophagus and the tumor. In this pilot study, we developed a clinical platform for quantification of accumulated doses and volumetric changes of esophagus via weekly Magnetic Resonance Imaging (MRI) for adaptive radiotherapy (RT). Material and methods: Eleven patients treated via intensity-modulated RT to 60–70 Gy in 2–3 Gy-fractions with concurrent chemotherapy underwent weekly MRIs. Eight patients developed AE grade 2 (AE2), 3–6 weeks after RT started. First, weekly MRI esophagus contours were rigidly propagated to planning CT and the distances between the medial esophageal axes were calculated as positional uncertainties. Then, the weekly MRI were deformably registered to the planning CT and the total dose delivered to esophagus was accumulated. Weekly Maximum Esophagus Expansion (MEex) was calculated using the Jacobian map. Eventually, esophageal dose parameters (Mean Esophagus Dose (MED), V90% and D5cc) between the planned and accumulated dose were compared. Results: Positional esophagus uncertainties were 6.8 ± 1.8 mm across patients. For the entire cohort at the end of RT: the median accumulated MED was significantly higher than the planned dose (24 Gy vs. 21 Gy p = 0.006). The median V90% and D5cc were 12.5 cm3 vs. 11.5 cm3 (p = 0.05) and 61 Gy vs. 60 Gy (p = 0.01), for accumulated and planned dose, respectively. The median MEex was 24% and was significantly associated with AE2 (p = 0.008). Conclusions: MRI is well suited for tracking esophagus volumetric changes and accumulating doses. Longitudinal esophagus expansion could reflect radiation-induced inflammation that may link to AE. Keywords: MRI, Esophagus, Adaptive radiotherapy, Lung cancer, Dose accumulation

PURPOSE: To identify published normal tissue complication probability (NTCP) models suitable for patient-specific dose-prescription in locally advanced non-small cell lung cancer (LA-NSCLC) through in-house validation. MATERIAL AND METHODS: From eight previously published candidate NTCP models (≥grade 2 acute esophagitis and radiation pneumonitis; AE2, RP2), patient-specific dose-responses were calculated using model variables and fractionation-corrected doses for 241 LA-NSCLC patients treated with chemo-IMRT to 50-80Gy@1.8-2.0Gy between 2004 and 2014 (AE2/RP2 rate: 50%/12%). A model was judged final if it significantly predicted AE2 or RP2 (p≤0.05), was discriminative and well calibrated (AUC>0.60; Hosmer-Lemeshow test p(HL)>0.05), which were assessed as the median over 1000 bootstrap samples. RESULTS: Models for AE2 had superior discrimination to RP2 models (AUC=0.63–0.65 vs. 0.51–0.65). The final AE2 model included mean esophageal dose and concurrent chemotherapy (AUC=0.65; p

Purpose/Objective(s) Pulmonary toxicity attributed to the proximal bronchial tree (PBT) is dose-limiting in stereotactic body radiation therapy (SBRT) for central lung tumors. The toxicity is of particular concern in ultracentral lesions, which are defined as the gross tumor volume abutting the PBT, esophagus, or other mediastinal structures. In this work we tested published pulmonary toxicity models associated with dose to the airway structures after SBRT for ultracentral tumors in an independent institutional cohort of patients. Materials/Methods The PubMed database was scrutinized for toxicity models evaluating pulmonary and airway toxicity for SBRT or hypofractionated radiation treatment for central tumors in the lung. The following search term criteria were used: (central) AND (lung tumor) AND (normal tissue complication probability) AND (radiation). Identified models were explored in all patients with ultracentral lung tumors treated between 2008 and 2017 at one large center (N = 88). This cohort included 60% primary lung and 40% metastatic patients that had been treated to 45 Gy in 5 fractions (fx), 50 Gy in 5 fx, 60 Gy in 8 fx, or 60 Gy in 15 fx. 16% of the patients received anti-VEGF therapy, which as previously shown, was associated with higher incidence of grade 5 hemorrhage. Results A total of six NTCP models were identified from work by Tekatli et al 2018. Of these, three models were for ≥grade 3 and three for grade 5 toxicity and included bronchial max dose, V60, V100, and V130. Clinical pulmonary toxicity consisted of radiation pneumonitis, atelectasis due to main stem bronchus occlusion, and/or hemoptysis. The pulmonary toxicity rate was 15% and 8% for ≥grade 3 toxicity and grade 5 toxicity, respectively, in the original study. Within the independent cohort, the corresponding values were 18% and 10%. Two maximum dose models were shown to be valid in our institutional dataset; other models failed this validation test. The model was made more robust by expanding the max dose region of the bronchial volume by focusing on the max dose to 0.1 cm3, 1 cm3 and 3 cm3. The max dose (0.1 cm3) model had improved performance with increased steepness. Generally, models from literature underestimated our institutional complication rates. Conclusion Pulmonary toxicity models for central tumors were identified from one study and explored within an independent cohort. The models derived from primarily central lung tumors systematically underestimated the pulmonary toxicity rate in the solely ultracentral tumor cohort, possibly also due to anti-VEGF therapy used in the independent cohort. A modified model from the best-performing max dose model was designed, and this model could be utilized for rationally constructed airways constraints in ultracentral patients treated with SBRT or hypofractionation. Author Disclosure I. Chen: None. M. Thor: None. A.J. Wu: Research Grant; CivaTech Oncology, Inc. Honoraria; 1199SEIU. Consultant; AstraZeneca, MORE Health. Advisory Board; Simphotek, Inc. Travel Expenses; AlphaTau Medical. Stock; Simphotek, Inc. A. Iyer: None. A. Apte: None. A. Rimner: Research Grant; Varian Medical Systems, Boehringer Ingelheim, Astra Zeneca, Pfizer, Merck. Consultant; MoreHealth. Advisory Board; Boehringer Ingelheim, Astra Zeneca, Merck; International Thymic Malignancies Interest Group, International Mesothelioma Interest Group. D.R. Gomez: Research Grant; Merck, AstraZeneca. Honoraria; BMS, Varian, Research to Practice. Speaker's Bureau; Merck. Advisory Board; AstraZeneca, Olympus. J.O. Deasy: Research Grant; Varian Corp., NIH, Breast Cancer Research Foundation. Stock; Paige.AI; AAPM.A. Jackson: None.

Purpose Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses were higher than reported due to inconsistent and insufficient heart segmentation. We tested our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts. Methods and Materials The RTOG 0617 data were downloaded from The Cancer Imaging Archive; the 442 patients with trial hearts and dose distributions were included. All hearts were resegmented using our DL pipeline and quality assured to meet the requirements for clinical implementation. Dose (V5%, V30%, and mean heart dose) was compared between the 2 sets of hearts (Wilcoxon signed-rank test). Each dose metric was associated with overall survival (Cox proportional hazards). Lastly, 18 volume similarity metrics were assessed for the hearts and correlated with |DoseDL – DoseRTOG0617| (linear regression; significance: P ≤ .0028; corrected for 18 tests). Results Dose metrics were significantly higher for DL hearts compared with trial hearts (eg, mean heart dose: 15 Gy vs 12 Gy; P = 5.8E-16). All 3 DL heart dose metrics were stronger overall survival predictors than those of the trial hearts (median, P = 2.8E-5 vs 2.0E-4). Thirteen similarity metrics explained |DoseDL – DoseRTOG0617|; the axial distance between the 2 centers of mass was the strongest predictor (CENTAxial; median, R2 = 0.47; P = 6.1E-62). CENTAxial agreed with the qualitatively identified inconsistencies in the superior direction. The trial’s qualitative heart contouring score was not correlated with |DoseDL – DoseRTOG0617| (median, R2 = 0.01; P = .02) or with any of the similarity metrics (median, Rs = 0.13 [range, –0.22 to 0.31]). Conclusions Using a coherent heart definition, as enabled through our open-source DL algorithm, the trial heart doses in RTOG 0617 were found to be significantly higher than previously reported, which may have led to an even more rapid mortality accumulation. Auto-segmentation is likely to reduce contouring and dose inconsistencies and increase the quality of clinical RT trials.

Purpose: To determine the incidence and predictors of gastric bleeding after chemoradiation for esophageal or gastroesophageal junction cancer. Methods and Materials: We reviewed patients receiving chemoradiation to at least 41.4 Gy for localized esophageal cancer whose fields included the stomach and who did not undergo surgical resection. The primary endpoint was grade ≥3 gastric hemorrhage (GB3+). Comprehensive stomach dose-volume parameters were collected, and stomach dose-volume histograms were generated for analysis. Results: A total of 145 patients met our inclusion criteria. Median prescribed dose was 50.4 Gy (range, 41.4-56 Gy). Median stomach Dmax was 53.0 Gy (1.0-62.7 Gy), and median stomach V40, V45, and V50 Gy were 112 cm3 (0-667 cm3), 84 cm3 (0-632 cm3), and 50 cm3 (0-565 cm3), respectively. Two patients (1.4%) developed radiation-induced GB3+. The only dosimetric factor that was significantly different for these patients was a higher stomach Dmax (58.1 and 58.3 Gy) than the cohort median (53 Gy). One of these patients also had cirrhosis, and the other had a history of nonsteroidal anti-inflammatory drug use. Five other patients had GB3+ events associated with documented tumor progression. A Cox proportional hazards model based on stomach Dmax with respect to the development of GB3+ was found to be statistically significant. Time-to-event curves and dose-volume atlases were generated, demonstrating an increased risk of GB3+ only when stomach Dmax was >58 Gy (P < .05). Conclusions: We observed a low rate of GB3+ events in patients who received chemoradiation to a median dose of 50.4 Gy to volumes that included a significant portion of the stomach. These results suggest that when prescribing 50.4 Gy for esophageal cancer, there is no need to minimize the irradiated gastric volume or dose for the sake of preventing bleeding complications. Limiting stomach maximum doses to

Purpose Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change. Methods and Materials Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1–to–week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL). Results Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75). Conclusions A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.

Public preregistration of statistical analysis plans (SAPs) is widely recognized for clinical trials, but adopted to a much lesser extent in observational studies. Registration of SAPs prior to analysis is encouraged to not only increase transparency and exactness but also to avoid positive finding bias and better standardize outcome modeling. Efforts to generally standardize outcome modeling, which can be based on clinical trial and/or observational data, have recently spurred. We suggest a three-step SAP concept in which investigators are encouraged to 1.) Design the SAP and circulate it among the co-investigators, 2.) Log the SAP with a public repository, which recognizes the SAP with a digital object identifier (DOI), and 3.) Cite (using the DOI), briefly summarize and motivate any deviations from the SAP in the associated manuscript. More specifically, the SAP should include the scope (brief data and study description, co-investigators, hypotheses, primary outcome measure, study title), in addition to step-by-step details of the analysis (handling of missing data, resampling, defined significance level, statistical function, validation, and variables and parametrization).

Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process. Approach. CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021. Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85–0.89) for the masseters, 0.80 (0.79–0.81) for the medial pterygoids, 0.81 (0.79–0.84) for the larynx, and 0.69 (0.67–0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. Significance. We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.

Purpose: This study investigated whether Magnetic Resonance image biomarkers (MR-IBMs) were associated with xerostomia 12 months after radiotherapy (Xer(12m)) and to test the hypothesis that the ratio of fat-to-functional parotid tissue is related to Xer(12m). Additionally, improvement of the reference Xer(12m) model based on parotid gland dose and baseline xerostomia, with MR-IBMs was explored.Methods: Parotid gland MR-IBMs of 68 head and neck cancer patients were extracted from pre-treatment T1-weighted MR images, which were normalized to fat tissue, quantifying 21 intensity and 43 texture image characteristics. The performance of the resulting multivariable logistic regression models after bootstrapped forward selection was compared with that of the logistic regression reference model. Validity was tested in a small external cohort of 25 head and neck cancer patients.Results: High intensity MR-IBM P90 (the 90th intensity percentile) values were significantly associated with a higher risk of Xer(12m). High P90 values were related to high fat concentration in the parotid glands. The MR-IBM P90 significantly improved model performance in predicting Xer(12m) (likelihood-ratio-test; p = 0.002), with an increase in internally validated AUC from 0.78 (reference model) to 0.83 (P90). The MR-IBM P90 model also outperformed the reference model (AUC = 0.65) on the external validation cohort (AUC = 0.83).Conclusion: Pre-treatment MR-IBMs were associated to radiation-induced xerostomia, which supported the hypothesis that the amount of predisposed fat within the parotid glands is associated with Xer(12m). In addition, xerostomia prediction was improved with MR-IBMs compared to the reference model. (C) 2018 The Authors. Published by Elsevier B.V.

Purpose/Objective(s) Analyses of locally-advanced non-small cell lung cancer (LA-NSCLC) series have identified radiation dose to the cardio-pulmonary system as a new key detriment for risk of death. However, spatial dose patterns are typically represented as one-dimensional dose metrics of pre-defined segmented structures. Here we introduce the use of a recently developed non-parametric approach based on an extended form of optimal mass transport (OMT), called unbalanced OMT distance. Thoracic dose distributions were ranked in relation to risk of death within RTOG 0617. This agnostic approach preserves the spatial dose information without pre-selection of specific thoracic sub-regions or normalization. Materials/Methods The 409 patients with evaluable data (CTs, dose, structures and overall survival status) were retrieved from The Cancer Imaging Archive. Patients were censored at 60 months. Data was split into a 70% training and a 30% validation data (n = 286 and 123). Dose distributions were deformably registered to randomly selected reference patients using multi-step B spline. The unbalanced OMT distance measures dose spread pattern and total amount of dose differences among patients. Using a newly proposed approximation of the unbalanced OMT distance via vector-valued OMT, the highly complicated distances between pairs of dose distributions were computed efficiently. The OMT distances were translated into risk scores represented by C-indices: small distances (low C-indices) represent high-risk; large distances (high C-indices) represent low-risk. Risk status in the validation data was measured as distances to the training reference patients. Lastly, two sub-regions were deduced: One high-risk region based on the 50 riskiest patients (overlap of at least 80% of distributions including the highest 80% dose relative to max dose) and one low-risk region based on the 50 least risky patients. The locations of the sub-regions were qualitatively compared to those of eight deep learning auto-segmented cardio-pulmonary substructures (aorta, atria, inferior and superior vena cava (SVC), pulmonary artery (PA), ventricles). Results The highest risk of death was identified in a region overlapping with the base of the heart, including the ascending aorta, PA and the superior part of the left atrium. In contrast, the ‘safe’ distributions associated with the lowest risk of death mostly avoided all cardio-pulmonary substructures with the exception of the SVC and right PA. Conclusion This non-parametric approach identified specific high-risk and low-risk regions associated with death in RTOG 0617. While there was no one-to-one correspondence between the identified high-risk region and the segmented cardio-pulmonary structures, this region focused on the base of the heart. The identified regions may direct further work on elucidating mechanisms and generalizability.

Simple Summary Dyspnea is a common symptomatic side-effect of thoracic radiation therapy. The aim of this study is to build a predictive model of any-grade radiation-induced dyspnea within six months after stereotactic body radiation therapy in patients treated for non-small cell lung cancer. The occurrence of pre-treatment chronic obstructive pulmonary disease and higher relative lungs volume receiving more than 15 Gy as well as heart volume were shown to be risk factors for dyspnea. The obtained results encourage further studies on the topic, which could validate the present organ-based findings and explore the voxel-based landscape of radiation dose sensitivity in the development of dyspnea. Abstract In this study, we investigated the prognostic factors for radiation-induced dyspnea after hypo-fractionated radiation therapy (RT) in 106 patients treated with Stereotactic Body RT for Non-Small-Cell Lung Cancer (NSCLC). The median prescription dose was 50 Gy (range: 40–54 Gy), delivered in a median of four fractions (range: 3–12). Dyspnea within six months after SBRT was scored according to CTCAE v.4.0. Biologically Effective Dose (α/β = 3 Gy) volume histograms for lungs and heart were extracted. Dosimetric parameters along with patient-specific and treatment-related factors were analyzed, multivariable logistic regression method with Leave-One-Out (LOO) internal validation applied. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot parameters. Fifty-seven patients (53.8%) out of 106 developed dyspnea of any grade after SBRT (25/57 grade ≥ 2 cases). A three-variable predictive model including patient comorbidity (COPD), heart volume and the relative lungs volume receiving more than 15 Gy was selected. The model displays an encouraging performance given by a training ROC-AUC = 0.71 [95%CI 0.61–0.80] and a LOO-ROC-AUC = 0.64 [95%CI 0.53–0.74]. Further modeling efforts are needed for dyspnea prediction in hypo-fractionated treatments in order to identify patients at high risk for developing lung toxicity more accurately.

Objectives To identify temporal patterns of patient-reported trismus during the first year post-radiotherapy, and to study their associations with maximal interincisal opening distances (MIOs). Design Single institution case series. Setting University hospital ENT clinic. Participants One hundred ninety six subjects who received radiotherapy (RT) for head and neck cancer (HNC) with or without chemotherapy in 2007-2012 to total dose of 64.6/68 Gy in 38/34 fractions, respectively. All subjects were prospectively assessed for mouth-opening ability (Gothenburg Trismus Questionnaire (GTQ), and MIO) pre-RT and at 3, 6, and 12 months after RT. Main outcome measures Correlations between temporally robust GTQ-symptoms and MIO as given by Pearson's correlation coefficients (Pr); temporally robust GTQ-symptom domains as given by factor analysis; rates of trismus with respect to baseline by risk ratios (RRs). Results Four temporally robust domains were identified: Eating (3-7 symptoms), Jaw (3-7), Pain (2-5), and Quality of Life (QoL, 2-5), and included 2-3 persistent symptoms across all post- RT assessments. The median RR for a moderate/severe (>2/>3) cut-off was the highest for Jaw (3.7/3.6) and QoL (3.2/2.9). The median Pr between temporally robust symptoms and MIO post-radiotherapy was 0.25-0.35/0.34-0.43/0.24-0.31/0.34-0.50 for Eating/Jaw/Pain/QoL, respectively. Conclusions Mouth-opening distances in HNC patients post-RT can be understood in terms of associated patient-reported outcomes on trismus-related difficulties. Our data suggest that a reduction in MIO can be expected as patients communicate their mouth-opening status to interfere with private/social life, a clinical warning signal for emerging or worsening trismus as patients are being followed after RT. This article is protected by copyright. All rights reserved.

BACKGROUND: Inter-fractional variation in urinary bladder volumes during the course of radiotherapy (RT) for prostate cancer causes deviations between planned and delivered doses. This study compared planned vs. daily cone-beam CT (CBCT)-based spatial bladder dose distributions, for prostate cancer patients receiving local prostate treatment (local treatment) vs. prostate including pelvic lymph node irradiation (pelvic treatment). MATERIAL AND METHODS: Twenty-seven patients (N=15 local treatment; N=12 pelvic treatment) were treated using daily image-guided RT (1.8 Gy@43–45 fx), adhering to a full bladder/empty rectum protocol. For each patient, 9–10 CBCTs were registered to the planning CT, using the clinically applied translations. The urinary bladder was manually segmented on each CBCT, 3mm inner shells were generated, and semi and quadrant sectors were created using axial/coronal cuts. Planned and delivered DVH metrics were compared across patients and between the two groups of treatment (t-test, p

Background and purpose: In this study we investigated the ability of textures from T1-weighted MRI scans post-contrast (T1wpost) to identify the critical muscle(s) for radiation-induced trismus. Materials and methods: The study included ten cases (Trismus: ≥Grade 1), and ten age-sex-tumor-location-and-stage-matched controls treated with intensity-modulated radiotherapy to 70 Gy@2.12 Gy in 2005–2009. Trismus status and T1wPost were conducted within one year post-radiotherapy. For the masseter, lateral and medial pterygoids, and temporalis (M/LP/MP/T), 24 textures were extracted (Grey Level Co-Occurrence (GLCM), Histogram, and Shape). Univariate logistic regression with Bootstrapping (1000 populations) was applied to compare the muscle mean dose (Dmean) and textures between cases and controls (ipsilateral muscles); candidate predictors were suggested by an average p ≤ 0.20 across all Bootstrap populations. Results: Dmean to M/LP/MP (p = 0.03/0.14/0.09), one MP/T (p = 0.12/0.17), and three M (p = 0.14–0.19) textures were candidate predictors. Three of these textures were GLCM- and two Histogram textures with the former being generally higher and the latter lower for cases compared to controls. The Dmean to M and MP, and Haralick Correlation (GLCM) of MP presented with the best discriminative ability (area under the receiver-operating characteristic curve: 0.85, 0.77, and 0.78), and the correlation between Dmean and this texture was weak (Spearman’s rank correlation coefficient: 0.26–0.27). Conclusions: Our exploratory study points towards an interplay between the dose to the masseter, and the medial pterygoid together with the local relationship between the mean MRI intensity relative to its variance of the medial pterygoid for radiation-induced trismus. This opens up for exploration of this interplay within the radiation-induced trismus etiology in the larger multi-institutional setting. Keywords: Head and neck cancer, Radiotherapy, Trismus, Quantitative image, Texture, Radiomics

An open-source library of implementations for deep-learning based image segmentation and radiotherapy outcomes models is presented in this work. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. The library was developed with the Computational Environment for Radiological Research (CERR) software platform. Centralizing model implementations in CERR builds upon its rich set of radiotherapy and radiomics tools and caters to the world-wide user base. CERR provides well-validated feature extraction pipelines for radiotherapy dosimetry and radiomics with fine control over the calculation settings, allowing users to select the appropriate parameters used in model derivation. Models for automatic image segmentation are distributed via containers, with seamless i/o to and from CERR. Container technology allow segmentation models to be deployed with a variety of scientific computing architectures. The library includes implementations of popular radiotherapy models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from the Image Biomarker Standardization Initiative and application-specific features found to be relevant across multiple sites and image modalities. Deep learning-based image segmentation models include state-of-the-art networks such as DeepLab and other problem-specific architectures. The source code is distributed at https://www.github.com/cerr/CERR under the GNU-GPL copyleft with additional restrictions on clinical and commercial use and provision to dual license in future.

An open-source library of implementations for deep-learning based image segmentation and outcomes models is presented in this work. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. The library was developed with Computational Environment for Radiological Research (CERR) software platform. CERR is a natural choice to centralize model implementations due to its comprehensiveness, popularity, and ease of use. CERR provides well-validated feature extraction for radiotherapy dosimetry and radiomics with fine control over the calculation settings. This allows users to select the appropriate feature calculation used in the model derivation. Models for automatic image segmentation are distributed via Singularity containers, with seamless i/o to and from CERR. Singularity containers allow for segmentation models to be deployed with a variety of scientific computing architectures. Deployment of models is driven by JSON configuration file, making it convenient to plug-in models. Models from the library can be called programmatically for batch evaluation. The library includes implementations for popular radiotherapy models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from Image Biomarker Standardization features found to be important across multiple sites and image modalities. Deep learning-based image segmentation models include state of the art networks such as Deeplab and other problem-specific architectures. The library is distributed as GNU-copyrighted software at https://www.github.com/cerr/CERR.