Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment.

Background And Purpose: Accurate delineation of orodental structures on radiotherapy computed tomography (CT) images is essential for dosimetric assessment and dental decisions. We propose a deep-learning (DL) auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned with the ClinRad osteoradionecrosis staging system.

Materials And Methods: Mandible and maxilla sub-volumes were manually defined on simulation CT images from 60 clinical cases, differentiating alveolar from basal regions; teeth were labelled individually.

Post head and neck cancer radiation therapy dynamic contrast enhanced magnetic resonance imaging association with high mandibular radiation dose.

Background: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a functional imaging modality that can quantify tissue permeability and blood flow. Due to vasculature changes resulting from radiation therapy (RT), DCE-MRI quantitative parameters should be significantly different in regions receiving a high radiation dose compared to regions receiving a low radiation dose. This work sought to determine if a significant difference exists in post head and neck cancer (HNC)-RT DCE-MRI quantitative parameters and v between regions of the mandible receiving a high radiation dose and regions of the mandible receiving a low radiation dose.

Feasibility of Longitudinal Relaxation Rate Mapping with Non-Cartesian Sampling and Compressed Sensing on a 1.5T MR-Linac.

Background: Quantitative mapping of the longitudinal relaxation rate (R1=1/T1) is a major building block for several multiparametric MRI protocols intended for adaptive radiation therapy planning. The implementation of these protocols is challenging in anatomical sites that experience large physiological motion.

Purpose: To implement and validate a motion-resolved quantitative T1 mapping method on a 1.

Optimizing Atlas Counts for MRI-Guided Atlas-Based Autosegmentation of Swallowing Muscles in Head and Neck Radiotherapy.

Purpose: Radiotherapy-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.

Technical development and In Silico implementation of SyntheticMR in head and neck adaptive radiation therapy: A prospective R-IDEAL stage 0/1 technology development report.

Background: SyntheticMR has the capability of generating quantitative relaxometry maps and synthetic contrast-weighted MRI images in rapid acquisition times. Recently, it has gained attention in the diagnostic community, however, no studies have investigated its feasibility on the MR-Simulation or MR-Linac systems, especially as part of the head and neck adaptive radiation oncology workflow.

Purpose: Demonstrating its feasibility will facilitate rapid quantitative biomarker extraction, which can be leveraged to guide adaptive radiation therapy decision making.

A method for sensitivity analysis of automatic contouring algorithms across different contrast weightings using synthetic magnetic resonance imaging.

Background And Purpose: A majority of institution-specific automatic magnetic resonance imaging (MRI)-based contouring algorithms utilize one contrast-weighting (i.e., T2-weighted), however their performance within this contrast-weighting (i.

Cost-effectiveness of personalized policies for implementing organ-at-risk sparing adaptive radiation therapy in head and neck cancer.

Background And Purpose: The principle of adaptive radiation therapy (ART) is to adjust radiation plans in response to anatomical changes during treatment. The purpose of this study was to develop a decision-making model for implementation of personalized ART that balances the costs and clinical benefits of radiation plan adaptations in head and neck cancer (HNC).

Materials And Methods: Using retrospective imaging data from 52 HNC patients, a Markov decision process (MDP) model was developed to determine optimal timing for plan adaptations based on the difference in normal tissue complication probability (ΔNTCP) between planned and delivered doses to organs-at-risk.

Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors.

Purpose: This study aims to identify radiomic features from contrast-enhanced CT (CECT) scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer (HNC) patients treated with radiotherapy (RT).

Materials And Methods: CECT images from 150 patients with confirmed ORN diagnosis (2008-2018) at MD Anderson Cancer Center (MDACC) were analyzed (80 % train, 20 % test). Radiomic features were extracted using PyRadiomics from manually segmented ORN regions and automated contralateral healthy mandible regions.

National Institutes of Health Funding to Support Radiation Oncology Research: A Comparative Trend Analysis Over a Decade, 2011-2021.

Purpose: Funding to support radiation oncology discovery and research is essential for advancement in therapeutic strategies to improve outcomes for patients with cancer. We aimed to comprehensively characterize trends in National Institutes of Health (NIH) funding that supports radiation oncology research over time to identify trends, successes, and areas for improvement.

Methods And Materials: We queried the NIH Research Portfolio Online Reporting Tools Expenditures and Results database to identify all awarded grants to support radiation oncology research conducted by principal investigators at academic centers, using 3 individual years as representative samples (2011, 2016, and 2021).

Radiographic classification of mandibular osteoradionecrosis: A blinded prospective multi-disciplinary interobserver diagnostic performance study.

Background & Purpose: Osteoradionecrosis (ORN) of the jaw is a severe complication affecting up to 15% of head and neck cancer patients treated with radiotherapy. The ClinRad system, endorsed by ASCO/ISOO/MASCC, incorporates radiographic features for ORN severity classification, but variability in imaging use and specialty expertise may impact diagnostic accuracy. This study benchmarks physician performance in diagnosing and staging ORN across specialties and imaging modalities.

Technical Optimization of SyntheticMR for the Head and Neck on a 3T MR-Simulator and 1.5T MR-Linac: A Prospective R-IDEAL Stage 2a Technology Innovation Report.

Objective: The purpose of this study was to optimize the technical tradeoffs associated with integrating the quantitative maps available from SyntheticMR into the head and neck adaptive radiation oncology workflow. Recent work has begun to investigate SyntheticMR in the adaptive radiation oncology workflow, however no studies have investigated the variation in acquisition parameters and their relationship to the resulting quantitative maps. Filling this gap will facilitate SyntheticMR's translation to the adaptive radiation therapy setting in the head and neck due to the reduced bias and increase repeatability and reproducibility of its generated quantitative relaxometric biomarkers.

Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients.

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023.

Depletion of Effector Regulatory T Cells Associates with Major Response to Induction Dual Immune Checkpoint Blockade.

Unlabelled: In a phase II trial, patients with local-regionally advanced human papillomavirus-positive oropharyngeal carcinoma (n = 35) received ipilimumab (anti-CTLA4) and nivolumab (anti-PD-1) as induction immunotherapy and concurrently with radiotherapy (NCT03799445). Coprimary endpoints included 6-month complete metabolic response rate (94%) and 2-year progression-free survival (84%). Induction yielded a 46% major histopathologic response rate.

Designing an Electronic Patient Reported Outcomes Information Infrastructure Supported by the RE-AIM Implementation Framework.

Objective: Patient-reported outcomes (PROs) contain valuable information that can be leveraged by providers to perform timely interventions and improve quality of life and survival. However, the implementation of electronic PROs (ePROs) remains a challenge from technical, behavioral, and evaluation perspectives. Our objective was to construct a robust electronic health record (EHR)-integrated ePRO information infrastructure founded on RE-AIM (Reach-Effectiveness-Adoption-Implementation-Maintenance) principles.

Externally validated digital decision support tool for time-to-osteoradionecrosis risk-stratification using right-censored multi-institutional observational cohorts.

Background: Existing studies on osteoradionecrosis of the jaw (ORNJ) have primarily used cross-sectional data, assessing risk factors at a single time point. Determining the time-to-event profile of ORNJ has important implications to monitor oral health in head and neck cancer (HNC) long-term survivors.

Methods: Data were retrospectively obtained for a clinical observational cohort of 1129 patients (198 ORNJ cases) with HNC treated with radiotherapy (RT) at The University of Texas MD Anderson Cancer Center.

Evaluating observer reliability and diagnostic accuracy of CT-LEFAT criteria for post-treatment head and neck lymphedema: A prospective blinded comparative analysis.

Background: Radiation-associated lymphedema and fibrosis (LEF) is a significant toxicity following radiation therapy (RT) for head and neck cancer (HNC) patients. Recently, the CT Lymphedema and Fibrosis Assessment Tool (CT-LEFAT) was developed to standardize LEF diagnosis through fat stranding visualized on CT. This study aims to evaluate the inter-observer reliability and diagnostic accuracy of the CT-LEFAT criteria.

Image-based Mandibular and Maxillary Parcellation and Annotation using Computer Tomography (IMPACT): A Deep Learning-based Clinical Tool for Orodental Dose Estimation and Osteoradionecrosis Assessment.

Background: Accurate delineation of orodental structures on radiotherapy CT images is essential for dosimetric assessments and dental decisions. We propose a deep-learning auto-segmentation framework for individual teeth and mandible/maxilla sub-volumes aligned with the ClinRad ORN staging system.

Methods: Mandible and maxilla sub-volumes were manually defined, differentiating between alveolar and basal regions, and teeth were labelled individually.

International multispecialty expert physician preoperative identification of extranodal extension in patients with oropharyngeal cancer using computed tomography: Prospective blinded human inter-observer performance evaluation.

Background: Pathologic extranodal extension (pENE) is a crucial prognostic factor in oropharyngeal cancer (OPC), but determining pENE from imaging has high inter-observer variability. The role of clinician specialty in the accuracy of imaging-detected extranodal extension (iENE) remains unclear. The purpose of this study is to assess the influence of clinician specialty on the accuracy of preoperative iENE detection in human papillomavirus (HPV)-positive OPC using computed tomography (CT) imaging.

Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints.

Background And Purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing of re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC).

Materials And Methods: A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities.

Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge.

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention.

PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients.

Patient-Reported Outcomes (PRO) consist of information provided directly by the patients about their health status including symptom ratings. PROs are commonly used in clinical practice to support clinical decision-making and have recently been incorporated into machine learning models to improve risk prediction. In this work, we aim to evaluate whether the inclusion of a patient stratification based on 12-month post-treatment predicted Patient Reported Outcomes improves risk prediction of radiation-induced toxicity and overall survival for head and neck cancer patients.