Proportional Hazards Violations in Phase 3 Cancer Clinical Trials: A Potential Source of Trial Misinterpretation.

BACKGROUND: Survival analyses of novel agents with long-term responders often exhibit differential hazard rates over time. Such proportional hazards violations (PHVs) may reduce the power of the log-rank test and lead to misinterpretation of trial results. We aimed to characterize the incidence and study attributes associated with PHVs in phase 3 oncology trials and assess the utility of restricted mean survival time (RMST) and MaxCombo as additional analyses. METHODS: Clinicaltrials.gov and PubMed were searched to identify 2-arm, randomized, phase 3 superiority-design cancer trials with time-to-event primary endpoints and published results through 2020. Patient-level data were reconstructed from published Kaplan-Meier curves. PHVs were assessed using Schoenfeld residuals. RESULTS: Three hundred fifty-seven Kaplan-Meier comparisons across 341 trials were analyzed, encompassing 292,831 enrolled patients. PHVs were identified in 85/357 (23.8%; 95%CI 19.7%, 28.5%) comparisons. In multivariable analysis, non-OS endpoints (odds ratio [OR] 2.16 [95%CI 1.21, 3.87]; P=.009) were associated with higher odds of PHVs, and immunotherapy comparisons (OR 1.94 [95%CI 0.98, 3.86]; P=.058) were weakly suggestive of higher odds of PHVs. Few trials with PHVs (25/85, 29.4%) pre-specified a statistical plan to account for PHVs. Fourteen trials with PHVs exhibited discordant statistical signals with RMST or MaxCombo, of which ten (71%) reported negative results. CONCLUSION: PHVs are common across therapy types, and attempts to account for PHVs in statistical design are lacking despite the potential for results exhibiting non-proportional hazards to be misinterpreted.

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.

BACKGROUND/OBJECTIVES: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer. METHODS: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer", "Pain", "Pain Management", "Analgesics", "Artificial Intelligence", "Machine Learning", and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines. RESULTS: Forty four studies from 2006-2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%). CONCLUSION: Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

A prospective cohort study on stereotactic radiotherapy in the management of dural recurrence of olfactory neuroblastoma.

BACKGROUND: Treatment for dural recurrence of olfactory neuroblastoma (ONB) is not standardized. We assess the outcomes of stereotactic body radiotherapy (SBRT) in this population. METHODS: ONB patients with dural recurrences treated between 2013 and 2022 on a prospective registry were included. Tumor control, survival, and patient-reported quality of life were analyzed. RESULTS: Fourteen patients with 32 dural lesions were evaluated. Time to dural recurrence was 58.3 months. Thirty lesions (94%) were treated with SBRT to a median dose of 27 Gy in three fractions. Two patients (3 of 32 lesions; 9%) developed in-field radiographic progression, five patients (38%) experienced progression in non-contiguous dura. Two-year local control was 85% (95% CI: 51-96%). There were no >grade 3 acute toxicities and 1 case of late grade 3 brain radionecrosis. CONCLUSION: In this largest study of SBRT reirradiation for ONB dural recurrence to date, high local control rates with minimal toxicity were attainable.

A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context.

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

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

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

Radiotherapy is used as a treatment for people with oropharyngeal cancer. It is important to distinguish the areas where cancer is present so the radiotherapy treatment can be targeted at the cancer. Computational methods based on artificial intelligence can automate this task but need to be able to distinguish areas where it is unclear whether cancer is present. In this study we compare these computational methods that are able to highlight areas where it is unclear whether or not cancer is present. Our approach accurately predicts how well these areas are distinguished by the models. Our results could be applied to improve the computational methods used during radiotherapy treatment. This could enable more targeted treatment to be used in the future, which could result in better outcomes for people with oropharyngeal cancer.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed. PURPOSE: To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data. METHODS: Two datasets were included for this study: Dataset I: 524 (training) and 359 (testing) oropharyngeal cancer patients from different institutions with their PET/CT pairs provided by the HECKTOR Challenge; Dataset II: 90 HN patients(testing) from a local institution with their planning CT, PET/CT pairs. To handle potentially missing PET images, a model training strategy named the "Blank Channel" method was implemented. To simulate the absence of a PET image, a blank array with the same dimensions as the CT image was generated to meet the dual-channel input requirement of the deep learning model. During the model training process, the model was randomly presented with either a real PET/CT pair or a blank/CT pair. This allowed the model to learn the relationship between the CT image and the corresponding GTV delineation based on available modalities. As a result, our model had the ability to handle flexible inputs during prediction, making it suitable for cases where PET images are missing. To evaluate the performance of our proposed model, we trained it using training patients from Dataset I and tested it with Dataset II. We compared our model (Model 1) with two other models which were trained for specific modality segmentations: Model 2 trained with only CT images, and Model 3 trained with real PET/CT pairs. The performance of the models was evaluated using quantitative metrics, including Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff Distance (HD95). In addition, we evaluated our Model 1 and Model 3 using the 359 test cases in Dataset I. RESULTS: Our proposed model(Model 1) achieved promising results for GTV auto-segmentation using PET/CT images, with the flexibility of missing PET images. Specifically, when assessed with only CT images in Dataset II, Model 1 achieved DSC of 0.56 ± 0.16, MSD of 3.4 ± 2.1 mm, and HD95 of 13.9 ± 7.6 mm. When the PET images were included, the performance of our model was improved to DSC of 0.62 ± 0.14, MSD of 2.8 ± 1.7 mm, and HD95 of 10.5 ± 6.5 mm. These results are comparable to those achieved by Model 2 and Model 3, illustrating Model 1's effectiveness in utilizing flexible input modalities. Further analysis using the test dataset from Dataset I showed that Model 1 achieved an average DSC of 0.77, surpassing the overall average DSC of 0.72 among all participants in the HECKTOR Challenge. CONCLUSIONS: We successfully refined a multi-modal segmentation tool for accurate GTV delineation for HN cancer. Our method addressed the issue of missing PET images by allowing flexible data input, thereby providing a practical solution for clinical settings where access to PET imaging may be limited.

Combination Chemotherapy Optimization with Discrete Dosing.

Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens; one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its potential for clinical applications through a numerical study of breast cancer.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

First-in-Human Hyperpolarized MRI for Tumor Metabolism in HNSCC.

This case report describes the use of hyperpolarized magnetic resonance imaging (MRI) in a patient with head and neck squamous cell carcinoma (HNSCC) to demonstrate its translational viability.

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.

Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. Methods: We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. Results: We identified 56 articles published from 2015-2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50%), followed by image-synthesis (13%), and multiple applications simultaneously (11%). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32%). Imaging data was used in 91% of studies, while only 13% incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60%), with Monte Carlo dropout being the most commonly implemented UQ method (32%) followed by ensembling (16%). 55% of studies did not share code or datasets. Conclusion: Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.

Development and Standardization of an Osteoradionecrosis Classification System in Head and Neck Cancer: Implementation of a Risk-Based Model.

PURPOSE: Osteoradionecrosis of the jaw (ORN) can manifest in varying severity. The aim of this study is to identify ORN risk factors and develop a novel classification to depict the severity of ORN. METHODS: Consecutive patients with head and neck cancer (HNC) treated with curative-intent intensity-modulated radiation therapy (IMRT) (≥45 Gy) from 2011 to 2017 were included. Occurrence of ORN was identified from in-house prospective dental and clinical databases and charts. Multivariable logistic regression model was used to identify risk factors and stratify patients into high-risk and low-risk groups. A novel ORN classification system was developed to depict ORN severity by modifying existing systems and incorporating expert opinion. The performance of the novel system was compared with 15 existing systems for their ability to identify and predict serious ORN event (jaw fracture or requiring jaw resection). RESULTS: ORN was identified in 219 of 2,732 (8%) consecutive patients with HNC. Factors associated with high risk of ORN were oral cavity or oropharyngeal primaries, received IMRT dose ≥60 Gy, current/ex-smokers, and/or stage III to IV periodontal condition. The ORN rate for high-risk versus low-risk patients was 12.7% versus 3.1% (P < .001) with an AUC of 0.71. Existing ORN systems overclassified serious ORN events and failed to recognize maxillary ORN. A novel ORN classification system, ClinRad, was proposed on the basis of vertical extent of bone necrosis and presence/absence of exposed bone/fistula. This system detected serious ORN events in 5.7% of patients and statistically outperformed existing systems. CONCLUSION: We identified risk factors for ORN and proposed a novel ORN classification system on the basis of vertical extent of bone necrosis and presence/absence of exposed bone/fistula. It outperformed existing systems in depicting the seriousness of ORN and may facilitate clinical care and clinical trials.

Dataset of weekly intra-treatment diffusion weighted imaging in head and neck cancer patients treated with MR-Linac.

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

Detection of Alteration in Carotid Artery Volumetry Using Standard-of-care Computed Tomography Surveillance Scans Following Unilateral Radiation Therapy for Early-stage Tonsillar Squamous Cell Carcinoma Survivors: A Cross-Sectional Internally-Matched Carotid Isodose Analysis.

Purpose: Radiation induced carotid artery disease (RICAD) is a major cause of morbidity and mortality among survivors of oropharyngeal cancer. This study leveraged standard-of-care CT scans to detect volumetric changes in the carotid arteries of patients receiving unilateral radiotherapy (RT) for early tonsillar cancer, and to determine dose-response relationship between RT and carotid volume changes, which could serve as an early imaging marker of RICAD. Methods and Materials: Disease-free cancer survivors (>3 months since therapy and age >18 years) treated with intensity modulated RT for early (T1-2, N0-2b) tonsillar cancer with pre- and post-therapy contrast-enhanced CT scans available were included. Patients treated with definitive surgery, bilateral RT, or additional RT before the post-RT CT scan were excluded. Pre- and post-treatment CTs were registered to the planning CT and dose grid. Isodose lines from treatment plans were projected onto both scans, facilitating the delineation of carotid artery subvolumes in 5 Gy increments (i.e. received 50-55 Gy, 55-60 Gy, etc.). The percent-change in sub-volumes across each dose range was statistically examined using the Wilcoxon rank-sum test. Results: Among 46 patients analyzed, 72% received RT alone, 24% induction chemotherapy followed by RT, and 4% concurrent chemoradiation. The median interval from RT completion to the latest, post-RT CT scan was 43 months (IQR 32-57). A decrease in the volume of the irradiated carotid artery was observed in 78% of patients, while there was a statistically significant difference in mean %-change (±SD) between the total irradiated and spared carotid volumes (7.0±9.0 vs. +3.5±7.2, respectively, p<.0001). However, no significant dose-response trend was observed in the carotid artery volume change withing 5 Gy ranges (mean %-changes (±SD) for the 50-55, 55-60, 60-65, and 65-70+ Gy ranges [irradiated minus spared]: -13.1±14.7, -9.8±14.9, -6.9±16.2, -11.7±11.1, respectively). Notably, two patients (4%) had a cerebrovascular accident (CVA), both occurring in patients with a greater decrease in carotid artery volume in the irradiated vs the spared side. Conclusions: Our data show that standard-of-care oncologic surveillance CT scans can effectively detect reductions in carotid volume following RT for oropharyngeal cancer. Changes were equivalent between studied dose ranges, denoting no further dose-response effect beyond 50 Gy. The clinical utility of carotid volume changes for risk stratification and CVA prediction warrants further evaluation.

Safety and Tolerability of Online Adaptive High-Field Magnetic Resonance-Guided Radiotherapy.

Importance: In 2018, the first online adaptive magnetic resonance (MR)-guided radiotherapy (MRgRT) system using a 1.5-T MR-equipped linear accelerator (1.5-T MR-Linac) was clinically introduced. This system enables online adaptive radiotherapy, in which the radiation plan is adapted to size and shape changes of targets at each treatment session based on daily MR-visualized anatomy. Objective: To evaluate safety, tolerability, and technical feasibility of treatment with a 1.5-T MR-Linac, specifically focusing on the subset of patients treated with an online adaptive strategy (ie, the adapt-to-shape [ATS] approach). Design, Setting, and Participants: This cohort study included adults with solid tumors treated with a 1.5-T MR-Linac enrolled in Multi Outcome Evaluation for Radiation Therapy Using the MR-Linac (MOMENTUM), a large prospective international study of MRgRT between February 2019 and October 2021. Included were adults with solid tumors treated with a 1.5-T MR-Linac. Data were collected in Canada, Denmark, The Netherlands, United Kingdom, and the US. Data were analyzed in August 2023. Exposure: All patients underwent MRgRT using a 1.5-T MR-Linac. Radiation prescriptions were consistent with institutional standards of care. Main Outcomes and Measures: Patterns of care, tolerability, and technical feasibility (ie, treatment completed as planned). Acute high-grade radiotherapy-related toxic effects (ie, grade 3 or higher toxic effects according to Common Terminology Criteria for Adverse Events version 5.0) occurring within the first 3 months after treatment delivery. Results: In total, 1793 treatment courses (1772 patients) were included (median patient age, 69 years [range, 22-91 years]; 1384 male [77.2%]). Among 41 different treatment sites, common sites were prostate (745 [41.6%]), metastatic lymph nodes (233 [13.0%]), and brain (189 [10.5%]). ATS was used in 1050 courses (58.6%). MRgRT was completed as planned in 1720 treatment courses (95.9%). Patient withdrawal caused 5 patients (0.3%) to discontinue treatment. The incidence of radiotherapy-related grade 3 toxic effects was 1.4% (95% CI, 0.9%-2.0%) in the entire cohort and 0.4% (95% CI, 0.1%-1.0%) in the subset of patients treated with ATS. There were no radiotherapy-related grade 4 or 5 toxic effects. Conclusions and Relevance: In this cohort study of patients treated on a 1.5-T MR-Linac, radiotherapy was safe and well tolerated. Online adaptation of the radiation plan at each treatment session to account for anatomic variations was associated with a low risk of acute grade 3 toxic effects.

Markov models for clinical decision-making in radiation oncology: A systematic review.

The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.

PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible. METHODS AND MATERIALS: The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients in this cohort (13.7%) developed ORN (grades I to IV). The (structural) clusters of mandibular dose-volume histograms (DVHs) for these patients were identified using the K-means clustering method. A soft-margin support vector machine was used to determine the cluster borders and partition the dose-volume space. The risk of ORN for each dose-volume region was calculated based on incidence rates and other clinical risk factors. RESULTS: The K-means clustering method identified 6 clusters among the DVHs. Based on the first 5 clusters, the dose-volume space was partitioned by the soft-margin support vector machine into distinct regions with different risk indices. The sixth cluster entirely overlapped with the others; the region of this cluster was determined by its envelopes. For each region, the ORN incidence rate per preradiation dental extraction status (a statistically significant, nondose related risk factor for ORN) was reported as the corresponding risk index. CONCLUSIONS: This study presents an unsupervised-learning analysis of a large-scale data set to evaluate the risk of mandibular ORN among patients with head and neck cancer. The results provide a visual risk-assessment tool for ORN (based on the whole DVH and preradiation dental extraction status) as well as a range of constraints for dose optimization under different risk levels.

Dyadic Yoga for Head and Neck Cancer Patients Undergoing Chemoradiation and their Family Caregivers.

OBJECTIVES: Concurrent chemoradiation to treat head and neck cancer (HNC) may result in debilitating toxicities. Targeted exercise such as yoga therapy may buffer against treatment-related sequelae; thus, this pilot RCT examined the feasibility and preliminary efficacy of a yoga intervention. Because family caregivers report low caregiving efficacy and elevated levels of distress, we included them in this trial as active study participants. METHODS: HNC patients and their caregivers were randomized to a 15-session dyadic yoga program or a waitlist control (WLC) group. Prior to randomization, patients completed standard symptom (MDASI-HN) and patients and caregivers completed quality of life (SF-36) assessments. The 15-session program was delivered parallel to patients' treatment schedules. Participants were re-assessed at patients' last day of chemoradiation and again 30 days later. Patients' emergency department visits, unplanned hospital admissions and gastric feeding tube placements were recorded over the treatment course and up to 30 days later. RESULTS: With a consent rate of 76%, 37 dyads were randomized. Participants in the yoga group completed a mean of 12.5 sessions and rated the program as "beneficial." Patients in the yoga group had clinically significantly less symptom interference and HNC symptom severity and better QOL than those in the WLC group. They were also less likely to have a hospital admission (OR = 3.00), emergency department visit (OR = 2.14), and/or a feeding tube placement (OR = 1.78). CONCLUSION: Yoga therapy appears to be a feasible, acceptable, and possibly efficacious behavioral supportive care strategy for HNC patients undergoing chemoradiation. A larger efficacy trial is warranted.

Longitudinal changes in the carotid arteries of head and neck cancer patients following radiation therapy: Results from a prospective serial imaging biomarker characterization study.

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