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.

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer.

Importance: Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use. Objective: To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes. Design, Setting, and Participants: For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women's Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023. Exposure: C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC. Main Outcomes and Measures: Overall survival and treatment toxicity outcomes of HNSCC. Results: The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P < .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia. Conclusions and Relevance: This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.

Purpose: Sarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes. Materials and Methods: 899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression. Results: DSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 - 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99, p < 0.0001) and test sets (r = 0.96, p < 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r ß 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis. Conclusion: We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC. SUMMARY STATEMENT: In this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.

Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy.

BACKGROUND/PURPOSE: Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images. MATERIALS/METHODS: We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the non-registered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance. RESULTS: 19 left submandibular glands, 18 right submandibular glands, 20 left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods took <30 s to execute per case, with the exception of Elastix 23 which took ∼458 s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p < 0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods. CONCLUSIONS: The ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy.

In vivo assessment of the safety of standard fractionation Temporally Feathered Radiation Therapy (TFRT) for head and neck squamous cell carcinoma: An R-IDEAL Stage 1/2a first-in-humans/feasibility demonstration of new technology implementation.

INTRODUCTION: Prior in silico simulations of studies of Temporally Feathered Radiation Therapy (TFRT) have demonstrated potential reduction in normal tissue toxicity. This R-IDEAL Stage 1/2A study seeks to demonstrate the first-in-human implementation of TFRT in treating patients with head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: Patients with HNSCC treated with definitive radiation therapy were eligible (70 Gy in 35 fractions) were eligible. The primary endpoint was feasibility of TFRT planning as defined by radiation start within 15 business days of CT simulation. Secondary endpoints included estimates of acute grade 3-5 toxicity. RESULTS: The study met its accrual goal of 5 patients. TFRT plans were generated in four of the five patients within 15 business days of CT simulation, therefore meeting the primary endpoint. One patient was not treated with TFRT at the physician's discretion, though the TFRT plan had been generated within sufficient time from the CT simulation. For patients who received TFRT, the median time from CT simulation to radiation start was 10 business days (range 8-15). The average time required for radiation planning was 6 business days. In all patients receiving TFRT, each subplan and every daily fraction was delivered in the correct sequence without error. The OARs feathered included: oral cavity, each submandibular gland, each parotid gland, supraglottis, and posterior pharyngeal wall (OAR pharynx). Prescription dose PTV coverage (>95%) was ensured in each TFRT subplan and the composite TFRT plan. One of five patients developed an acute grade 3 toxicity. CONCLUSIONS: This study demonstrates the first-in-human implementation of TFRT (R-IDEAL Stage 1), proving its feasibility in the modern clinical workflow. Additionally, assessments of acute toxicities and dosimetric comparisons to a standard radiotherapy plan were described (R-IDEAL Stage 2a).

Association of Medicaid Insurance With Survival Among Patients With Small Cell Lung Cancer.

Importance: Small cell lung cancer (SCLC) is an aggressive neoplasm requiring rapid access to subspecialized multidisciplinary care. For this reason, insurance coverage such as Medicaid may be associated with oncologic outcomes in this disproportionately economically vulnerable population. With Medicaid expansion under the Affordable Care Act, it is important to understand outcomes associated with Medicaid coverage among patients with SCLC. Objective: To determine the association of Medicaid coverage with survival compared with other insurance statuses. Design, Setting, and Participants: This cohort study included adult patients with limited-stage (LS) and extensive-stage (ES) SCLC in the US National Cancer Database from 2004 to 2013. Data were analyzed in January 2019. Main Outcomes and Measures: Patients were analyzed with respect to insurance status. Associations of insurance status with survival were interrogated with univariate analyses, multivariable analyses, and propensity score matching. Results: A total of 181 784 patients with SCLC (93 131 [51.2%] female; median [interquartile range] age; 67 [60-75] years for patients with LS-SCLC and 68 [60-75] years for patients with ES-SCLC) were identified, of whom 70 247 (38.6%) had LS-SCLC and 109 479 (60.2%) had ES-SCLC. On univariate analyses of patients with LS-SCLC, Medicaid coverage was not associated with a survival advantage compared with being uninsured (hazard ratio, 1.02; 95% CI, 0.96-1.08; P = .49). Likewise, on multivariable analyses of patients with ES-SCLC, compared with being uninsured, Medicaid coverage was not associated with a survival advantage (hazard ratio, 1.00; 95% CI, 0.96-1.03; P = .78). After propensity score matching, median survival was similar between the uninsured and Medicaid groups both among patients with LS-SCLC (14.4 vs 14.1 months; hazard ratio, 1.05; 95% CI, 0.98-1.12; P = .17) and those with ES-SCLC (6.3 vs 6.4 months; hazard ratio, 1.00; 95% CI, 0.96-1.04; P = .92). Conclusions and Relevance: Despite of billions of dollars in annual federal and state spending, Medicaid was not associated with improved survival in patients with SCLC compared with being uninsured in the US National Cancer Database. These findings suggest that there are substantial outcome inequalities for SCLC relevant to the policy debate on the Medicaid expansion under the Affordable Care Act.

Surveillance imaging for patients with head and neck cancer treated with definitive radiotherapy: A partially observed Markov decision process model.

BACKGROUND: A possible surveillance model for patients with head and neck cancer (HNC) who received definitive radiotherapy was created using a partially observed Markov decision process. The goal of this model is to guide surveillance imaging policies after definitive radiotherapy. METHODS: The partially observed Markov decision process model was formulated to determine the optimal times to scan patients. Transition probabilities were computed using a data set of 1508 patients with HNC who received definitive radiotherapy between the years 2000 and 2010. Kernel density estimation was used to smooth the sample distributions. The reward function was derived using cost estimates from the literature. Additional model parameters were estimated using either data from the literature or clinical expertise. RESULTS: When considering all forms of relapse, the model showed that the optimal time between scans was longer than the time intervals used in the institutional guidelines. The optimal policy dictates that there should be less time between surveillance scans immediately after treatment compared with years after treatment. Comparable results also held when only locoregional relapses were considered as relapse events in the model. Simulation results for the inclusive relapse cases showed that <15% of patients experienced a relapse over a simulated 36-month surveillance program. CONCLUSIONS: This model suggests that less frequent surveillance scan policies can maintain adequate information on relapse status for patients with HNC treated with radiotherapy. This model could potentially translate into a more cost-effective surveillance program for this group of patients.

Volumetric assessment of apparent diffusion coefficient predicts outcome following chemoradiation for cervical cancer.

OBJECTIVE: To determine the utility of volumetric diffusion weighted imaging (DWI) compared to other clinical factors for predicting recurrence and survival in cervical cancer patients treated with definitive chemoradiation. METHODS AND MATERIALS: We retrospectively studied cervical cancer patients treated with definitive chemoradiation between 2009-2013 at a single institution with a baseline MRI with DWI and 18F-FDG positron emission tomography/computed tomography (FDG-PET) scan. To identify clinical and imaging metrics correlated with survival and recurrence endpoints, variable importance values were calculated from random forest models. To provide clinically relevant threshold values, recursive partitioning analysis dichotomized patients into potential risk groups based on selected metrics. Cox's proportional hazard models assessed the effect of clinical and imaging factors on survival endpoints. RESULTS: Ninety-three patients were included in the analysis (median age 50 years). At a median follow-up of 35.6 months, 32 patients (34%) had disease recurrence. In the best multivariate model including clinical and imaging parameters, 90th percentile ADC < 1.917 was the only significantly associated factor with worse progression free survival (PFS). Overall survival, PFS, and distant metastasis free survival (DMFS) were significantly different between patient groups divided on 90th percentile ADC with threshold of 1.917 × 10-3 mm2/s and MRI volume with threshold of 18.9 cc (P = 0.037, P = 0.0002, P = 0.001). High MRI volume and low ADC were associated with worse clinical outcomes. CONCLUSIONS: Volumetric 90th percentile ADC value of the primary tumor on pretreatment MRI was a significant predictor of PFS and DMFS in cervical cancer patients, independent of established clinical factors and SUV on FDG-PET.

Industry Funding Is Correlated With Publication Productivity of US Academic Radiation Oncologists.

PURPOSE: Industry payments to physicians are financial conflicts of interest and may influence research findings and medical decisions. We aim to (1) characterize industry payments within radiation oncology; and (2) explore the potential correlation between receiving disclosed industry payments and academic productivity. MATERIALS/METHODS: CMS database was used to extract 2015 industry payments. For academic radiation oncologists, research productivity was characterized by h- and m-indices, as well as receipt of National Institutes of Health (NIH) funding, which is not an industry payment. Logistic regression models were used to determine whether publication metrics (m-index, h-index) and other study characteristics such as gender, PhD status, NIH institution funding status, were associated with the endpoints, research and general payments. Associations between the amount of payments (if any) and publication metrics were further studied using linear regression models. RESULTS: A total of 22,543 individual payments totaling $25,532,482 to 2,995 radiation oncologists were included. Among the 1,189 academic radiation oncologists, 75% received less than $167; on the other hand, 10 (<1%) individuals received $6,425,728 (51%) of payments. On multiple logistic regression, research payments were significantly associated with the m-index, odds ratio 2.86 (95% confidence interval, 1.84-4.45, p-value <0.0001); as well as with the h-index, odds ratio 1.03 (95% confidence interval, 1.01-1.05, p-value <0.0001). The linear regression model shows that both m-index and h-index were significantly positively associated with the amount of general payments (p-values <0.0001). CONCLUSION: There is an association between disclosed payment from the industry and increased individual research productivity metrics. Further research to find the cause behind this association is warranted.

Magnetic Resonance-based Response Assessment and Dose Adaptation in Human Papilloma Virus Positive Tumors of the Oropharynx treated with Radiotherapy (MR-ADAPTOR): An R-IDEAL stage 2a-2b/Bayesian phase II trial.

BACKGROUND: Current standard radiotherapy for oropharynx cancer (OPC) is associated with high rates of severe toxicities, shown to adversely impact patients' quality of life. Given excellent outcomes of human papilloma virus (HPV)-associated OPC and long-term survival of these typically young patients, treatment de-intensification aimed at improving survivorship while maintaining excellent disease control is now a central concern. The recent implementation of magnetic resonance image - guided radiotherapy (MRgRT) systems allows for individual tumor response assessment during treatment and offers possibility of personalized dose-reduction. In this 2-stage Bayesian phase II study, we propose to examine weekly radiotherapy dose-adaptation based on magnetic resonance imaging (MRI) evaluated tumor response. Individual patient's plan will be designed to optimize dose reduction to organs at risk and minimize locoregional failure probability based on serial MRI during RT. Our primary aim is to assess the non-inferiority of MRgRT dose adaptation for patients with low risk HPV-associated OPC compared to historical control, as measured by Bayesian posterior probability of locoregional control (LRC). METHODS: Patients with T1-2 N0-2b (as per AJCC 7th Edition) HPV-positive OPC, with lymph node <3 cm and <10 pack-year smoking history planned for curative radiotherapy alone to a dose of 70 Gy in 33 fractions will be eligible. All patients will undergo pre-treatment MRI and at least weekly intra-treatment MRI. Patients undergoing MRgRT will have weekly adaptation of high dose planning target volume based on gross tumor volume response. The stage 1 of this study will enroll 15 patients to MRgRT dose adaptation. If LRC at 6 months with MRgRT dose adaptation is found sufficiently safe as per the Bayesian model, stage 2 of the protocol will expand enrollment to an additional 60 patients, randomized to either MRgRT or standard IMRT. DISCUSSION: Multiple methods for safe treatment de-escalation in patients with HPV-positive OPC are currently being studied. By leveraging the ability of advanced MRI techniques to visualize tumor and soft tissues through the course of treatment, this protocol proposes a workflow for safe personalized radiation dose-reduction in good responders with radiosensitive tumors, while ensuring tumoricidal dose to more radioresistant tumors. MRgRT dose adaptation could translate in reduced long term radiation toxicities and improved survivorship while maintaining excellent LRC outcomes in favorable OPC. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT03224000; Registration date: 07/21/2017.

Predicting treatment Response based on Dual assessment of magnetic resonance Imaging kinetics and Circulating Tumor cells in patients with Head and Neck cancer (PREDICT-HN): matching 'liquid biopsy' and quantitative tumor modeling.

BACKGROUND: Magnetic resonance imaging (MRI) has improved capacity to visualize tumor and soft tissue involvement in head and neck cancers. Using advanced MRI, we can interrogate cell density using diffusion weighted imaging, a quantitative imaging that can be used during radiotherapy, when diffuse inflammatory reaction precludes PET imaging, and can assist with target delineation as well. Correlation of circulating tumor cells (CTCs) measurements with 3D quantitative tumor characterization could potentially allow selective, patient-specific response-adapted escalation or de-escalation of local therapy, and improve the therapeutic ratio, curing the greatest number of patients with the least toxicity. METHODS: The proposed study is designed as a prospective observational study and will collect pretreatment CT, MRI and PET/CT images, weekly serial MR imaging during RT and post treatment CT, MRI and PET/CT images. In addition, blood sample will be collected for biomarker analysis at those time intervals. CTC assessments will be performed on the CellSave tube using the FDA-approved CellSearch® Circulating Tumor Cell Kit (Janssen Diagnostics), and plasma from the EDTA blood samples will be collected, labeled with a de-identifying number, and stored at - 80 °C for future analyses. DISCUSSION: The primary objective of the study is to evaluate the prognostic value and correlation of weekly tumor response kinetics (gross tumor volume and MR signal changes) and circulating tumor cells of mucosal head and neck cancers during radiation therapy using MRI in predicting treatment response and clinical outcomes. This study will provide landmark information as to the utility of CTCs ('liquid biopsy) and tumor-specific functional quantitative imaging changes during treatment to guide personalization of treatment for future patients. Combining the biological information from CTCs and the structural information from MRI may provide more information than either modality alone. In addition, this study could potentially allow us to determine the optimal time to obtain MR imaging and/ or CTCs during radiotherapy to assess tumor response and provide guidance for patient selection and stratification for future dose escalation or de-escalation strategies. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT03491176 ). Date of registration: 9th April 2018. (retrospectively registered). Date of enrolment of the first participant: 30th May 2017.

Emerging Magnetic Resonance Imaging Technologies for Radiation Therapy Planning and Response Assessment.

Functional and molecular MRI techniques are capable of measuring biologic properties of tumor tissue. Knowledge of these biological properties may improve radiation treatment by more accurately identifying tumor volumes, characterizing radioresistant subvolumes of tumor before radiation therapy (RT), and identifying recurrent disease after RT. Intravoxel incoherent motion MRI, blood oxygenation level-dependent MRI, tissue oxygenation level-dependent MRI, hyperpolarized 13C MRI, and chemical exchange saturation transfer MRI are relatively new MRI techniques that have shown promise for contributing to RT planning and response assessment. This review critically evaluates these emerging MR techniques, their potential role in RT planning, utility to date, and challenges to integration into the current clinical workflow.

Three-dimensional imaging assessment of anatomic invasion and volumetric considerations for chemo/radiotherapy-based laryngeal preservation in T3 larynx cancer.

OBJECTIVES: To investigate the impact of 3-Diminsional (3D) tumor volume (TV) and extent of involvement of primary tumor on treatment outcomes in a large uniform cohort of T3 laryngeal carcinoma patients treated with nonsurgical laryngeal preservation strategies. MATERIALS AND METHODS: The pretreatment contrast-enhanced computed tomography images of 90 patients with T3 laryngeal carcinoma were reviewed. Primary gross tumor volume (GTVp) was delineated to calculate the 3D TV and define the extent of invasion. Cartilage and soft tissue involvement was coded. The extent of invasion was dichotomized into non/limited invasion versus multiple invasion extension (MIE), and was subsequently correlated with survival outcomes. RESULTS: The median TV was 6.6 cm3. Sixty-five patients had non/limited invasion, and 25 had MIE. Median follow-up for surviving patients was 52 months. The 5-year local control and overall survival rates for the whole cohort were 88% and 68%, respectively. There was no correlation between TV and survival outcomes. However, patients with non/limited invasion had better 5-year local control (LC) than those with MIE (95% vs 72%, p = .009) but did not have a significantly higher rate of overall survival (OS) (74% vs 67%, p = .327). In multivariate correlates of LC, MIE maintained statistical significance whereas baseline airway status showed a statistically significance trend with poor LC (p = .0087 and 0.06, respectively). Baseline good performance status was an independent predictor of improved OS (p = .03) in multivariate analysis. CONCLUSION: The extent of primary tumor invasion is an independent prognostic factor of LC of the disease after definitive radiotherapy in T3 larynx cancer.

Barriers to Combined-Modality Therapy for Limited-Stage Small Cell Lung Cancer.

Importance: Combined-modality therapy with chemotherapy and radiation therapy plays a crucial role in the upfront treatment of patients with limited-stage small cell lung cancer (SCLC), but there may be barriers to utilization in the United States. Objective: To estimate utilization rates and factors associated with chemotherapy and radiation therapy delivery for limited-stage SCLC using the National Cancer Database. Design, Setting, and Participants: Analysis of initial management of all limited-stage SCLC cases from 2004 through 2013 in the National Cancer Database. Main Outcomes and Measures: Utilization rates of chemotherapy and radiation therapy at time of initial treatment. Multivariable analysis identified independent clinical and socioeconomic factors associated with utilization and overall survival. Results: A total of 70 247 cases met inclusion criteria (55.3% female; median age, 68 y [range, 19-90 y]). Initial treatment was 55.5% chemotherapy and radiation therapy, 20.5% chemotherapy alone, 3.5% radiation therapy alone, and 20.0% neither (0.5% not reported). Median survival was 18.2 (95% CI, 17.9-18.4), 10.5 (95% CI, 10.3-10.7), 8.3 (95% CI, 7.7-8.8), and 3.7 (95% CI, 3.5-3.8) months, respectively. Being uninsured was associated with a lower likelihood of both chemotherapy (odds ratio [OR], 0.65; 95% CI, 0.56-0.75; P < .001) and radiation therapy (OR, 0.75; 95% CI, 0.67-0.85; P < .001) administration on multivariable analysis. Medicare/Medicaid insurance had no impact on chemotherapy use, whereas Medicaid (OR, 0.79; 95% CI, 0.72-0.87; P < .001) and Medicare (OR, 0.86; 95% CI, 0.82-0.91; P < .001) were independently associated with a lower likelihood of radiation therapy delivery. Lack of health insurance (HR, 1.19; 95% CI, 1.13-1.26; P < .001), Medicaid (HR, 1.27; 95% CI, 1.21-1.32; P < .001), and Medicare (HR, 1.12; 95% CI, 1.09-1.15; P < .001) coverage were independently associated with shorter survival on adjusted analysis, while chemotherapy (HR, 0.55; 95% CI, 0.54-0.57; P < .001) and radiation therapy (HR, 0.62; 95% CI, 0.60-0.63; P < .001) were associated with a survival benefit. Conclusions and Relevance: Substantial proportions of patients documented in a major US cancer registry did not receive radiation therapy or chemotherapy as part of initial treatment for limited-stage SCLC, which, in turn, was associated with poor survival. Lack of radiation therapy delivery was uniquely associated with government insurance coverage, suggesting a need for targeted access improvement in this population. Additional work will be necessary to conclusively define exact population patterns, specific treatment deficiencies, and causative factors leading to heterogeneous care delivery.

Correction to: Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old.

In the original publication [1] the name of author Jeremy M. Aymard was spelled wrong. The original article was updated to rectify this error.

Long-term patient reported outcomes following radiation therapy for oropharyngeal cancer: cross-sectional assessment of a prospective symptom survey in patients ≥65 years old.

BACKGROUND: Given the potential for older patients to experience exaggerated toxicity and symptoms, this study was performed to characterize patient reported outcomes in older patients following definitive radiation therapy (RT) for oropharyngeal cancer (OPC). METHODS: Cancer-free head and neck cancer survivors (>6 months since treatment completion) were eligible for participation in a questionnaire-based study. Participants completed the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN). Those patients ≥65 years old at treatment for OPC with definitive RT were included. Individual and overall symptom severity and clinical variables were analyzed. RESULTS: Of the 79 participants analyzed, 82% were male, 95% white, 41% T3/4 disease, 39% RT alone, 27% induction chemotherapy, 52% concurrent, and 18% both, and 96% IMRT. Median age at RT was 71 yrs. (range: 65-85); median time from RT to MDASI-HN was 46 mos. (2/3 > 24 mos.). The top 5 MDASI-HN items rated most severe in terms of mean (±SD) ratings (0-10 scale) were dry mouth (3.48 ± 2.95), taste (2.81 ± 3.29), swallowing (2.59 ± 2.96), mucus in mouth/throat (2.04 ± 2.68), and choking (1.30 ± 2.38) reported at moderate-severe levels (≥5) by 35, 29, 29, 18, and 13%, respectively. Thirty-nine % reported none (0) or no more than mild (1-4) symptoms across all 22 MDASI-HN symptoms items, and 38% had at least one item rated as severe (≥7). Hierarchical cluster analysis resulted in 3 patient groups: 1) ~65% with ranging from none to moderate symptom burden, 2) ~35% with moderate-severe ratings for a subset of classically RT-related symptoms (e.g. dry mouth, mucus, swallowing) and 3) 2 pts. with severe ratings of most items. CONCLUSIONS: The overall long-term symptom burden seen in this older OPC cohort treated with modern standard therapy was largely favorable, yet a higher symptom group (~35%) with a distinct pattern of mostly local and classically RT-related symptoms was identified.

Quantitative assessment of target delineation variability for thymic cancers: Agreement evaluation of a prospective segmentation challenge.

OBJECTIVES: We sought to quantitatively determine the inter-observer variability of expert radiotherapy target-volume delineation for thymic cancers, as part of a larger effort to develop an expert-consensus contouring atlas. METHODS: A pilot dataset was created consisting of a standardized case presentation with pre- and post-operative DICOM CT image sets from a single patient with Masaoka-Koga Stage III thymoma. Expert thoracic radiation oncologists delineated tumor targets on the pre- and post-operative scans as they would for a definitive and adjuvant case, respectively. Respondents completed a survey including recommended dose prescription and target volume margins for definitive and post-operative scenarios. Inter-observer variability was analyzed quantitatively with Warfield's simultaneous truth, performance level estimation (STAPLE) algorithm and Dice similarity coefficient (DSC). RESULTS: Seven users completed contouring for definitive and adjuvant cases; of these, 5 completed online surveys. Segmentation performance was assessed, with high mean±SD STAPLE-estimated segmentation sensitivity for definitive case GTV and CTV at 0.77 and 0.80, respectively, and post-operative CTV sensitivity of 0.55; all volumes had specificity of ≥0.99. Inter-observer agreement was markedly higher for the definitive target volumes, with mean±SD DSC of 0.88±0.03 and 0.89±0.04 for GTV and CTV respectively, compared to post-op CTV DSC of 0.69±0.06 (Kruskal-Wallis p<0.01. CONCLUSION: Expert agreement for definitive case volumes was exceptionally high, though significantly lower agreement was noted post-operatively. Technique and dose prescription between experts was substantively consistent, and these preliminary results will be utilized to create an expert-consensus contouring atlas to aid the non-expert radiation oncologist in the planning of these challenging, rare tumors.

Favorable patient reported outcomes following IMRT for early carcinomas of the tonsillar fossa: Results from a symptom assessment study.

BACKGROUND: A questionnaire-based study was conducted to assess long-term patient reported outcomes (PROs) following definitive IMRT-based treatment for early stage carcinomas of the tonsillar fossa. METHODS: Participants had received IMRT with or without systemic therapy for squamous carcinoma of the tonsillar fossa (T1-2 and N0-2b) with a minimum follow-up of 2years. Patients completed a validated head and neck cancer-specific PRO instrument, the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN). Symptoms were compared between treatment groups of interest and overall symptom burden was evaluated. RESULTS: Of 139 participants analyzed, 51% had received ipsilateral neck IMRT, and 62% single modality IMRT alone (no systemic therapy). There were no differences in mean severity ratings for the top-ranked individual symptoms or symptom interference for those treated with bilateral versus ipsilateral neck IMRT alone. However, 40% of those treated with bilateral versus 25% of those treated with ipsilateral neck RT alone reported moderate-to-severe levels of dry mouth (p=0.03). Fatigue, numbness/tingling, and constipation were rated more severe for those who had received systemic therapy (p<0.05 for each), but absolute differences were small. Overall, 51% had no more than mild symptom ratings across all 22 symptoms assessed. CONCLUSIONS: The long-term patient reported symptom profile in this cohort of tonsil cancer survivors treated with definitive IMRT-based treatment showed a majority of patients with no more than mild symptoms, low symptom interference, and provides an opportunity for future comparison studies with other treatment approaches.