IMPORTANCE Major weight loss is common in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy (RT). How baseline and posttreatment body composition affects outcome is unknown. OBJECTIVE To determine whether lean body mass before and after RT for HNSCC predicts survival and locoregional control. DESIGN, SETTING, AND PARTICIPANT Retrospective study of 2840 patients with pathologically proven HNSCC undergoing curative RT at a single academic cancer referral center from October 1, 2003, to August 31, 2013. One hundred ninety patients had computed tomographic (CT) scans available for analysis of skeletal muscle (SM). The effect of pre-RT and post-RT SM depletion (defined as a CT-measured L3 SM index of less than 52.4 cm2 /m2 for men and less than 38.5 cm2 /m2 for women) on survival and disease control was evaluated. Final follow-up was completed on September 27, 2014, and data were analyzed from October 1, 2014, to November 29, 2015. MAIN OUTCOMES AND MEASURES Primary outcomes were overall and disease-specific survival and locoregional control. Secondary analyses included the influence of pre-RT body mass index (BMI) and interscan weight loss on survival and recurrence. RESULTS Among the 2840 consecutive patients who underwent screening, 190 had whole-body positron emission tomography–CT or abdominal CT scans before and after RT and were included for analysis. Of these, 160 (84.2%) were men and 30 (15.8%) were women; their mean (SD) age was 57.7 (9.4) years. Median follow up was 68.6 months. Skeletal muscle depletion was detected in 67 patients (35.3%) before RT and an additional 58 patients (30.5%) after RT. Decreased overall survival was predicted by SM depletion before RT (hazard ratio [HR], 1.92; 95% CI, 1.19–3.11; P = .007) and after RT (HR, 2.03; 95% CI, 1.02–4.24; P = .04). Increased BMI was associated with significantly improved survival (HR per 1-U increase in BMI, 0.91; 95% CI, 0.87–0.96; P < .001). Weight loss without SM depletion did not affect outcomes. Post-RT SM depletion was more substantive in competing multivariate models of mortality risk than weight loss–based metrics (Bayesian information criteria difference, 7.9), but pre-RT BMI demonstrated the greatest prognostic value. CONCLUSIONS AND RELEVANCE Diminished SM mass assessed by CT imaging or BMI can predict oncologic outcomes for patients with HNSCC, whereas weight loss after RT initiation does not predict SM loss or survival.

In the original version of the Data Descriptor the surname of author Hesham Elhalawani was misspelled. This has now been corrected in the HTML and PDF versions.

Purpose To develop a quality assurance (QA) workflow using a robust, curated, manually-segmented anatomic ROI library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation CT (SimCT) to diagnostic CT (DxCT) co-registration. Materials and Methods SimCTs and DxCTs of twenty patients with head and neck squamous cell carcinoma treated with curative-intent intensity modulated radiotherapy (IMRT) between August 2011 and May 2012 were retrospectively retrieved under an institutional review board approval. 68 reference anatomic regions of interest (ROIs) in addition to gross tumor and nodal targets were then manually contoured on each scan. DxCT was registered to SimCT rigidly, and through 4 different deformable image registration (DIR) algorithms; Atlas-based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs using similarity coefficient metrics (i.e. Dice similarity coefficient) and surface distance metrics (i.e. 95% maximum Hausdorff distance). Non-parametric Steel test with control was used to compare different DIR algorithms to rigid registration (RIR) with post hoc Wilcoxon rank test for stratified metric comparison. Results A total of 2720 anatomic and 50 tumor/nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for both anatomic and target ROIs conformance as shown for the majority of comparison metrics (Steel test, p-value <0.008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures/category, and SimCT image slice thickness. Conclusion Development of a formal ROI-based QA workflow for registration assessment revealed improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck DxCT-SimCT allineation, especially for target delineation.

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Cross sectional imaging is essential for the patient-specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Due to challenges ensuring data quality and patient de-identification, publicly available datasets including diagnostic and radiation treatment planning imaging are scarce. In this data descriptor, we detail the collection and processing of computed tomography based imaging in 215 patients with head and neck squamous cell carcinoma that were treated with radiotherapy. Using cross sectional imaging, we calculated total body skeletal muscle and adipose content before and after treatment. We detail techniques for validating the high quality of these data and describe the processes of data de-identification and transfer. All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival. These data are a valuable resource for studying the association between patient-specific anatomic and metabolic features, treatment planning, and oncologic outcomes, and the first that allows for the integration of body composition as a risk factor or study outcome.