Bioelectrical impedance analysis as a quantitative measure of sarcopenia in head and neck cancer patients treated with radiotherapy.

BACKGROUND AND PURPOSE: Sarcopenia is associated with decreased survival in head and neck cancer patients treated with radiotherapy. This study sought to determine whether in-clinic multifrequency bioelectrical impedance analysis (BIA) can identify survival-associated sarcopenia in patients with head and neck cancer. MATERIALS AND METHODS: This prospective observational study enrolled 50 patients with head and neck cancer undergoing radiation therapy. Baseline BIA measures of skeletal muscle (SM) mass, fat-free mass (FFM), and fat mass (FM) were compared to CT-based estimates using linear regression. Sex-specific BIA-derived thresholds for sarcopenia were defined by the maximum Youden Index on receiver operator characteristic (ROC) curves. Patients were stratified by sarcopenia status and OS was compared using the Kaplan-Meier method and log-rank test. RESULTS: Among 48 evaluable patients, BIA measures of body composition were strongly correlated with CT measures: SM mass (r = 0.97; R2 = 0.94; p < 0.0001), FFM (r = 0.97; R2 = 0.94; p < 0.0001) and FM (r = 0.95; R2 = 0.90; p < 0.0001). SM mass index < 9.19 kg/m2 identified sarcopenia men with high sensitivity (91.7%) and specificity (92.9%), whereas in women SM mass index < 6.53 kg/m2 was sensitive for sarcopenia (100%), but not specific. Patients with sarcopenia, defined by either CT or BIA, exhibited decreased OS (HR = not estimable; CT p = 0.009; BIA p = 0.03). CONCLUSION: BIA provides accurate estimates of body composition in head and neck cancer patients. Implementation of BIA in clinical practice may identify patients with sarcopenia at risk for poor survival.

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges.

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the "HPV" challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the "local recurrence" challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

Incompletely treated malignancies of the major salivary gland: Toward evidence-based care.

BACKGROUND: Unexpected malignancy is common in major salivary gland tumors due to variability of workup, creating challenging treatment decisions. The purpose of this study was to define treatment-related outcomes for patients with incompletely treated major salivary gland tumors. METHODS: A retrospective cohort study was completed of patients with incompletely treated major salivary gland tumors. Tumor burden at presentation was established and treatment categorized. The Cox Proportional Hazards model was used to determine predictors of survival and failure. RESULTS: Of the 440 included patients, patients with gross residual or metastatic disease had a worse overall survival (OS; P < .001). Presentation status was an independent predictor of OS on multivariate analysis (gross residual disease adjusted hazard ratio [HRadjusted ] 2.55; 95% confidence interval [CI] 1.20-5.30; metastatic disease HRadjusted 9.53; 95% CI 3.04-27.06). CONCLUSION: Failure to achieve gross total resection during initial surgery resulted in worse OS. Adequate preoperative planning is required for initial surgical management to optimize tumor control and survival.

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.

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.

Patterns-of-failure guided biological target volume definition for head and neck cancer patients: FDG-PET and dosimetric analysis of dose escalation candidate subregions.

BACKGROUND: To identify the radio-resistant subvolumes in pretreatment FDG-PET by mapping the spatial location of the origin of tumor recurrence after IMRT for head-and-neck squamous cell cancer to the pretreatment FDG-PET/CT. METHODS: Patients with local/regional recurrence after IMRT with available FDG-PET/CT and post-failure CT were included. For each patient, both pre-therapy PET/CT and recurrence CT were co-registered with the planning CT (pCT). A 4-mm radius was added to the centroid of mapped recurrence growth target volumes (rGTV's) to create recurrence nidus-volumes (NVs). The overlap between boost-tumor-volumes (BTV) representing different SUV thresholds/margins combinations and NVs was measured. RESULTS: Forty-seven patients were eligible. Forty-two (89.4%) had type A central high dose failure. Twenty-six (48%) of type A rGTVs were at the primary site and 28 (52%) were at the nodal site. The mean dose of type A rGTVs was 71Gy. BTV consisting of 50% of the maximum SUV plus 10mm margin was the best subvolume for dose boosting due to high coverage of primary site NVs (92.3%), low average relative volume to CTV1 (41%), and least average percent voxels outside CTV1 (19%). CONCLUSIONS: The majority of loco-regional recurrences originate in the regions of central-high-dose. When correlated with pretreatment FDG-PET, the majority of recurrences originated in an area that would be covered by additional 10mm margin on the volume of 50% of the maximum FDG uptake.