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.
...To determine whether lean body mass before and after RT for HNSCC predicts survival and locoregional control.
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.
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.
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.
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.
First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We ...hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUV
). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.
Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images ...obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.
Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.
Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies Spearman's ρ = 0.94 (
< 0.0001).
Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.
The incidence of oropharyngeal squamous cell carcinoma (OPSCC) in the US is rapidly increasing, driven largely by the epidemic of human papillomavirus (HPV)-mediated OPSCC. Although survival for ...patients with HPV mediated OPSCC (HPV+ OPSCC) is generally better than that of patients with non-virally mediated OPSCC, this effect is not uniform. We hypothesized that tobacco exposure remains a critical modifier of survival for HPV+ OPSCC patients.
We conducted a retrospective analysis of 611 OPSCC patients with concordant p16 and HPV testing treated at a single institute (2002-2013). Survival analysis was performed using Kaplan-Meier analysis and Cox regression. Recursive partitioning analysis (RPA) was used to define tobacco exposure associated with survival (p < 0.05).
Tobacco exposure impacted overall survival (OS) for HPV+ patients on univariate and multivariate analysis (p = 0.002, p = 0.003 respectively). RPA identified 30 pack-years (PY) as a threshold at which survival became significantly worse in HPV+ patients. OS and disease-free survival (DFS) for HPV+ > 30 PY patients didn't differ significantly from HPV- patients (p = 0.72, p = 0.27, respectively). HPV+ > 30 PY patients had substantially lower 5-year OS when compared to their ≤30 PYs counterparts: 78.4% vs 91.6%; p = 0.03, 76% vs 88.3%; p = 0.07, and 52.3% vs 74%; p = 0.05, for stages I, II, and III (AJCC 8th Edition Manual), respectively.
Tobacco exposure can eliminate the survival benefit associated with HPV+ status in OPSCC patients. Until this effect can be clearly quantified using prospective datasets, de-escalation of treatment for HPV + OPSCC smokers should be avoided.
Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce interphysician variability, which is one of the largest sources of uncertainty in head and neck radiation ...therapy. In addition to using uniform margin expansions to auto-delineate high-risk CTVs, very little work has been performed to provide patient- and disease-specific high-risk CTVs. The aim of the present study was to develop a deep neural network for the auto-delineation of high-risk CTVs.
Fifty-two oropharyngeal cancer patients were selected for the present study. All patients were treated at The University of Texas MD Anderson Cancer Center from January 2006 to August 2010 and had previously contoured gross tumor volumes and CTVs. We developed a deep learning algorithm using deep auto-encoders to identify physician contouring patterns at our institution. These models use distance map information from surrounding anatomic structures and the gross tumor volume as input parameters and conduct voxel-based classification to identify voxels that are part of the high-risk CTV. In addition, we developed a novel probability threshold selection function, based on the Dice similarity coefficient (DSC), to improve the generalization of the predicted volumes. The DSC-based function is implemented during an inner cross-validation loop, and probability thresholds are selected a priori during model parameter optimization. We performed a volumetric comparison between the predicted and manually contoured volumes to assess our model.
The predicted volumes had a median DSC value of 0.81 (range 0.62-0.90), median mean surface distance of 2.8 mm (range 1.6-5.5), and median 95th Hausdorff distance of 7.5 mm (range 4.7-17.9) when comparing our predicted high-risk CTVs with the physician manual contours.
These predicted high-risk CTVs provided close agreement to the ground-truth compared with current interobserver variability. The predicted contours could be implemented clinically, with only minor or no changes.
Abstract Background Owing to its physical properties, intensity-modulated proton therapy (IMPT) used for patients with oropharyngeal carcinoma has the ability to reduce the dose to organs at risk ...compared to intensity-modulated radiotherapy (IMRT) while maintaining adequate tumor coverage. Our aim was to compare the clinical outcomes of these two treatment modalities. Methods We performed a 1:2 matching of IMPT to IMRT patients. Our study cohort consisted of IMPT patients from a prospective quality of life study and consecutive IMRT patients treated at a single institution during the period 2010–2014. Patients were matched on unilateral/bilateral treatment, disease site, human papillomavirus status, T and N status, smoking status, and receipt of concomitant chemotherapy. Survival analyzes were performed using a Cox model and binary toxicity endpoints using a logistic regression analysis. Results Fifty IMPT and 100 IMRT patients were included. The median follow-up time was 32 months. There were no imbalances in patient/tumor characteristics except for age (mean age 56.8 years for IMRT patients and 61.1 years for IMPT patients, p -value = 0.010). Statistically significant differences were not observed in overall survival (hazard ratio (HR) = 0.55; 95% confidence interval (CI): 0.12–2.50, p -value = 0.44) or in progression-free survival (HR = 1.02; 95% CI: 0.41–2.54; p -value = 0.96). The age-adjusted odds ratio (OR) for the presence of a gastrostomy (G)-tube during treatment for IMPT vs IMRT were OR = 0.53; 95% CI: 0.24–1.15; p -value = 0.11 and OR = 0.43; 95% CI: 0.16–1.17; p -value = 0.10 at 3 months after treatment. When considering the pre-planned composite endpoint of grade 3 weight loss or G-tube presence, the ORs were OR = 0.44; 95% CI: 0.19–1.0; p -value = 0.05 at 3 months after treatment and OR = 0.23; 95% CI: 0.07–0.73; p -value = 0.01 at 1 year after treatment. Conclusion Our results suggest that IMPT is associated with reduced rates of feeding tube dependency and severe weight loss without jeopardizing outcome. Prospective multicenter randomized trials are needed to validate such findings.
To automate the estimation of swallowing motion from 2D MR cine images using deformable registration for future applications of personalized margin reduction in head and neck radiotherapy and outcome ...assessment of radiation-associated dysphagia.
Twenty-one patients with serial 2D FSPGR-MR cine scans of the head and neck conducted through the course of definitive radiotherapy for oropharyngeal cancer. Included patients had at least one cine scan before, during, or after radiotherapy, with a total of 52 cine scans. Contours of 7 swallowing related regions-of-interest (ROIs), including pharyngeal constrictor, epiglottis, base of tongue, geniohyoid, hyoid, soft palate, and larynx, were manually delineated from consecutive frames of the cine scan covering at least one swallowing cycle. We applied a modified thin-plate-spline robust-point-matching algorithm to register the point sets of each ROI automatically over frames. The deformation vector fields from the registration were then used to estimate the motion during swallowing for each ROI. Registration errors were estimated by comparing the deformed contours with the manual contours.
On average 22 frames of each cine scan were contoured. The registration for one cine scan (7 ROIs over 22 frames) on average took roughly 22 minutes. A number of 8018 registrations were successfully batch processed without human interaction after the contours were drawn. The average registration error for all ROIs and all patients was 0.36 mm (range: 0.06 mm- 2.06 mm). Larynx had the average largest motion in superior direction of all structures under consideration (range: 0.0 mm- 58.7 mm). Geniohyoid had the smallest overall motion of all ROIs under consideration and the superior-inferior motion was larger than the anterior-posterior motion for all ROIs.
We developed and validated a deformable registration framework to automate the estimation of swallowing motion from 2D MR cine scans.
Abstract Objective To externally validate head and neck cancer (HNC) photon-derived normal tissue complication probability (NTCP) models in patients treated with proton beam therapy (PBT). Methods ...This prospective cohort consisted of HNC patients treated with PBT at a single institution. NTCP models were selected based on the availability of data for validation and evaluated by using the leave-one-out cross-validated area under the curve (AUC) for the receiver operating characteristics curve. Results 192 patients were included. The most prevalent tumor site was oropharynx ( n = 86, 45%), followed by sinonasal ( n = 28), nasopharyngeal ( n = 27) or parotid ( n = 27) tumors. Apart from the prediction of acute mucositis (reduction of AUC of 0.17), the models overall performed well. The validation (PBT) AUC and the published AUC were respectively 0.90 versus 0.88 for feeding tube 6 months PBT; 0.70 versus 0.80 for physician-rated dysphagia 6 months after PBT; 0.70 versus 0.68 for dry mouth 6 months after PBT; and 0.73 versus 0.85 for hypothyroidism 12 months after PBT. Conclusion Although a drop in NTCP model performance was expected for PBT patients, the models showed robustness and remained valid. Further work is warranted, but these results support the validity of the model-based approach for selecting treatment for patients with HNC.
To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients ...randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan-Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from .62 to .79 and from .66 to .80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features.
Because magnetic resonance imaging-guided radiation therapy (MRIgRT) offers exquisite soft tissue contrast and the ability to image tissues in arbitrary planes, the interest in this technology has ...increased dramatically in recent years. However, intrinsic geometric distortion stemming from both the system hardware and the magnetic properties of the patient affects MR images and compromises the spatial integrity of MRI-based radiation treatment planning, given that for real-time MRIgRT, precision within 2 mm is desired. In this article, we discuss the causes of geometric distortion, describe some well-known distortion correction algorithms, and review geometric distortion measurements from 12 studies, while taking into account relevant imaging parameters. Eleven of the studies reported phantom measurements quantifying system-dependent geometric distortion, while 2 studies reported simulation data quantifying magnetic susceptibility-induced geometric distortion. Of the 11 studies investigating system-dependent geometric distortion, 5 reported maximum measurements less than 2 mm. The simulation studies demonstrated that magnetic susceptibility-induced distortion is typically smaller than system-dependent distortion but still nonnegligible, with maximum distortion ranging from 2.1 to 2.6 mm at a field strength of 1.5 T. As expected, anatomic landmarks containing interfaces between air and soft tissue had the largest distortions. The evidence indicates that geometric distortion reduces the spatial integrity of MRI-based radiation treatment planning and likely diminishes the efficacy of MRIgRT. Better phantom measurement techniques and more effective distortion correction algorithms are needed to achieve the desired spatial precision.
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