BACKGROUND: The authors hypothesized that unilateral intensity‐modulated radiotherapy (IMRT) would decrease toxicity compared with bilateral IMRT for patients with lateralized palatine tonsillar cancer and a neck classification of N0 to N2b, with similar oncological outcomes.METHODS: A total of 154 patients were treated with postoperative IMRT from 1997 through 2013. Data were collected prospectively from 2005 to 2013 and retrospectively collected before 2005. Of those patients with lateralized primary and N0 to N2b disease, 48 received unilateral IMRT (group 1) and 59 received bilateral IMRT (group 2); a total of 47 patients had nonlateralized primary or N2c to N3 disease and received bilateral IMRT (group 3).RESULTS: The median follow‐up was 5.5 years. The 5‐year locoregional control rates were similar in group 1, group 2, and group 3 (100%, 96%, and 94%, respectively; pooled comparison: P = .39 and group 1 vs group 2 comparison: P = .19). The 5‐year overall survival rates were similar in group 1, group 2, and group 3 (85%, 79%, and 76%, respectively; pooled comparison: P = .60 and group 1 vs group 2 comparison: P = .25). There were no contralateral neck recurrences noted among unilaterally treated patients. Unilateral IMRT reduced acute toxicity and improved patient‐reported quality of life compared with bilateral IMRT.CONCLUSIONS: Unilateral IMRT appears to reduce acute toxicity and achieves oncological outcomes similar to those of bilateral IMRT in selected patients with lateralized palatine tonsillar cancer with a neck classification of N0 to N2b. Cancer 2017;123:4594‐4607. © 2017 American Cancer Society.

Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) have a poor prognosis, with a significant risk of progression or death despite multimodal treatment with surgery, chemotherapy, and radiotherapy. Immune checkpoint inhibitors targeting the programmed death receptor-1 (PD1) have dramatically changed the treatment landscape for recurrent/metastatic disease, improving overall survival in both the first- and second-line palliative settings. This success has driven the investigation of treatment strategies incorporating immunotherapy earlier into the multimodal curative-intent or salvage treatment of both locally advanced and recurrent/metastatic HNSCC. This review encompassed the following three subjects, with a focus on recently reported and ongoing clinical trials: (1) the use of neoadjuvant immunotherapy prior to surgery for locally advanced HNSCC, (2) the use of immunochemoradiotherapy for locally advanced head and neck cancers, and (3) novel uses of immunotherapy in the salvage of recurrent/metastatic HNSCC via a combined modality, including reirradiation paradigms. The results of these studies are eagerly awaited to improve patient outcomes in this challenging disease.

(1) Background: Recent studies report high accuracies when using machine learning (ML) algorithms to classify prostate cancer lesions on publicly available datasets. However, it is unknown if these trained models generalize well to data from different institutions. (2) Methods: This was a retrospective study using multi-parametric Magnetic Resonance Imaging (mpMRI) data from our institution (63 mpMRI lesions) and the ProstateX-2 challenge, a publicly available annotated image set (112 mpMRI lesions). Residual Neural Network (ResNet) algorithms were trained to classify lesions as high-risk (hrPCA) or low-risk/benign. Models were trained on (a) ProstateX-2 data, (b) local institutional data, and (c) combined ProstateX-2 and local data. The models were then tested on (a) ProstateX-2, (b) local and (c) combined ProstateX-2 and local data. (3) Results: Models trained on either local or ProstateX-2 image data had high Area Under the ROC Curve (AUC)s (0.82–0.98) in the classification of hrPCA when tested on their own respective populations. AUCs decreased significantly (0.23–0.50, p < 0.01) when models were tested on image data from the other institution. Models trained on image data from both institutions re-achieved high AUCs (0.83–0.99). (4) Conclusions: Accurate prostate cancer classification models trained on single-institutional image data performed poorly when tested on outside-institutional image data. Heterogeneous multi-institutional training image data will likely be required to achieve broadly applicable mpMRI models.

Background: Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology has been discovered. As such, uncovering objective biomarkers is important to lend credibility to criteria for diagnosis and to help differentiate the two disorders. Methods: We assessed cognitive differences in 80 subjects with GWI and 38 with CFS by comparing corresponding fMRI scans during 2-back working memory tasks before and after exercise to model brain activation during normal activity and after exertional exhaustion, respectively. Voxels were grouped by the count of total activity into the Automated Anatomical Labeling (AAL) atlas and used in an “ensemble” series of machine learning algorithms to assess if a multi-regional pattern of differences in the fMRI scans could be detected. Results: A K-Nearest Neighbor (70%/81%), Linear Support Vector Machine (SVM) (70%/77%), Decision Tree (82%/82%), Random Forest (77%/78%), AdaBoost (69%/81%), Naïve Bayes (74%/78%), Quadratic Discriminant Analysis (QDA) (73%/75%), Logistic Regression model (82%/82%), and Neural Net (76%/77%) were able to differentiate CFS from GWI before and after exercise with an average of 75% accuracy in predictions across all models before exercise and 79% after exercise. An iterative feature selection and removal process based on Recursive Feature Elimination (RFE) and Random Forest importance selected 30 regions before exercise and 33 regions after exercise that differentiated CFS from GWI across all models, and produced the ultimate best accuracies of 82% before exercise and 82% after exercise by Logistic Regression or Decision Tree by a single model, and 100% before and after exercise when selected by any six or more models. Differential activation on both days included the right anterior insula, left putamen, and bilateral orbital frontal, ventrolateral prefrontal cortex, superior, inferior, and precuneus (medial) parietal, and lateral temporal regions. Day 2 had the cerebellum, left supplementary motor area and bilateral pre- and post-central gyri. Changes between days included the right Rolandic operculum switching to the left on Day 2, and the bilateral midcingulum switching to the left anterior cingulum. Conclusion: We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model.

Gulf War Illness (GWI) is a debilitating condition characterized by dysfunction of cognition, pain, fatigue, sleep, and diverse somatic symptoms with no known underlying pathology. As such, uncovering objective biomarkers such as differential regions of activity within a Functional Magnetic Resonance Imaging (fMRI) scan is important to enhance validity of the criteria for diagnosis. Symptoms are exacerbated by mild activity, and exertional exhaustion is a key complaint amongst sufferers. We modeled this exertional exhaustion by having GWI (n = 80) and sedentary control (n = 31) subjects perform submaximal exercise stress tests on two consecutive days. Cognitive differences were assessed by comparing fMRI scans performed during 2-Back working memory tasks before and after the exercise. Machine learning algorithms were used to identify differences in brain activation patterns between the two groups on Day 1 (before exercise) and Day 2 (after exercise). The numbers of voxels with t > 3.17 (corresponding to p < 0.001 uncorrected) were determined for brain regions defined by the Automated Anatomical Labeling (AAL) atlas. Data were divided 70:30 into training and test sets. Recursive feature selection identified twenty-nine regions of interest (ROIs) that significantly distinguished GWI from control on Day 1 and 28 ROIs on Day 2. Ten regions were present in both models between the two days, including right anterior insula, orbital frontal cortex, thalamus, bilateral temporal poles, and left supramarginal gyrus and cerebellar Crus 1. The models had 70% accuracy before exercise on Day 1 and 85% accuracy after exercise on Day 2, indicating the logistic regression model significantly differentiated subjects with GWI from the sedentary control group. Exercise caused changes in these patterns that may indicate the cognitive differences caused by exertional exhaustion. A second set of predictive models was able to classify previously identified GWI exercise subgroups START, STOPP, and POTS for both Days 1 and Days 2 with 67% and 69% accuracy respectively. This study was the first of its kind to differentiate GWI and the three sub-phenotypes START, STOPP, and POTS from a sedentary control using a logistic regression estimation method.

To explore if early perfusion-weighted magnetic resonance imaging (PWI) may be a promising imaging biomarker to predict local recurrence (LR) of brain metastases after stereotactic radiosurgery (SRS). This is a prospective pilot study of adult brain metastasis patients who were treated with SRS and imaged with PWI before and 1 week later. Relative cerebral blood volume (rCBV) parameter maps were calculated by normalizing to the mean value of the contralateral white matter on PWI. Cox regression was conducted to explore factors associated with time to LR, with Bonferroni adjusted p2.0cm³ had significantly higher LR than if ≤2.0cm³: 54% vs 0% at 1 year, respectively, p = 0.008. A future study to confirm the association of early PWI and LR of the high-risk cohort of lesions >2.0cm³ is estimated to require 258 patients. PWI at week 1 after SRS may have borderline association with LR. Tumors <2.0cm³ have low risk of LR after SRS and may be low-yield for predictive biomarker studies. Information regarding sample size and potential challenges for future imaging biomarker studies may be gleaned from this pilot study.