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Predicting survival of oral squamous cell carcinoma through the use of prediction modeling has been underused, and the development of prediction models would augment clinicians' ability to provide absolute risk estimates for individual patients.OBJECTIVES To develop a prediction model using machine learning for 5-year overall survival among patients with oral squamous cell carcinoma and compare this model with a prediction model created from the TNM (Tumor, Node, Metastasis) clinical and pathologic stage. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort study was conducted of 33 065 patients with oral squamous cell carcinoma from the National Cancer Data Base between January 1, 2004, and December 31, 2011. Patients were excluded if the treatment was considered palliative, staging demonstrated T0 or Tis, or survival or staging data were missing. Patient, tumor, treatment, and outcome information were obtained from the National Cancer Data Base. The data were split into a distribution of 80% for training and 20% for testing. The model was created using 2-class decision forest architecture. Permutation feature importance scores were used to determine the variables that were used in the model's prediction and their order of significance. Statistical analysis was conducted from August 1, 2018, to January 10, 2019.MAIN OUTCOMES AND MEASURES Ability to predict 5-year overall survival assessed through area under the curve, accuracy, precision, and recall. RESULTS Among the 33 065 patients in the study, the mean (SD) age was 64.6 (14.0) years, 19 791 were men (59.9%), 13 274 were women (40.1%), and 29 783 (90.1%) were white. At 60 months, there were 16 745 deaths (50.6%). The median time of follow-up was 56.8 months (range, 0-155.6 months). Age, pathologic T stage, positive margins at the time of surgery, lymph node size, and institutional identification were identified among the most significant variables. The calculated area under the curve for this machine learning model was 0.80 (95% CI, 0.79-0.81), accuracy was 71%, precision was 71%, and recall was 68%. In comparison, the calculated area under the curve of the TNM staging system was 0.68 (95% CI, 0.67-0.70), accuracy was 65%, precision was 69%, and recall was 52%.CONCLUSIONS AND RELEVANCE Using machine learning algorithms, a prediction model was created based on patient social, demographic, clinical, and pathologic features. The developed prediction model proved to be better than a prediction model that exclusively used TNM pathologic and clinical stage according to all performance metrics. This study highlights the role that machine learning may play in individual patient risk estimation in the era of big data.

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Background The scientific understanding of tinnitus and its etiology have transitioned from thinking of tinnitus as solely a peripheral auditory problem to an increasing awareness that cortical networks may play a critical role in tinnitus percept or bother. With this change, studies that seek to use structural brain imaging techniques to better characterize tinnitus patients have become more common. These studies include using voxel-based morphometry (VBM) to determine if there are differences in regional gray matter volume in individuals who suffer from tinnitus and those who do not. However, studies using VBM in patients with tinnitus have produced inconsistent and sometimes contradictory results. Objective This paper is a systematic review of all of the studies to date that have used VBM to study regional gray matter volume in people with tinnitus, and explores ways in which methodological differences in these studies may account for their heterogeneous results. We also aim to provide guidance on how to conduct future studies using VBM to produce more reproducible results to further our understanding of disease processes such as tinnitus. Methods Studies about tinnitus and VBM were searched for using PubMed and Embase. These returned 15 and 25 results respectively. Of these, nine met the study criteria and were included for review. An additional 5 studies were identified in the literature as pertinent to the topic at hand and were added to the review, for a total of 13 studies. Results There was significant heterogeneity among the studies in several areas, including inclusion and exclusion criteria, software programs, and statistical analysis. We were not able to find publicly shared data or code for any study. Discussion The differences in study design, software analysis, and statistical methodology make direct comparisons between the different studies difficult. Especially problematic are the differences in the inclusion and exclusion criteria of the study, and the statistical design of the studies, both of which could radically alter findings. Thus, heterogeneity has complicated efforts to explore the etiology of tinnitus using structural MRI. Conclusion There is a pressing need to standardize the use of VBM when evaluating tinnitus patients. While some heterogeneity is expected given the rapid advances in the field, more can be done to ensure that there is internal validity between studies.

Background Several prospective studies report improved outcomes with pretreatment nutrition interventions prior to radiation therapy for head and neck cancer (HNC), but none have assessed similar interventions before surgery for HNC. Methods POINT, a pilot randomized controlled trial, was conducted to evaluate a multimodal nutrition intervention. Patients undergoing primary surgery with free flap reconstruction for HNC were randomly assigned to the control arm or a preoperative multimodal nutrition intervention. Results POINT included 49 patients. Nutrition risk scores did not change significantly for either the intervention or control group. Control patients had a significant decrease in body weight in the preoperative period (p < 0.001). Conversely, weight among intervention patients did not significantly decrease (p = 0.680). The intervention mitigated weight loss in patients with dysphagia (p = 0.001). Conclusions Preoperative nutrition optimization shows potential to reduce weight loss normally experienced by patients with head and neck cancer prior to surgical extirpation, especially among those with subjective dysphagia.