Publication trend, resource utilization, and impact of the US National Cancer Database: A systematic review.

BACKGROUND: The utilization and impact of the studies published using the National Cancer Database (NCDB) is currently unclear. In this study, we aim to characterize the published studies, and identify relatively unexplored areas for future investigations. METHODS: A literature search was performed using PubMed in January 2017 to identify all papers published using NCDB data. Characteristics of the publications were extracted. Citation frequencies were obtained through the Web of Science. RESULTS: Three hundred 2 articles written by 230 first authors met the inclusion criteria. The number of publications grew exponentially since 2013, with 108 articles published in 2016. Articles were published in 86 journals. The majority of the published papers focused on digestive system cancer, while bone and joints, eye and orbit, myeloma, mesothelioma, and Kaposi Sarcoma were never studied. Thirteen institutions in the United States were associated with more than 5 publications. The papers have been cited for a total of 9858 times since the publication of the first paper in 1992. Frequently appearing keywords congregated into 3 clusters: "demographics," "treatments and survival," and "statistical analysis method." Even though the main focuses of the articles captured a extremely wide range, they can be classified into 2 main categories: survival analysis and characterization. Other focuses include database(s) analysis and/or comparison, and hospital reporting. CONCLUSION: The surging interest in the use of NCDB is accompanied by unequal utilization of resources by individuals and institutions. Certain areas were relatively understudied and should be further explored.

Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging.

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data.Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming.Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively.Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073-81. ©2017 AACR.