Performance Evaluation of Radiologists with Artificial Neural Network for Differential Diagnosis of Intra-Axial Cerebral Tumors on MR Images

Abstract

BACKGROUND AND PURPOSE:Previous studies have suggested that use of an artificial neural network (ANN) system is beneficial for radiological diagnosis. Our purposes in this study were to construct an ANN for the differential diagnosis of intra-axial cerebral tumors on MR images and to evaluate the effect of ANN outputs on radiologists' diagnostic performance.

Evaluation of Apparent Diffusion Coefficient Thresholds for Diagnosis of Medulloblastoma Using Diffusion-Weighted Imaging

“…In another retype. In fact, such studies using artificial neural networks to characterize brain tumors are already underway for adult brain tumors 5 . As an example, investigators are assessing pattern classification methods for discriminating between primary gliomas and metastases, as well as grading primary brain tumors 6 .…”

“…Extensive literature describes the implementation of CAD for detection and diagnosis of a number of conditions including pulmonary nodules, breast cancer, and aneurysms 2,3 . However, applications of CAD to neuroradiology, and in particular, to brain tumor diagnosis, have been relatively limited to date [4][5][6] . The goal of the present study was to test an analytic technique based on diffusionweighted imaging for classification of pediatric posterior fossa tumors that might be amenable to eventual automated use via CAD.…”

Artificial Neural Networks for Prediction of Response to Chemoradiation in HT29 Xenografts

“…Artificial neural networks (ANN) have been used in clinical diagnosis (9), image analysis in radiology (10) and histopathology (11), and in interpretation of various wave forms including electrocardiograms (12,13), echoencephalograms (13), and electromyography (14). In some of these studies, human observations have also been used as inputs to the networks (15).…”

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

“…The average AUC for all radiologists for differentiation among the three lesions increased significantly from 0.910 to 0.985 (P = 0.0024) when they used the computer output. We also constructed an ANN for the differential diagnosis of intra-axial cerebral tumors on MR images and evaluated the effect of ANN outputs on radiologists' diagnostic performance [60]. The radiologists collected MR images of 126 patients with intra-axial cerebral tumors (58 high-grade gliomas, 37 low-grade gliomas, 19 metastatic tumors, and 12 malignant lymphomas).…”

“…In the CAD system for classification of cerebral tumors, the ANN was designed to differentiate among 4 categories of tumors (high-grade gliomas, low-grade gliomas, metastases, and malignant lymphomas) with use of 2 clinical parameters (age and history of malignant tumor) and 13 radiologic findings (e.g., location, signal intensity on T2-wieghted images) in MR images [60]. Figure 9 shows an ANN classifier for differentiating cerebral tumors into 4 categories, which was composed of 15 input units and 4 output units.…”