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AJNR - American Journal of Neuroradiology

Published ahead of print on April 3, 2008
doi: 10.3174/ajnr.A1037

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American Journal of Neuroradiology 29:1153-1158, June-July 2008
© 2008 American Society of Neuroradiology

BRAIN

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

K. Yamashita^a, T. Yoshiura^a, H. Arimura^b, F. Mihara^a, T. Noguchi^a, A. Hiwatashi^a, O. Togao^a, Y. Yamashita^d, T. Shono^c, S. Kumazawa^b, Y. Higashida^b and H. Honda^a

^a Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
^b Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
^c Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
^d Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Japan

Please address correspondence to Takashi Yoshiura, PhD, Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582 Japan; e-mail: tyoshiu{at}med.kyushu-u.ac.jp

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.

MATERIALS AND METHODS: We 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). We constructed a single 3-layer feed-forward ANN with a Levenberg-Marquardt algorithm. 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 and 13 radiologic findings in MR images. Subjective ratings for the 13 radiologic findings were provided independently by 2 attending radiologists. All 126 cases were used for training and testing of the ANN based on a leave-one-out-by-case method. In the observer test, MR images were viewed by 9 radiologists, first without and then with ANN outputs. Each radiologist's performance was evaluated through a receiver operating characteristic (ROC) analysis on a continuous rating scale.

RESULTS: The averaged area under the ROC curve for ANN alone was 0.949. The diagnostic performance of the 9 radiologists increased from 0.899 to 0.946 (P < .001) when they used ANN outputs.

CONCLUSIONS: The ANN can provide useful output as a second opinion to improve radiologists' diagnostic performance in the differential diagnosis of intra-axial cerebral tumors seen on MR imaging.