PII S0730-725X(97)00302-0 ● Original Contribution MRI MEASUREMENT OF BRAIN TUMOR RESPONSE: COMPARISON OF VISUAL METRIC AND AUTOMATIC SEGMENTATION LAURENCE P. CLARKE,* ROBERT P. VELTHUIZEN,* MATT CLARK,² J ORGE GAVIRIA,* LARRY HALL,² DMITRY GOLDGOF,² R EED MURTAGH,* S. PHUPHANICH,‡ AND STEVEN BREM‡ *Department of Radiology, College of Medicine, University of South Florida and the H. Lee Moffitt Cancer and Research Institute, Tampa, FL; ²Department of Computer Science and Engineering, College of Engineering, University of South Florida, Tampa, FL; and the ‡Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA An automatic magnetic resonance imaging (MRI) multispectral segmentation method and a visual metric are compared for their effectiveness to measure tumor response to therapy. Automatic response measurements are important for multicenter clinical trials. A visual metric such as the product of the largest diameter and the largest perpendicular diameter of the tumor is a standard approach, and is currently used in the Radiation Treatment Oncology Group (RTOG) and the Eastern Cooperative Oncology Group (EGOG) clinical trials. In the standard approach, the tumor response is based on the percentage change in the visual metric and is categorized into cure, partial response, stable disease, or progression. Both visual and automatic methods are applied to six brain tumor cases (gliomas) of varying levels of segmentation difficulty. The analyzed data were serial multi- spectral MR images, collected using MR contrast enhancement. A fully automatic knowledge guided method (KG) was applied to the MRI multispectral data, while the visual metric was taken from the MRI films using the T 1 gadolinium enhanced image, with repeat measurements done by two radiologists and two residents. Tumor measurements from both visual and automatic methods are compared to ‘‘ground truth,’’ (GT) i.e., manually segmented tumor. The KG method was found to slightly overestimate tumor volume, but in a consistent manner, and the estimated tumor response compared very well to hand-drawn ground truth with a correlation coefficient of 0.96. In contrast, the visually estimated metric had a large variation between observers, particularly for difficult cases, where the tumor margins are not well delineated. The inter-observer variation for the measure- ment of the visual metric was only 16%, i.e., observers generally agreed on the lengths of the diameters. However, in 30% of the studied cases no consensus was found for the categorical tumor response measurement, indicating that the categories are very sensitive to variations in the diameter measurements. Moreover, the method failed to correctly identify the response in half of the cases. The data demonstrate that automatic 3D methods are clearly necessary for objective and clinically meaningful assessment of tumor volume in single or multicenter clinical trials. © 1998 Elsevier Science Inc. Keywords: MRI segmentation; Fuzzy clustering; Knowledge guided segmentation; Brain tumors; Therapy response. INTRODUCTION Magnetic resonance imaging (MRI) multispectral seg- mentation methods have been proposed for the determi- nation of the volume of normal brain tissues (e.g., white or gray matter), multiple sclerosis (MS) lesions, and tumor volume. 1–3 The advantages of multispectral meth- ods, as opposed to single image gray scale methods such as seed growing, are the potential ability to differentiate the tissues within the tumor bed to better delineate the active tumor margins as required for radiation treatment planning (RTP). 1,4–6 In this work we are interested in the measurement of the relative changes in tumor volume during therapy, or more specifically, tumor response measurements. 4–6 Tumor response measurement, how- ever, poses a very difficult segmentation problem com- pared to all other applications because the segmentation RECEIVED 6/30/97; ACCEPTED 11/04/97. Address correspondence to Laurence P. Clarke, Ph.D., Pro- fessor of Radiology and Physics, Department of Radiology, College of Medicine, 12901 Bruce B. Downs Blvd., MDC 17, Tampa, FL 33612-4799. E-mail: clarke@rad.usf.edu Magnetic Resonance Imaging, Vol. 16, No. 3, pp. 271–279, 1998 © 1998 Elsevier Science Inc. All rights reserved. Printed in the USA. 0730-725X/98 $19.00 + .00 271