Clarke L P, Velthuizen R P, Clark M, Gaviria J, Hall L, Goldgof D, Murtagh R, Phuphanich S, Brem S
Department of Radiology, College of Medicine, University of South Florida, and the H. Lee Moffitt Cancer and Research Institute, Tampa 33612-4799, USA.
Magn Reson Imaging. 1998 Apr;16(3):271-9. doi: 10.1016/s0730-725x(97)00302-0.
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 multispectral 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 T1 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 measurement 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.
比较了一种自动磁共振成像（MRI）多光谱分割方法和一种视觉指标在测量肿瘤治疗反应有效性方面的表现。自动反应测量对于多中心临床试验至关重要。一种视觉指标，如肿瘤最大直径与最大垂直直径的乘积，是一种标准方法，目前用于放射治疗肿瘤学组（RTOG）和东部肿瘤协作组（EGOG）的临床试验。在标准方法中，肿瘤反应基于视觉指标的百分比变化，并分为治愈、部分反应、疾病稳定或进展。将视觉和自动方法应用于六个分割难度不同的脑肿瘤病例（胶质瘤）。分析的数据是使用磁共振对比增强收集的系列多光谱MR图像。一种全自动知识引导方法（KG）应用于MRI多光谱数据，而视觉指标则从使用T1钆增强图像的MRI胶片中获取，由两名放射科医生和两名住院医生进行重复测量。将视觉和自动方法的肿瘤测量结果与“真实情况”（GT），即手动分割的肿瘤进行比较。发现KG方法略微高估了肿瘤体积，但方式一致，并且估计的肿瘤反应与手绘真实情况相比非常好，相关系数为0.96。相比之下，视觉估计指标在观察者之间存在很大差异，特别是对于困难病例，肿瘤边缘没有很好地勾勒出来。视觉指标测量的观察者间差异仅为16%，即观察者通常对直径长度达成一致。然而，在30%的研究病例中，对于分类肿瘤反应测量未达成共识，这表明这些类别对直径测量的变化非常敏感。此外，该方法在一半的病例中未能正确识别反应。数据表明，在单中心或多中心临床试验中，自动3D方法对于客观和临床有意义地评估肿瘤体积显然是必要的。
