In 1979, the World Health Organization (WHO) established criteria based on tumour volume change for classifying response to therapy as (i) progressive disease (PD), (ii) partial recovery (PR), and (iii) no change (NC). Typically, the tumour volume is reported from diameter measurements, using the calliper method. Alternatively, the Cavalieri method provides unbiased volume estimates of any structure without assumptions about its shape. In this study, we applied the Cavalieri method in combination with point counting to investigate the changes in tumour volume in four patients with high grade glioma, using 3D MRI. In particular, the volume of tumour within the enhancement boundary, the enhancing abnormality (EA), was estimated fromT1weighted images, and the volume of the non-enhancing abnormality, (NEA) enhancing abnormality, was estimated fromT2relaxation time and magnetic transfer ratio tissue characterization maps. We compared changes in tumour volume estimated by the Cavalieri method with those obtained using the calliper method. Absolute tumour volume differed significantly between the two methods. Analysis of relative change in tumour volume, based on the WHO criteria, provided a different classification using the calliper and Cavalieri methods. The benefit of the Cavalieri method over the calliper method in the estimation of tumour volume is justified by the following factors. First, Cavalieri volume estimates are mathematically unbiased. Second, the Cavalieri method is highly efficient under an appropriate sampling density (i.e.EA volume estimates can be obtained with a coefficient of error no higher than 5% in 2–3 min). Third, the source of variation of the volume estimates due to disagreements between observers, and within observer, is much greater in the positioning of the calliper diameters than in the identification of the tumour boundaries when applying the Cavalieri method. Additionally, the error prediction formula, available to estimate the coefficient of error of Cavalieri volume estimates from the data, allows us to establish more precise classification criteria against which to identify potentially clinical significant changes in tumour volume.