An improved procedure to quantify tumour vascularity using true colour image analysis. Comparison with the manual hot-spot procedure in a human melanoma xenograft model.

Abstract

In a number of recent papers, the degree of tumour vascularization has been described as a promising new prognostic factor. Methods for the assessment of vascular density involve immunohistochemical staining of the vasculature, followed by counting the number of vessel profiles in the angiogenic hot spot. One of the problems of this procedure is the selection of the angiogenic hot spot, which has been described as being subject to inter-observer variation. In this study, the value of true colour image analysis in reducing inter-observer variation has been assessed. Highly (MV3) and poorly (M14) vascularized human melanoma xenografts were used to evaluate the image analysis procedure, and the image analysis results were compared with results from the conventional manual hot-spot procedure. Assessment by image analysis was performed on measurement fields covering the entire tumour tissue specimens rather than on a single hot-spot field. Also, by selecting the most densely vascularized area from all fields assessed by the semi-automatic procedure, it was possible to objectify the hot spot selection (automated hot-spot procedure). Manual assessment showed a good correlation between two independent observers for MV3 xenografts (r = 0.74, P = 0.014), but a poor correlation for M14 xenographs (r = 0.4, P > 0.05). Automated assessment by different operators showed good correlations for both MV3 xenografts (r = 0.99, P < 0.001) and M14 xenografts (r = 0.80, P = 0.006). It is concluded that although both manual vessel counting and semi-automated image analysis can differentiate between the level of vascularization in the two types of xenograft (P < 0.001 for both methods), the automated method is favourable in that it showed no significant inter-observer effects. In M14 xenografts, the manual hot-spot vessel densities did not correlate well with the automated hot-spot densities (r = 0.27, P > 0.05), indicating that selection of angiogenic hot spots in this tumour type is indeed subject to observer bias. The automated hot-spot vessel densities were a reliable indicator of overall tumour vessel density in both tumour types. Image analysis allows analysis of vessel subclasses based on morphological criteria such as vessel profile area or diameter. In the model system used, the discrimination between MV3 and M14 xenografts was further enhanced by selectively examining vessels with diameters between 6 and 9 microns (P < 0.0005). In conclusion, image analysis appears to offer an objective and more reproducible method to quantify tumour vascularity than manual counting of vessel profiles in the hot spot. Analysis of subclasses of vessels may further enhance the value of vessel density measurements in discriminating between tumour types differing in biological behaviour.