Tumor Digital Masking Allows Precise Patient Triaging: A Study Based on Ki-67 Scoring in Gastrointestinal Stromal Tumors

Abstract

Background: Technological advances constantly provide cutting-edge tools that enhance the progress of diagnostic capabilities. Gastrointestinal stromal tumors belong to a family of mesenchymal tumors where patient triaging is still based on traditional criteria such as mitotic count, tumor size, and tumor location. Limitations of the human eye and randomness in choice of area for mitotic figure counting compel us to seek more objective solutions such as digital image analysis. Presently, the labelling of proliferative activity is becoming a routine task amidst many cancers. The purpose of the present study was to compare the traditional method of prediction based on mitotic ratio with digital image analysis of cell cycle-dependent proteins.

Methods: Fifty-seven eligible cases were enrolled. Furthermore, a digital analysis of previously performed whole tissue section immunohistochemical assays was executed. Digital labelling covered both hotspots and not-hotspots equally.

Results: We noted a significant diversity of proliferative activities, and consequently, the results pointed to 6.5% of Ki-67, counted in hotspots, as the optimal cut-off for low-high-grade GIST. ROC analysis (AUC = 0.913; 95% CI: 0.828-0.997, p < 0.00001) and odds ratio (OR = 40.0, 95% CI: 6.7-237.3, p < 0.0001) pointed to Ki-67 16% as the cut-off for very high-grade (groups 5-6) cases. With help of a tumor digital map, we revealed possible errors resulting from a wrong choice of field for analysis. We confirmed that Ki-67 scores are in line with the level of intracellular metabolism that could be used as the additional biomarker.

Conclusions: Tumor digital masking is very promising solution for repeatable and objective labelling. Software adjustments of nuclear shape, outlines, size, etc. are helpful to omit other Ki-67-positive cells especially small lymphocytes. Our results pointed to Ki-67 as a good biomarker in GIST, but concurrently, we noted significant differences in used digital approaches which could lead to unequivocal results.

Figure 1

Tercile analysis depicting progress of the Ki-67 score with higher risk of recurrence.

Figure 2

The Kaplan-Meier diagram for RFS and 10% Ki-67 cut-off value.

Figure 3

A compilation of Ki-67 labelling with concurrent digital masking. Yellow dots represent strong Ki-67 expression, red dots represent weak expression and blue points correspond to the negative results.