Abstract
Introduction. The ability to reliably differentiate neoplastic from nonneoplastic specimen and ascertain the tumour grade of diffusely infiltrating gliomas (DIGs) is often challenging. Aims and Objective. To evaluate utility of image morphometry in identifying DIG areas and to predict tumour grade. Materials and Methods. Image morphometry was used to analyze the following nuclear features of 30 DIGs and 10 controls (CG): major axis of nucleus (MAJX), minor axis of nucleus (MINX), nuclear area (NA), nuclear perimeter (NP), nuclear roundness (NR), nuclear density (ND), and percentage of total nuclear area (%TNA). Results. Statistically significant differences in all parameters, except NR, were observed between all groups, with strong positive correlation with tumour grade (r > 0.7). The mean values were maximum for HGG and minimum for CG. For NR, the difference between CG/HGG was statistically significant, unlike CG/LGG and LGG/HGG. It was observed that NA distributions for CG were nearly Gaussian type with smaller range, while gliomas displayed erratic pattern with larger range. NA and NP exhibited strong positive correlation with ND. Conclusion. Image morphometry has immense potential in being a powerful tool to distinguish normal from neoplastic tissue and also to differentiate LGG from HGG cases, especially in tiny stereotactic biopsies.
Highlights
The ability to reliably differentiate neoplastic from nonneoplastic specimen and ascertain the tumour grade of diffusely infiltrating gliomas (DIGs) is often challenging
The mean values of major axis of nucleus (MAJX), minor axis of nucleus (MINX), nuclear area (NA), nuclear perimeter (NP), nuclear roundness (NR), nuclear density (ND), and %TNA of the three groups were represented in Figures 3(a)– 3(g) respectively; the range of the parameters for each group was presented as error bar in the figures
Using the regression analysis of survival data, they observed that the best predictors for patient outcome were grading, as observed on histology, and nuclear area percentage, as evaluated by image analysis [12]
Summary
The ability to reliably differentiate neoplastic from nonneoplastic specimen and ascertain the tumour grade of diffusely infiltrating gliomas (DIGs) is often challenging. To evaluate utility of image morphometry in identifying DIG areas and to predict tumour grade. Image morphometry was used to analyze the following nuclear features of 30 DIGs and 10 controls (CG): major axis of nucleus (MAJX), minor axis of nucleus (MINX), nuclear area (NA), nuclear perimeter (NP), nuclear roundness (NR), nuclear density (ND), and percentage of total nuclear area (%TNA). Significant differences in all parameters, except NR, were observed between all groups, with strong positive correlation with tumour grade (r > 0.7). To date only few studies have utilized nuclear morphometric measurements, like mean major axis (MAJX), minor axis (MINX), nuclear area (NA), nuclear perimeter (NP), and roundness of nucleus (NR), to determine the nuclear size and shape profiles in neoplastic tissues in CNS tumors [6,7,8,9,10,11,12]. The initial four parameters, namely, MAJX, MINX, NA, and NP, are related to nuclear size (hypertrophy), while NR is related to nuclear shape
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