Cytological malignancy grading systems for fine needle aspiration biopsies of breast cancer

A prime factor deciding the survival rate of a breast cancer patient is the accuracy with which the malignancy grade of a breast tumor is determined. A Fine Needle Aspiration (FNA) biopsy is a key mechanism for breast cancer diagnosis as well as for assigning grades to malignant cases. In this paper, based on published cytological malignancy grading systems, we propose six computer-aided grading frameworks to assign malignancy grades to cytological images of FNA biopsies of breast cancer. The proposed computer-aided grading frameworks were tested on 332 FNA biopsy images composed of 66 images with high malignancy (G3) and 266 images with intermediate malignancy (G2) that were histopathologically validated using the Bloom-Richardson grading system. The best results were obtained for the Support Vector Machine classifier for computer-aided versions of the Robinson's and Khan et al.'s cytological grading systems with accuracies of 97.57% and 96.98% for case classification (where a case is a pair of 100× and 400× magnification images for a patient) and 95.23% and 98.36% for patient classification, respectively.

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