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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Cytological malignancy grading systems for fine needle aspiration biopsies of breast cancer
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Cytological malignancy grading systems for fine needle aspiration biopsies of breast cancer

机译：乳腺癌细针穿刺活组织检查的细胞学恶性分级系统

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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.
机译：决定乳腺癌患者的存活率的主要因素是确定乳腺肿瘤的恶性等级的准确性。精细针吸入（FNA）活检是乳腺癌诊断的关键机制，以及分配对恶性病例的成绩。本文基于已发表的细胞学恶性分级系统，我们提出了六种计算机辅助评分框架，将恶性等级分配给乳腺癌FNA活检的细胞学图像。在332个FNA活检图像上测试了所提出的计算机辅助分级框架，该图像由具有高恶性（G3）和266个图像的66个图像组成，所述中间恶性肿瘤（G2）使用Bloom-Richardson分级系统进行组织病理学验证。用于罗宾逊和汗等人的计算机辅助版本的支持向量机分类器获得最佳结果。案例分类的精度为97.57 ％和96.98 ％的细胞学分级系统（其中案例是一对的对于患者的100×和400倍倍率图像分别为患者分类的95.23 ％和98.36 ％。

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