Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1667-1670. doi: 10.1109/EMBC48229.2022.9871593.
Breast cancer remains the leading cause of cancer deaths and the second highest cause of death, in general, among women worldwide. Fortunately, over the last few decades, with the introduction of mammography, the mortality rate of breast cancer has significantly decreased. However, accurate classification of breast masses in mammograms is especially challenging. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. In this study, classification of benign and malignant masses, based on the subtraction of temporally sequential digital mammograms and machine learning, is proposed. The performance of the algorithm was evaluated on a dataset created for the purposes of this study. In total, 196 images from 49 patients, with precisely annotated mass locations and biopsy confirmed malignant cases, were included. Ninety-six features were extracted and five feature selection algorithms were employed to identify the most important features. Ten classifiers were tested using leave-one-patient-out and 7-fold cross-validation. Neural Networks, achieved the highest classification performance with 90.85% accuracy and 0.91 AUC, an improvement compared to the state-of-the-art. These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the classification of breast masses as benign or malignant.
乳腺癌仍然是全球女性癌症死亡的主要原因，也是总体上死亡的第二大原因。幸运的是，在过去几十年中，随着乳房 X 光摄影术的引入，乳腺癌的死亡率显著下降。然而，准确地对乳房肿块进行分类在乳房 X 光片中尤其具有挑战性。目前正在开发各种计算机辅助诊断（CAD）系统，以帮助放射科医生对乳房异常进行准确分类。在这项研究中，提出了基于时间序列数字乳房 X 光片减影和机器学习的良性和恶性肿块分类方法。该算法的性能是在为该研究目的创建的数据集上进行评估的。总共有 49 名患者的 196 张图像，这些图像具有精确标记的肿块位置和活检证实的恶性病例。提取了 96 个特征，并使用五种特征选择算法来识别最重要的特征。使用留一患者法和 7 折交叉验证测试了 10 种分类器。神经网络的分类性能最高，准确率为 90.85%，AUC 为 0.91，与现有技术相比有所提高。这些结果表明，时间序列连续乳房 X 光片减影对于良性或恶性乳房肿块的分类是有效的。
