Lin Qing, Tan Wei-Min, Ge Jing-Yu, Huang Yan, Xiao Qin, Xu Ying-Ying, Jin Yi-Ting, Shao Zhi-Ming, Gu Ya-Jia, Yan Bo, Yu Ke-Da
Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Fudan University, Shanghai 200438, China.
Fundam Res. 2023 Jun 18;5(2):880-889. doi: 10.1016/j.fmre.2023.04.018. eCollection 2025 Mar.
Mammography is the mainstream imaging modality used for breast cancer screening. Identification of microcalcifications associated with malignancy may result in early diagnosis of breast cancer and aid in reducing the morbidity and mortality associated with the disease. Computer-aided diagnosis (CAD) is a promising technique due to its efficiency and accuracy. Here, we demonstrated that an automated deep-learning pipeline for microcalcification detection and classification on mammography can facilitate early diagnosis of breast cancer. This technique can not only provide the classification results of mammography, but also annotate specific calcification regions. A large mammography dataset was collected, including 4,810 mammograms with 6,663 microcalcification lesions based on biopsy results, of which 3,301 were malignant and 3,362 were benign. The system was developed and tested using images from multiple centers. The overall classification accuracy values for discriminating between benign and malignant breasts were 0.8124 for the training set and 0.7237 for the test set. The sensitivity values of malignant breast cancer prediction were 0.8891 for the training set and 0.7778 for the test set. In addition, we collected information regarding pathological sub-type (pathotype) and estrogen receptor (ER) status, and we subsequently explored the effectiveness of deep learning-based pathotype and ER classification. Automated artificial intelligence (AI) systems may assist clinicians in making judgments and improve their efficiency in breast cancer screening, diagnosis, and treatment.
乳腺钼靶摄影是用于乳腺癌筛查的主流成像方式。识别与恶性肿瘤相关的微钙化可能有助于乳腺癌的早期诊断，并有助于降低与该疾病相关的发病率和死亡率。计算机辅助诊断（CAD）因其效率和准确性而成为一种很有前景的技术。在此，我们证明了一种用于乳腺钼靶摄影微钙化检测和分类的自动化深度学习流程能够促进乳腺癌的早期诊断。该技术不仅可以提供乳腺钼靶摄影的分类结果，还能标注特定的钙化区域。我们收集了一个大型乳腺钼靶摄影数据集，根据活检结果，其中包括4810幅乳腺钼靶图像以及6663个微钙化病灶，其中3301个为恶性，3362个为良性。该系统使用来自多个中心的图像进行开发和测试。区分良性和恶性乳房的总体分类准确率在训练集中为0.8124，在测试集中为0.7237。恶性乳腺癌预测的灵敏度在训练集中为0.8891，在测试集中为0.7778。此外，我们收集了有关病理亚型（病理类型）和雌激素受体（ER）状态的信息，随后我们探讨了基于深度学习的病理类型和ER分类的有效性。自动化人工智能（AI）系统可以协助临床医生进行判断，并提高他们在乳腺癌筛查、诊断和治疗方面的效率。
