Li Guanghui, Xiao Lingli, Wang Guanying, Liu Ying, Liu Longzhong, Huang Qinghua
School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China.
Healthcare (Basel). 2023 Jul 13;11(14):2014. doi: 10.3390/healthcare11142014.
Breast cancer is one of the most prevalent cancers in women nowadays, and medical intervention at an early stage of cancer can significantly improve the prognosis of patients. Breast ultrasound (BUS) is a widely used tool for the early screening of breast cancer in primary care hospitals but it relies heavily on the ability and experience of physicians. Accordingly, we propose a knowledge tensor-based Breast Imaging Reporting and Data System (BI-RADS)-score-assisted generalized inference model, which uses the BI-RADS score of senior physicians as the gold standard to construct a knowledge tensor model to infer the benignity and malignancy of breast tumors and axes the diagnostic results against those of junior physicians to provide an aid for breast ultrasound diagnosis. The experimental results showed that the diagnostic AUC of the knowledge tensor constructed using the BI-RADS characteristics labeled by senior radiologists achieved 0.983 (95% confidential interval (CI) = 0.975-0.992) for benign and malignant breast cancer, while the diagnostic performance of the knowledge tensor constructed using the BI-RADS characteristics labeled by junior radiologists was only 0.849 (95% CI = 0.823-0.876). With the knowledge tensor fusion, the AUC is improved to 0.887 (95% CI = 0.864-0.909). Therefore, our proposed knowledge tensor can effectively help reduce the misclassification of BI-RADS characteristics by senior radiologists and, thus, improve the diagnostic performance of breast-ultrasound-assisted diagnosis.
乳腺癌是当今女性中最常见的癌症之一，癌症早期的医学干预可以显著改善患者的预后。乳腺超声（BUS）是基层医院广泛用于乳腺癌早期筛查的工具，但它严重依赖医生的能力和经验。因此，我们提出了一种基于知识张量的乳腺影像报告和数据系统（BI-RADS）评分辅助广义推理模型，该模型以 senior 医生的 BI-RADS 评分作为金标准构建知识张量模型，以推断乳腺肿瘤的良恶性，并将诊断结果与 junior 医生的结果进行对比，为乳腺超声诊断提供辅助。实验结果表明，使用 senior 放射科医生标注的 BI-RADS 特征构建的知识张量对乳腺良恶性肿瘤的诊断 AUC 达到 0.983（95%置信区间（CI）=0.975-0.992），而使用 junior 放射科医生标注的 BI-RADS 特征构建的知识张量的诊断性能仅为 0.849（95%CI = 0.823-0.876）。通过知识张量融合，AUC 提高到 0.887（95%CI = 0.864-0.909）。因此，我们提出的知识张量可以有效帮助减少 senior 放射科医生对 BI-RADS 特征的误分类，从而提高乳腺超声辅助诊断的性能。
