Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, People's Republic of China.
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
J Digit Imaging. 2022 Aug;35(4):910-922. doi: 10.1007/s10278-019-00266-4.
This study performed and assessed a novel program to improve the accuracy of short-term breast cancer risk prediction by using information from craniocaudal (CC) and mediolateral-oblique (MLO) views of two breasts. An age-matched dataset of 556 patients with at least two sequential full-field digital mammography examinations was applied. In the second examination, 278 cases were diagnosed and pathologically verified as cancer, and 278 were negative, while all cases in the first examination were negative (not recalled). Two generalized linear-model-based risk prediction models were established with global- and local-based bilateral asymmetry features for CC and MLO views first. Then, a new fusion risk model was developed by fusing prediction results of the CC- and MLO-based risk models with an adaptive alpha-integration-based fusion method. The AUC of the fusion risk model was 0.72 ± 0.02, which was significantly higher than the AUC of CC- or MLO-based risk model (P < 0.05). The maximum odds ratio for CC- and MLO-based risk models were 8.09 and 5.25, respectively, and increased to 11.99 for the fusion risk model. For subgroups of patients aged 37-49 years, 50-65 years, and 66-87 years, the AUCs of 0.73, 0.71, and 0.75 for the fusion risk model were higher than AUC for CC- and MLO-based risk models. For the BIRADS 2 and 3 subgroups, the AUC values were 0.72 and 0.71 respectively for the fusion risk model which were higher than the AUC for the CC- and MLO-based risk models. This study demonstrated that the fusion risk model we established could effectively derive and integrate supplementary and useful information extracted from both CC and MLO view images and adaptively fuse them to increase the predictive power of the short-term breast cancer risk assessment model.
本研究提出并评估了一种新方案，旨在通过使用来自双侧头尾位（CC）和内外斜位（MLO）视图的信息，提高短期乳腺癌风险预测的准确性。该研究应用了一个年龄匹配的 556 例患者数据集，这些患者至少接受了两次全数字化乳腺摄影检查。在第二次检查中，278 例被诊断为癌症并经病理证实，278 例为阴性，而第一次检查中的所有病例均为阴性（未召回）。首先，基于双侧的全局和局部不对称性特征，建立了基于两个视图的全局和局部风险预测模型，然后采用自适应α融合方法融合了 CC 和 MLO 基础风险模型的预测结果，开发了一个新的融合风险模型。融合风险模型的 AUC 为 0.72±0.02，显著高于 CC 或 MLO 基础风险模型的 AUC（P<0.05）。CC 和 MLO 基础风险模型的最大优势比分别为 8.09 和 5.25，而融合风险模型则增加到 11.99。对于年龄在 37-49 岁、50-65 岁和 66-87 岁的患者亚组，融合风险模型的 AUC 分别为 0.73、0.71 和 0.75，高于 CC 和 MLO 基础风险模型的 AUC。对于 BI-RADS 2 和 3 亚组，融合风险模型的 AUC 值分别为 0.72 和 0.71，高于 CC 和 MLO 基础风险模型的 AUC 值。本研究表明，我们建立的融合风险模型可以有效地从 CC 和 MLO 视图图像中提取并整合补充和有用的信息，并自适应地融合这些信息，以提高短期乳腺癌风险评估模型的预测能力。
