Jiang Mingkuan, Han Lu, Sun Hang, Li Jing, Bao Nan, Li Hong, Zhou Shi, Yu Tao
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, People's Republic of China.
Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, People's Republic of China.
Phys Med Biol. 2021 May 4;66(10). doi: 10.1088/1361-6560/abf38b.
Considering the complementarity of mammography and breast MRI, the research of feature fusion diagnosis based on cross-modality images was explored to improve the accuracy of breast cancer diagnosis. 201 patients with both mammography and breast MRI were collected retrospectively, including 117 cases of benign lesions and 84 cases of malignant ones. Two feature optimization strategies of sequential floating forward selection (SFFS), SFFS-1 and SFFS-2, were defined based on the sequential floating forward selection method. Each strategy was used to analyze the diagnostic performance of single-modality images and then to study the feature fusion diagnosis of cross-modality images. Three feature fusion approaches were compared: optimizing MRI features and then fusing those of mammography; optimizing mammography features and then fusing those of MRI; selecting the effective features from the whole feature set (mammography and MRI). Support vector machine, Naive Bayes, and K-nearest neighbor were employed as the classifiers and were finally integrated to get better performance. The average accuracy and area under the ROC curve (AUC) of MRI (88.56%, 0.9 for SFFS-1, 88.39%, 0.89 for SFFS-2) were better than mammography (84.25%, 0.84 for SFFS-1, 80.43%, 0.80 for SFFS-2). Furthermore, compared with a single modality, the average accuracy and AUC of cross-modality feature fusion can improve from 85.40% and 0.86 to 89.66% and 0.91. Classifier integration improved the accuracy and AUC from 90.49%, 0.92 to 92.37%, and 0.97. Cross-modality image feature fusion can achieve better diagnosis performance than a single modality. Feature selection strategy SFFS-1 has better efficiency than SFFS-2. Classifier integration can further improve diagnostic accuracy.
考虑到乳腺钼靶摄影和乳腺磁共振成像（MRI）的互补性，为提高乳腺癌诊断的准确性，开展了基于跨模态图像的特征融合诊断研究。回顾性收集了201例同时进行乳腺钼靶摄影和乳腺MRI检查的患者，其中良性病变117例，恶性病变84例。基于序列浮动前向选择方法定义了两种特征优化策略，即序列浮动前向选择-1（SFFS-1）和序列浮动前向选择-2（SFFS-2）。每种策略用于分析单模态图像的诊断性能，进而研究跨模态图像的特征融合诊断。比较了三种特征融合方法：先优化MRI特征，然后融合乳腺钼靶摄影特征；先优化乳腺钼靶摄影特征，然后融合MRI特征；从整个特征集（乳腺钼靶摄影和MRI）中选择有效特征。采用支持向量机、朴素贝叶斯和K近邻作为分类器，最后进行集成以获得更好的性能。MRI的平均准确率和ROC曲线下面积（AUC）（SFFS-1为88.56%，0.9；SFFS-2为88.39%，0.89）优于乳腺钼靶摄影（SFFS-1为84.25%，0.84；SFFS-2为80.43%，0.80）。此外，与单模态相比，跨模态特征融合的平均准确率和AUC可从85.40%和0.86提高到89.66%和0.91。分类器集成将准确率和AUC从90.49%、0.92提高到92.37%和0.97。跨模态图像特征融合比单模态能实现更好的诊断性能。特征选择策略SFFS-1比SFFS-2具有更高的效率。分类器集成可进一步提高诊断准确性。
