School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China.
School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China; Research Centers for Biomedical Engineering, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China.
Comput Methods Programs Biomed. 2018 Jul;161:45-53. doi: 10.1016/j.cmpb.2018.04.008. Epub 2018 Apr 19.
Breast cancer is a leading cause of death from cancer for females. The high mortality rate of breast cancer is largely due to the complexity among invasive breast cancer and its significantly varied clinical outcomes. Therefore, improving the accuracy of breast cancer survival prediction has important significance and becomes one of the major research areas. Nowadays many computational models have been proposed for breast cancer survival prediction, however, most of them generate the predictive models by employing only the genomic data information and few of them consider the complementary information from pathological images.
In our study, we introduce a novel method called GPMKL based on multiple kernel learning (MKL), which efficiently employs heterogeneous information containing genomic data (gene expression, copy number alteration, gene methylation, protein expression) and pathological images. With above heterogeneous features, GPMKL is proposed to execute feature fusion which is embedded in breast cancer classification.
Performance analysis of the GPMKL model indicates that the pathological image information plays a critical part in accurately predicting the survival time of breast cancer patients. Furthermore, the proposed method is compared with other existing breast cancer survival prediction methods, and the results demonstrate that the proposed framework with pathological images performs remarkably better than the existing survival prediction methods.
All results performed in our study suggest that the usefulness and superiority of GPMKL in predicting human breast cancer survival.
乳腺癌是女性癌症死亡的主要原因。乳腺癌死亡率高主要是因为浸润性乳腺癌的复杂性及其临床表现差异较大。因此，提高乳腺癌生存预测的准确性具有重要意义，已成为主要研究领域之一。目前已经提出了许多用于乳腺癌生存预测的计算模型，但是大多数模型仅使用基因组数据信息生成预测模型，很少有模型考虑病理图像的补充信息。
在本研究中，我们引入了一种基于多核学习（MKL）的新方法 GPMKL，该方法有效地利用了包含基因组数据（基因表达、拷贝数改变、基因甲基化、蛋白表达）和病理图像的异构信息。利用上述异构特征，GPMKL 被提出用于嵌入乳腺癌分类中的特征融合。
GPMKL 模型的性能分析表明，病理图像信息在准确预测乳腺癌患者生存时间方面起着关键作用。此外，将所提出的方法与其他现有的乳腺癌生存预测方法进行比较，结果表明，具有病理图像的建议方法明显优于现有的生存预测方法。
我们研究中的所有结果都表明，GPMKL 在预测人类乳腺癌生存方面具有实用性和优越性。
