Dongdong Sun1, Ao Li2, Bo Tang3, Minghui Wang4. 1. School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China. Electronic address: sddchina@mail.ustc.edu.cn. 2. 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. Electronic address: aoli@ustc.edu.cn. 3. School of Information Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei 230027, China. Electronic address: tb214200@mail.ustc.edu.cn. 4. 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. Electronic address: mhwang@ustc.edu.cn.
Abstract
BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: All results performed in our study suggest that the usefulness and superiority of GPMKL in predicting human breast cancer survival.
BACKGROUND AND OBJECTIVE: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. METHODS: 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. RESULTS: Performance analysis of the GPMKL model indicates that the pathological image information plays a critical part in accurately predicting the survival time of breast cancerpatients. 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. CONCLUSIONS: All results performed in our study suggest that the usefulness and superiority of GPMKL in predicting humanbreast cancer survival.
Authors: Jessica Gliozzo; Marco Mesiti; Marco Notaro; Alessandro Petrini; Alex Patak; Antonio Puertas-Gallardo; Alberto Paccanaro; Giorgio Valentini; Elena Casiraghi Journal: Brief Bioinform Date: 2022-07-18 Impact factor: 13.994