遗传学报2024，Vol.51Issue(4) ：443-453.DOI:10.1016/j.jgg.2023.09.010

IMAGGS:a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

Shuyu Liang Sicheng Xu Shichong Zhou Cai Chang Zhiming Shao Yuanyuan Wang Sheng Chen Yunxia Huang Yi Guo
遗传学报2024，Vol.51Issue(4) ：443-453.DOI:10.1016/j.jgg.2023.09.010
✕
  • NETL
  • NSTL
  • 万方数据

IMAGGS:a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

Shuyu Liang 1Sicheng Xu 2Shichong Zhou 3Cai Chang 3Zhiming Shao 4Yuanyuan Wang 1Sheng Chen 4Yunxia Huang 3Yi Guo1
扫码查看

作者信息

  • 1. Department of Electronic Engineering,School of Information Science and Technology,Fudan University,Shanghai 200433,China;The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI)of Shanghai,Shanghai 200032,China
  • 2. Shanghai Key Laboratory of Metabolic Remodeling and Health,Institute of Metabolism and Integrative Biology,Fudan University,Shanghai 200433,China
  • 3. Department of Ultrasound,Fudan University Shanghai Cancer Center,Department of Oncology,Shanghai Medical College,Fudan University,Shanghai 200032,China
  • 4. Department of Breast Surgery,Fudan University Shanghai Cancer Center,Department of Oncology,Shanghai Medical College,Fudan University,Shanghai 200032,China
  • 折叠

Abstract

Investigating correlations between radiomic and genomic profiling in breast cancer(BC)molecular sub-types is crucial for understanding disease mechanisms and providing personalized treatment.We present a well-designed radiogenomic framework image-gene-gene set(IMAGGS),which detects multi-way as-sociations in BC subtypes by integrating radiomic and genomic features.Our dataset consists of 721 patients,each of whom has 12 ultrasound(US)images captured from different angles and gene mutation data.To better characterize tumor traits,12 multi-angle US images are fused using two distinct strategies.Then,we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm,deviating from the prevalent one-to-one relationship pattern observed in previous studies.Key radiomic and genomic features are screened using these associations.In addition,gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes.We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases.Taken together,IMAGGS provides a comprehensive characterization for diseases by associating imaging,genes,and gene sets,paving the way for biological interpretation of radiomics and development of targeted therapy.

Key words

IMAGGS/Radiogenomic framework/"Image-gene-gene set"associations/Molecular subtypes/Breast cancer

引用本文复制引用

基金项目

National Natural Science Foundation of China(81830058)

National Natural Science Foundation of China(82071945)

National Natural Science Foundation of China(91959207)

National Natural Science Foundation of China(92159301)

National Natural Science Foundation of China(82302212)

Science and Technology Commission of Shanghai Municipality(22ZR1404800)

出版年

2024
遗传学报
中国遗传学会 中国科学院遗传与发育生物学研究所

遗传学报

CSTPCDCSCD
影响因子：0.821
ISSN：1673-8527
参考文献量53
段落导航相关论文