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

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

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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.

IMAGGSRadiogenomic framework"Image-gene-gene set"associationsMolecular subtypesBreast cancer

Shuyu Liang、Sicheng Xu、Shichong Zhou、Cai Chang、Zhiming Shao、Yuanyuan Wang、Sheng Chen、Yunxia Huang、Yi Guo

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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

Shanghai Key Laboratory of Metabolic Remodeling and Health,Institute of Metabolism and Integrative Biology,Fudan University,Shanghai 200433,China

Department of Ultrasound,Fudan University Shanghai Cancer Center,Department of Oncology,Shanghai Medical College,Fudan University,Shanghai 200032,China

Department of Breast Surgery,Fudan University Shanghai Cancer Center,Department of Oncology,Shanghai Medical College,Fudan University,Shanghai 200032,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaScience and Technology Commission of Shanghai Municipality

818300588207194591959207921593018230221222ZR1404800

2024

10.1016/j.jgg.2023.09.010

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

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CSTPCD
影响因子：0.821
ISSN：1673-8527
年,卷(期)：2024.51(4)
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