Evidence map›Paper›PMID 42365208›Full record

ArticleInsights into imaging2026

MRI-based quantification of intratumoral heterogeneity for predicting recurrence risk in ER+/HER2- breast cancer.

Yang Chen, Jie Shi, Jing Chen, Lizhi Xie, Jin Ye, Wei Tang, Qin Xiao, Yan Huang, Yajia Gu, Weijun Peng

Abstract read
In one paragraph

Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Yang Chen *Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Jie Shi *MR Research China, GE Healthcare, Beijing, China.
Jing Chen *Department of Ultrasound, Secondary Sanatorium of Air Force Healthcare Center for Special Services, Hangzhou, China.
Lizhi XieMR Research China, GE Healthcare, Beijing, China.
Jin YeDepartment of Data Science & AI, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
Wei TangDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Qin XiaoDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Yan HuangDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Yajia GuDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China. guyajia@shca.org.cn.
Weijun PengDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China. weijun_2020@163.com.ORCID http://orcid.org/0000-0002-0117-7559

Funding

National Natural Science Foundation of China NSFC82430061
6 · The paper itself

Abstract

objectivesThe need for a cost-effective, rapid, and increasingly accessible alternative to the 21-gene assay prompted this study, which developed a novel MRI-based intratumoral heterogeneity score (ITHscore) to quantify tumor heterogeneity and integrated it with radiomic and clinical features to predict the 21-gene recurrence score (RS). MATERIALS AND

methodsThis retrospective study included ER+/HER2- breast cancer patients who underwent 21-gene assay and preoperative MRI at our institution (April 2017-March 2019). Patients were randomly split into training (70%) and internal test (30%) cohorts, with an external test cohort from the public Duke-Breast-Cancer-MRI dataset. Tumor volumes of interest were automatically segmented using a pre-trained Scalable and Transferable U-Net framework, followed by k-means clustering to compute the ITHscore. Predictive models for the RS were built using clinical, radiomics, and ITHscore features with the support vector machine method, and evaluated by receiver operating characteristic curves.

resultsThe institutional dataset comprised 452 patients (training: 316 (187 high-risk, 129 low-risk); internal test: 136 (80 high-risk, 56 low-risk)), while the external Duke cohort included 230 patients (44 high-risk, 186 low-risk). The ITHscore was significantly elevated in high-risk patients (p < 0.001), and its incorporation into the clinical-radiomic model improved RS prediction, yielding AUCs of 0.86 for the internal test cohort and 0.82 for the external test cohort.

conclusionsIn this exploratory study, the ITHscore, which held promise as a noninvasive and intuitive means of characterizing intratumoral heterogeneity, demonstrated a potential incremental value for predicting RS in patients with ER+/HER2- breast cancer. CRITICAL RELEVANCE STATEMENT: The MRI-derived quantification of intratumoral heterogeneity facilitates simple and quantitative assessment of tumor heterogeneity and demonstrates potential incremental value in providing a rapid, cost-effective, and accessible prediction of the recurrence score in ER+/HER2- breast cancer. KEY POINTS: There is a need for a cost-effective, rapid, and increasingly accessible alternative to the 21-gene assay for predicting recurrence risk in ER+/HER2- breast cancer. The intratumoral heterogeneity score demonstrated potential as a noninvasive, intuitive, and quantitative biomarker for characterizing intratumoral heterogeneity and predicting recurrence score.

Indexed as

Artificial intelligenceBreast neoplasmsMagnetic resonance imagingNeoplasm recurrenceRadiomics

Identifiers

PMID42365208
PMCPMC13310210

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