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

OBJECTIVES: The 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 METHODS: This 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. RESULTS: The 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. CONCLUSIONS: In 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.

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Publication Details

Journal
Insights into Imaging
Published
2026-06-27
DOI
https://doi.org/10.1186/s13244-026-02342-0
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Yang Chen, Ye Gu, Y Huang, W T Tang et al.
Insights into Imaging
Radiomics and Machine Learning in Medical Imaging
article

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

Yang Chen, Ye Gu, Y Huang, W T Tang, Lizhi Xie, Jin Ye, Weijun Peng, Qin Xiao, Jie Shi, Jing Chen
article en

Abstract

OBJECTIVES: The 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 METHODS: This 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. RESULTS: The 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. CONCLUSIONS: In 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.

Insights into ImagingVol. 17(1)
Shanghai Medical College of Fudan University (CN), United States Air Force (US), Fudan University Shanghai Cancer Center (CN), Monash University (AU)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 6%
Radiomics and Machine Learning in Medical Imaging
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