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Volume 26
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10.3390/ijms262311624
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer

by
Qingpei Lai
1,†,
Xinzhi Teng
1,†,
Jiang Zhang
1,
Xinyu Zhang
1,
Yufeng Jiang
1,
Yao Pu
1,
Peixin Yu
1,
Wen Li
1,
Tian Li
1,
Jing Cai
1,2 and
Ge Ren
1,*
1
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
2
The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2025, 26(23), 11624; https://doi.org/10.3390/ijms262311624
Submission received: 28 October 2025 / Revised: 27 November 2025 / Accepted: 28 November 2025 / Published: 30 November 2025
(This article belongs to the Section Molecular Informatics)
Versions Notes

Abstract

This study addresses the limited mechanistic understanding behind medical imaging for tumor microenvironment (TME) assessment. We developed a novel framework that analyzes tumor imaging heterogeneity index (TIHI)-correlated genes to uncover underlying TME biology and therapeutic vulnerabilities. DCE-MRI and mRNA data from 987 high-risk breast cancer patients in the I-SPY2 trial, together with mRNA data from 508 patients in GSE25066, were analyzed. TIHI-associated genes were identified via Pearson correlation, clustered via weighted gene co-expression network analysis (WGCNA), and subgroups were defined via non-negative matrix factorization (NMF). The clinical relevance of the image-to-gene comprehensive (I2G-C) subtype defined by subgroups was assessed using logistic regression and Cox analysis. I2G-C comprised four clusters with distinct immune and replication/repair functions. It further stratified receptor, PAM50, and RPS5 subtypes. The “immune+/replication+” was more likely to achieve pathological complete response (pCR) (OR = 2.587, p < 0.001), while the “immune−/replication−” was the least likely to achieve pCR (OR = 0.402, p < 0.001). The “immune+/replication+” showed sensitivity to pembrolizumab (OR = 10.192, p < 0.001) and veliparib/carboplatin (OR = 5.184, p = 0.006), while “immune-/replication-” responded poorly to pembrolizumab (OR = 0.086, p < 0.001). Additionally, “immune+/replication-” had the best distant recurrence-free survival (DRFS), whereas “immune-/replication+” had the worst (log-rank p = 6 × 10−4, HR = 5.45). By linking imaging heterogeneity directly to molecular subtypes and therapeutic response, this framework provides a robust, non-invasive surrogate for genomic profiling and a strategic tool for personalized neoadjuvant therapy selection.
Keywords: breast cancer; tumor imaging heterogeneity index; tumor microenvironment; image-to-gene comprehensive subtype breast cancer; tumor imaging heterogeneity index; tumor microenvironment; image-to-gene comprehensive subtype

Share and Cite

MDPI and ACS Style

Lai, Q.; Teng, X.; Zhang, J.; Zhang, X.; Jiang, Y.; Pu, Y.; Yu, P.; Li, W.; Li, T.; Cai, J.; et al. Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer. Int. J. Mol. Sci. 2025, 26, 11624. https://doi.org/10.3390/ijms262311624

AMA Style

Lai Q, Teng X, Zhang J, Zhang X, Jiang Y, Pu Y, Yu P, Li W, Li T, Cai J, et al. Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer. International Journal of Molecular Sciences. 2025; 26(23):11624. https://doi.org/10.3390/ijms262311624

Chicago/Turabian Style

Lai, Qingpei, Xinzhi Teng, Jiang Zhang, Xinyu Zhang, Yufeng Jiang, Yao Pu, Peixin Yu, Wen Li, Tian Li, Jing Cai, and et al. 2025. "Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer" International Journal of Molecular Sciences 26, no. 23: 11624. https://doi.org/10.3390/ijms262311624

APA Style

Lai, Q., Teng, X., Zhang, J., Zhang, X., Jiang, Y., Pu, Y., Yu, P., Li, W., Li, T., Cai, J., & Ren, G. (2025). Tumor Imaging Heterogeneity Index-Inspired Insights into the Unveiling Tumor Microenvironment of Breast Cancer. International Journal of Molecular Sciences, 26(23), 11624. https://doi.org/10.3390/ijms262311624

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