Breast cancer molecular subtyping is fundamental to precision oncology, yet current clinical practice depends on invasive tissue sampling that inherently fails to capture complete tumor heterogeneity. The convergence of radiomics and deep learning with multiparametric magnetic resonance imaging presents a transformative pathway for non-invasive molecular characterization. This review systematically examines the methodological evolution, current capabilities, and translational trajectory of magnetic resonance imaging (MRI)-based radiomics and deep learning for breast cancer molecular subtype prediction, with particular emphasis on computational challenges and their solutions. We synthesize evidence across four thematic domains: (1) Conventional radiomics feature engineering. (2) Deep learning architecture and transfer learning. (3) Integrated multimodal frameworks. (4) Emerging paradigms include self-supervised learning and mechanistically interpretable models. Our analysis demonstrates that multimodal approaches consistently outperform single-modality models, achieving area under the curve values of 0.850 to 0.960 for luminal versus non-luminal discrimination, 0.840 to 0.970 for human epidermal growth factor receptor 2 (HER2) status prediction, and 0.860 to 0.980 for triple-negative breast cancer identification in recent multicenter studies. Nevertheless, substantial challenges persist in data standardization, model generalizability, and clinical deployment; these remain the primary barriers to widespread adoption. From a computational perspective, this review identifies that: (1) Transfer learning and self-supervised learning are essential for overcoming data scarcity. (2) Intermediate fusion strategies offer the optimal balance between performance and interpretability for multimodal integration. (3) Current explainable AI methods, while valuable, provide post-hoc explanations rather than inherently interpretable reasoning, which limit clinical trust. Future research must prioritize robust external validation, development of inherently interpretable models, and exploration of foundation models to accelerate clinical translation. By systematically mapping computational challenges and solutions, this review establishes a methodological roadmap for developing clinically trustworthy deep learning radiomics models in breast cancer.
References
[1] Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., Jemal, A. (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229-263.
[2] Yu, Z., Ma, W., Zhang, Y., Gao, Y., Jie, H., Jiang, J., Huang, C. (2025) Advances in DCE-MRI radiomics for non-invasive prediction of breast cancer molecular subtypes: research progress and clinical translation. Breast Cancer: Targets and Therapy, 1111-1118.
[3] Allison, K. H., Hammond, M. E. H., Dowsett, M., McKernin, S. E., Carey, L. A., Fitzgibbons, P. L., Perlmutter, J. (2020) Estrogen and progesterone receptor testing in breast cancer: ASCO/CAP guideline update. Journal of Clinical Oncology, 38(12), 1346-1366.
[4] Rossi, C., Fraticelli, S., Fanizza, M., Ferrari, A., Ferraris, E., Messina, A., Lucioni, M. (2023) Concordance of immunohistochemistry for predictive and prognostic factors in breast cancer between biopsy and surgical excision: a single-centre experience and review of the literature. Breast Cancer Research and Treatment, 198(3), 573-582.
[5] Xu, M., Yan, Y., Chen, Y., Chen, X., Gong, K., Fu, F. (2025) Expression and subtype discordance between core needle biopsy and surgical specimen in breast cancer. Journal of Surgical Research, 307, 42-52.
[6] Mann, R. M., Cho, N., Moy, L. (2019) Breast MRI: state of the art. Radiology, 292(3), 520-536.
[7] Ma, W., Yang, L., Zhang, Y., Gao, Y., Jie, H., Huang, C. (2025) Heterogeneity assessment of breast cancer tumor microenvironment: multiparametric quantitative analysis with DCE-MRI and discovery of radiomics biomarkers. Breast Cancer: Targets and Therapy, 573-581.
[8] Partridge, S. C., Nissan, N., Rahbar, H., Kitsch, A. E., Sigmund, E. E. (2017) Diffusion‐weighted breast MRI: Clinical applications and emerging techniques. Journal of Magnetic Resonance Imaging, 45(2), 337-355.
[9] Uematsu, T. (2015) Focal breast edema associated with malignancy on T2-weighted images of breast MRI: peritumoral edema, prepectoral edema, and subcutaneous edema. Breast Cancer, 22(1), 66-70.
[10] Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J., Walsh, S. (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology, 14(12), 749-762.
[11] Gillies, R. J., Kinahan, P. E., Hricak, H. (2016) Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563-577.
[12] Bengio, Y., Lecun, Y., Hinton, G. (2021) Deep learning for AI. Communications of the ACM, 64(7), 58-65.
[13] Parekh, V. S., Jacobs, M. A. (2019) Deep learning and radiomics in precision medicine. Expert Review of Precision Medicine and Drug Development, 4(2), 59-72.
[14] Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., Aerts, H. J. (2018) Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510.
[15] Wang, Z., Wei, S. (2025) Diagnosing breast cancer subtypes using MRI radiomics and machine learning: a systematic review. Journal of Radiation Research and Applied Sciences, 18(1), 101260.
[16] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Moher, D. (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. British Medical Journal, 372.
[17] Majidpour, J., Beitollahi, H. (2025) Multi-objective feature selection of radiomics and deep learning features for breast cancer subtype detection: J. Majidpour and H. Beitollahi. The Journal of Supercomputing, 81(14), 1330.
[18] Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J., Andrearczyk, V., Apte, A., Löck, S. (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2), 328-338.
[19] Sollini, M., Cozzi, L., Antunovic, L., Chiti, A., Kirienko, M. (2017) PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Scientific Reports, 7(1), 358.
[20] Han, Y., Huang, M., Xie, L., Cao, Y., Dong, Y. (2025) The value of intratumoral and peritumoral radiomics features based on multiparametric MRI for predicting molecular staging of breast cancer. Frontiers in Oncology, 15, 1379048.
[21] Wang, J., Kato, F., Oyama-Manabe, N., Li, R., Cui, Y., Tha, K. K., Shirato, H. (2015) Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: a pilot radiomics study. PloS one, 10(11), e0143308.
[22] Mayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., Cook, G. (2020) Introduction to radiomics. Journal of Nuclear Medicine, 61(4), 488-495.
[23] Fusco, R., Granata, V., Petrosino, T., Vallone, P., Iasevoli, M. A. D., Mattace Raso, M., Petrillo, A. (2025) Machine and deep learning on radiomic features from contrast-enhanced mammography and dynamic contrast-enhanced magnetic resonance imaging for breast cancer characterization. Bioengineering, 12(9), 952.
[24] Cheng, Y., Ren, R., Xu, Y., Duan, S., Zhang, J., Bao, Z. (2025) Dynamic contrast-enhanced MRI-based radiomics model of intra-tumoral kinetic heterogeneity for predicting breast cancer molecular subtypes. Frontiers in Molecular Biosciences, 12, 1635296.
[25] Wang, X., Hu, X., Wang, C., Yang, H., Hu, Y., Lan, X., Zhang, J. (2025) Automatic segmentation and molecular subtype classification of breast cancer using an MRI-based deep learning framework. Radiology: Imaging Cancer, 7(3), e240184.
[26] Xu, H., Yang, A., Kang, M., Lai, H., Zhou, X., Chen, Z., Deng, H. (2025) Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes. Scientific Reports, 15(1), 14720.
[27] Shen, D., Wu, G., Suk, H. I. (2017) Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248.
[28] Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Tang, A. (2017) Deep learning: a primer for radiologists. Radiographics, 37(7), 2113-2131.
[29] Huang, Y., Wei, L., Hu, Y., Shao, N., Lin, Y., He, S., Lin, Y. (2021) Multi-parametric MRI-based radiomics models for predicting molecular subtype and androgen receptor expression in breast cancer. Frontiers in Oncology, 11, 706733.
[30] Liang, Y., Tang, W., Zhang, J., Wang, T., Ng, W. W., Chen, S., Guo, Y. (2025) Xradnet: A radiomics-guided breast cancer molecular subtype prediction network with a radiomics explanation. IEEE Journal of Biomedical and Health Informatics, 29(8), 5875-5886.
[31] Shiri, I., Salimi, Y., Mohammadi Kazaj, P., Bagherieh, S., Amini, M., Saberi Manesh, A., Zaidi, H. (2025) Deep radiogenomics sequencing for breast tumor gene-phenotype decoding using dynamic contrast magnetic resonance imaging. Molecular Imaging and Biology, 27(1), 32-43.
[32] Gholizade, M., Soltanizadeh, H., Rahmanimanesh, M., Sana, S. S. (2025) A review of recent advances and strategies in transfer learning. International Journal of System Assurance Engineering and Management, 16(3), 1123-1162.
[33] Raghu, M., Zhang, C., Kleinberg, J., Bengio, S. (2019) Transfusion: understanding transfer learning for medical imaging. Advances in Neural Information Processing Systems, 32.
[34] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Polosukhin, I. (2017) Attention is all you need. Advances in Neural Information Processing Systems, 30.
[35] Chen, H., Martel, A. L. (2025) Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and convolutional neural networks. Journal of Medical Imaging, 12(S2), S22007-S22007.
[36] Singh, A., Sengupta, S., Lakshminarayanan, V. (2020) Explainable deep learning models in medical image analysis. Journal of Imaging, 6(6), 52.
[37] Luo, Y., Wei, J., Gu, Y., Zhu, C., Xu, F. (2025) Predicting molecular subtype in breast cancer using deep learning on mammography images. Frontiers in Oncology, 15, 1638212.
[38] Wang, W., Wang, Z., Wang, L., Li, J., Pang, Z., Qu, Y., Cui, S. (2025) Study on predicting breast cancer Ki-67 expression using a combination of radiomics and deep learning based on multiparametric MRI. Magnetic Resonance Imaging, 121, 110401.
[39] Sucre, A., Sánchez, X. C., Perez-Herrera, L. V., dM Vivanco, M., García-González, M. J., López-Linares, K., Garin-Muga, A. (2025) Multimodal fusion strategies for survival prediction in breast cancer: a comparative deep learning study. Computational and Structural Biotechnology Journal, 27, 4505-4516.
[40] Hong, D., Peng, J., Xu, P., Liu, W., Liu, Z., Ye, Z. (2026) A Multimodal fusion model of radiomics and deep learning integrating the tumor microenvironment accurately predicts pathological complete response in breast cancer. Academic Radiology, 33(4), 1473-1483.
[41] Giaquinto, A. N., Sung, H., Newman, L. A., Freedman, R. A., Smith, R. A., Star, J., Siegel, R. L. (2024) Breast cancer statistics 2024. CA: A Cancer Journal for Clinicians, 74(6), 477-495.
[42] Yao, M., Ye, D., Wang, Y., Shen, T., Yan, J., Zou, D., Sun, S. (2025) Application of DCE-MRI radiomics and heterogeneity analysis in predicting luminal and non-luminal subtypes of breast cancer. Frontiers in Oncology, 15, 1523507.
[43] Huang, Z., Qiu, Z., Chen, S., Zhang, Y., Wang, K., Zeng, Q., Bu, J. (2025) Optimizing deep learning models for luminal and nonluminal breast cancer classification using multidimensional ROI in DCE‐MRI – a multicenter study. Cancer Medicine, 14(9), e70931.
[44] Wang, Q., Zheng, N., Yu, Q., Shao, S. (2025) Multimodal imaging and advanced quantitative techniques for HER-2 status prediction in breast cancer. Discover Oncology, 16(1), 1820.
[45] Bianchini, G., De Angelis, C., Licata, L., Gianni, L. (2022) Treatment landscape of triple-negative breast cancer -expanded options, evolving needs. Nature Reviews Clinical Oncology, 19(2), 91-113.
[46] Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., Oermann, E. K. (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Medicine, 15(11), e1002683.
[47] Chen, W., Lin, G., Zhou, Y., Chen, Y., Shi, C., Zhao, T., Ji, J. (2026) Multiregional MRI-based deep learning radiomics to predict axillary response after neoadjuvant chemotherapy in breast cancer patients. Insights into Imaging, 17(1), 21.
[48] Gu, J., Tong, T., He, C., Xu, M., Yang, X., Tian, J., Wang, K. (2022) Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study. European Radiology, 32(3), 2099-2109.
[49] Spring, L., Greenup, R., Niemierko, A., Schapira, L., Haddad, S., Jimenez, R., Bardia, A. (2017) Pathologic complete response after neoadjuvant chemotherapy and long-term outcomes among young women with breast cancer. Journal of the National Comprehensive Cancer Network, 15(10), 1216-1223.
[50] Choudhery, S., Gomez-Cardona, D., Favazza, C. P., Hoskin, T. L., Haddad, T. C., Goetz, M. P., Boughey, J. C. (2022) MRI radiomics for assessment of molecular subtype, pathological complete response, and residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy. Academic Radiology, 29, S145-S154.
[51] Park, H., Lim, Y., Ko, E. S., Cho, H. H., Lee, J. E., Han, B. K., Park, K. W. (2018) Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clinical Cancer Research, 24(19), 4705-4714.
[52] Acquaah-Mensah, G. K., Aguilar, B., Abdilleh, K. (2025) Radiomics combined with transcriptomics improves prediction of breast cancer recurrence, molecular subtype and grade. Cancers, 17(17), 2912.
[53] Yuan, Q., Hong, Z., Ye, R., Yang, P., Lin, J., Jiang, X., Niu, M. (2025) Integrating deep feature extraction and MRI radiomics for survival prediction in breast cancer after neoadjuvant chemotherapy. Academic Radiology, 33(3), 872-888.
[54] Romeo, V., Kapetas, P., Clauser, P., Rasul, S., Cuocolo, R., Caruso, M., Pinker, K. (2023) Simultaneous 18F-FDG PET/MRI radiomics and machine learning analysis of the primary breast tumor for the preoperative prediction of axillary lymph node status in breast cancer. Cancers, 15(20), 5088.
[55] Zhao, B. (2021) Understanding sources of variation to improve the reproducibility of radiomics. Frontiers in Oncology, 11, 633176.
[56] Orlhac, F., Frouin, F., Nioche, C., Ayache, N., Buvat, I. (2019) Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology, 291(1), 53-59.
[57] Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J., Andrearczyk, V., Apte, A., Löck, S. (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2), 328-338.
[58] Vallières, M., Zwanenburg, A., Badic, B., Cheze Le Rest, C., Visvikis, D., Hatt, M. (2018) Responsible radiomics research for faster clinical translation. Journal of Nuclear Medicine, 59(2), 189-193.
[59] Holzinger, A., Langs, G., Denk, H., Zatloukal, K., Müller, H. (2019) Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(4), e1312.
[60] Tjoa, E., Guan, C. (2020) A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793-4813.
[61] Lundberg, S. M., Lee, S. I. (2017) A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
[62] Varoquaux, G. (2018) Cross-validation failure: Small sample sizes lead to large error bars. Neuroimage, 180, 68-77.
[63] Hosny, A., Aerts, H. J., Mak, R. H. (2019) Handcrafted versus deep learning radiomics for prediction of cancer therapy response. The Lancet Digital Health, 1(3), e106-e107.
[64] Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Natarajan, V. (2023) Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging. Nature Biomedical Engineering, 7(6), 756-779.
[65] Ji, Y., Lin, D., Wang, X., Zhang, L., Zhou, W., Ge, C., Li, R. (2026) A generative foundation model for chest radiography. NEJM AI, 3(5), AIoa2500799.
[66] Vourtsis, A., Kachulis, A. (2018) The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1,886 women. European Radiology, 28(2), 592-601.
[67] Xu, Z., Yang, Q., Li, M., Gu, J., Du, C., Chen, Y., Li, B. (2022) Predicting HER2 status in breast cancer on ultrasound images using deep learning method. Frontiers in Oncology, 12, 829041.
[68] He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., Zhang, K. (2019) The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30-36.
[69] Benjamens, S., Dhunnoo, P., Meskó, B. (2020) The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digital Medicine, 3(1), 118.
[70] Li, Q., Teodoro, G., Jiang, Y., Kong, J. (2024) NACNet: a histology context-aware transformer graph convolution network for predicting treatment response to neoadjuvant chemotherapy in Triple Negative Breast Cancer. Computerized Medical Imaging and Graphics, 118, 102467.
[71] Salehzehi, S., Jafarpoor Nesheli, S., Eftekhar, A., Shojaee Rocky, M., Vejdani Noghreiyan, V., Bazazpour, A., Rezaeijo, S. M. (2025) Graph neural network-based multi-label classification of breast cancer using multiparametric MRI and clinical features. Discover Oncology, 17, 194.
[72] Xia, Y., Qu, N., Lin, Y., Zhao, W., Teng, F., Chang, Y., Liu, W. (2025) Graph-based multi-modality network for axillary lymph node metastasis prediction in early-stage breast cancer. Computer Methods and Programs in Biomedicine, 109021.
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Lu, X., Yusof Ali, H. B. b., & Wang, J. (2026) Radiomics and Deep Learning in MRI-based Molecular Subtype Classification of Breast Cancer: A Comprehensive Review. Scientific Research Bulletin, 3(3), 1-20. https://doi.org/10.71052/srb2024/EBWX3831