Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 1987;235:177–82.

Article  PubMed  CAS  Google Scholar 

Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Davidson NE, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl J Med. 2005;353:1673–84.

Article  PubMed  CAS  Google Scholar 

Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl J Med. 2001;344:783–92.

Article  PubMed  CAS  Google Scholar 

Swain SM, Shastry M, Hamilton E. Targeting HER2-positive breast cancer: advances and future directions. Nat Rev Drug Discov. 2023;22:101–26.

Article  PubMed  CAS  Google Scholar 

Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, et al. Human epidermal growth factor Receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update. J Clin Oncol. 2018;36:2105–22.

Article  PubMed  CAS  Google Scholar 

Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N. Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 2020;49:74–80.

Article  PubMed  Google Scholar 

Yeh AC, Li H, Zhu Y, Zhang J, Khramtsova G, Drukker K, et al. Radiogenomics of breast cancer using dynamic contrast-enhanced MRI and gene expression profiling. Cancer Imaging. 2019;19:48.

Article  PubMed  PubMed Central  Google Scholar 

Wu J, Cao G, Sun X, Lee J, Rubin DL, Napel S, et al. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy. Radiology. 2018;288:26–35.

Article  PubMed  Google Scholar 

Shi Z, Huang X, Cheng Z, Xu Z, Lin H, Liu C, et al. MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer. Radiology. 2023;308:e222830.

Article  PubMed  Google Scholar 

Fan M, Zhang P, Wang Y, Peng W, Wang S, Gao X, et al. Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer. Eur Radiol. 2019;29:4456–67.

Article  PubMed  Google Scholar 

Fan M, Cheng H, Zhang P, Gao X, Zhang J, Shao G, et al. DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers. J Magn Reson Imaging. 2018;48:237–47.

Article  PubMed  Google Scholar 

Lu H, Yin J. Texture analysis of breast DCE-MRI based on intratumoral subregions for predicting HER2 2+ status. Front Oncol. 2020;10:543.

Article  PubMed  PubMed Central  Google Scholar 

Wu J, Gong G, Cui Y, Li R. Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy. J Magn Reson Imaging. 2016;44:1107–15.

Article  PubMed  PubMed Central  Google Scholar 

Chaudhury B, Zhou M, Goldgof DB, Hall LO, Gatenby RA, Gillies RJ, et al. Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis. J Magn Reson Imaging. 2015;42:1421–30.

Article  PubMed  PubMed Central  Google Scholar 

Kazerouni AS, Hormuth DA 2nd, Davis T, Bloom MJ, Mounho S, et al. Quantifying tumor heterogeneity via MRI habitats to characterize microenvironmental alterations in HER2+ breast cancer. Cancers. 2022;14:1837.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Feng S, Yin J. Dynamic contrast-enhanced magnetic resonance imaging radiomics analysis based on intratumoral subregions for predicting luminal and nonluminal breast cancer. Quant Imaging Med Surg. 2023;13:6735–49.

Article  PubMed  PubMed Central  Google Scholar 

Jochelson MS, Lobbes MBI. Contrast-enhanced mammography: state of the art. Radiology. 2021;299:36–48.

Article  PubMed  Google Scholar 

Wang S, Wang Z, Li R, You C, Mao N, Jiang T, et al. Association between quantitative and qualitative image features of contrast-enhanced mammography and molecular subtypes of breast cancer. Quant Imaging Med Surg. 2022;12:1270–80.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Zhu S, Wang S, Guo S, Wu R, Zhang J, Kong M, et al. Contrast-enhanced mammography radiomics analysis for preoperative prediction of breast cancer molecular subtypes. Acad Radiol. 2024;31:2228–38.

Article  PubMed  Google Scholar 

Nicosia L, Bozzini AC, Ballerini D, Palma S, Pesapane F, Raimondi S, et al. Radiomic features applied to contrast enhancement spectral mammography: possibility to predict breast cancer molecular subtypes in a non-invasive manner. Int J Mol Sci. 2022;23:15322.

Article  PubMed  PubMed Central  Google Scholar 

Petrillo A, Fusco R, Di Bernardo E, Petrosino T, Barretta ML, Porto A, et al. Prediction of breast cancer histological outcome by radiomics and artificial intelligence analysis in contrast-enhanced mammography. Cancers. 2022;14:2132.

Article  PubMed  PubMed Central  CAS  Google Scholar 

La Forgia D, Fanizzi A, Campobasso F, Bellotti R, Didonna V, Lorusso V, et al. Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome. Diagnostics. 2020;10:708.

Article  PubMed  PubMed Central  Google Scholar 

Amin MB, Edge S, Greene F, Byrd DR, Brookland RK, Washington MK, et al. AJCC Cancer Staging Manual, 8th edn. New York: Springer. 2017:589–636.

Jain A, Nandakumar K, Ross A. Score normalization in multimodal biometric system. Pattern Recogn. 2005;38:2270–85.

Article  Google Scholar 

Lehmann TM, Gönner C, Spitzer K. Addendum: B-spline interpolation in medical image processing. IEEE Trans Med Imaging. 2001;20:660–5.

Article  PubMed  CAS  Google Scholar 

van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–e7.

Article  PubMed  PubMed Central  Google Scholar 

Koo TK, Li MY. A Guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–63.

Article  PubMed  PubMed Central  Google Scholar 

Chen TQ, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. 2016. pp 785–794. https://doi.org/10.1145/2939672.2939785.

Nielsen D. Tree boosting with xgboost-why does xgboost win “every” machine learning competition? [D]. NTNU. 2016. Available online: https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/2433761/16128_FULLTEXT.pdf.

Nagy JA, Chang SH, Dvorak AM, Dvorak HF. Why are tumour blood vessels abnormal and why is it important to know? Br J Cancer. 2009;100:865–9.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Fan Y, Zhao D, Su J, Yuan W, Niu S, Guo W, et al. Radiomic signatures based on mammography and magnetic resonance imaging as new markers for estimation of Ki-67 and HER-2 status in breast cancer. J Comput Assist Tomogr. 2023;47:890–7.

Article  PubMed  Google Scholar 

Sang L, Liu Z, Huang C, Xu J, Wang H. Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer. Clin Radio. 2024;79:60–6.

Article  CAS  Google Scholar 

Ramtohul T, Djerroudi L, Lissavalid E, Nhy C, Redon L, Ikni L, et al. Multiparametric MRI and radiomics for the prediction of HER2-Zero, -Low, and -Positive breast cancers. Radiology. 2023;308:e222646.

Article  PubMed  Google Scholar 

Blaschke E, Abe H. MRI phenotype of breast cancer: Kinetic assessment for molecular subtypes. J Magn Reson Imaging. 2015;42:920–4.

Article