A Novel Biomarker for Identifying HER2-low Breast Cancer Using Synthetic MRI

A Novel Biomarker for Identifying HER2-low Breast Cancer Using Synthetic MRI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Novel Biomarker for Identifying HER2-low Breast Cancer Using Synthetic MRI Junzhong Liu, Zhaofeng Zheng, Yujing Chu, Mingyuan Pang, Longjiang Fang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6966189/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract To evaluate the diagnostic utility of quantitative parameters derived from synthetic magnetic resonance imaging (SyMRI) in differentiating HER2-low-expressing breast cancer from non-low-expressing subtypes. This retrospective study included 70 patients with pathologically confirmed unilateral invasive breast cancer who underwent preoperative 3.0T MRI with SyMRI sequences. Based on IHC/FISH results, patients were classified into HER2-low (n = 48) and non-low (n = 22) groups. Two radiologists independently measured T1, T2, PD, and ADC values of the lesions. Logistic regression analysis was used to identify the most effective predictors of HER2 expression status. ROC curve analysis was conducted to assess the discriminative performance of these predictors. Univariate logistic regression revealed that the T2 value was a significant predictor for differentiating HER2-zero from HER2-low expression. T2 values demonstrated moderate diagnostic performance, with an AUC of 0.817. The optimal cutoff value was 96.167 milliseconds, yielding a sensitivity of 68.2% and a specificity of 85.4%. T2 quantification derived from SyMRI shows potential as a noninvasive biomarker for identifying HER2-low-expressing breast cancer, supporting its potential role in guiding individualized treatment strategies. Health sciences/Biomarkers/Predictive markers Health sciences/Oncology/Cancer/Breast cancer Breast cancer HER2 Magnetic resonance imaging Quantitative parameters Imaging characteristics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Breast cancer is the most prevalent malignancy among women globally, accounting for approximately one-third of all female cancers and representing the leading cause of cancer-related mortality in women 1 . It is now widely recognized as a heterogeneous disease comprising distinct molecular and histological subtypes. Based on these classifications, breast cancer is broadly divided into three principal categories: hormone receptor-positive (estrogen receptor-positive [ER+] and/or progesterone receptor-positive [PR+]), human epidermal growth factor receptor 2-positive (HER2+), and triple-negative breast cancer (TNBC) 2 . HER2 is a transmembrane tyrosine kinase receptor that plays a crucial role in promoting tumor cell proliferation, invasion, and metastasis. It is both a key oncogenic driver and an important biomarker for prognostication and therapeutic targeting in breast cancer 3 – 5 . Traditionally, breast cancers have been dichotomized into HER2-positive and HER2-negative groups based on the extent of HER2 expression. Standard treatment for HER2-positive breast cancer involves a combination of anti-HER2 monoclonal antibodies—such as trastuzumab and pertuzumab—with chemotherapy. In contrast, patients with HER2-negative tumors have historically derived no clinical benefit from anti-HER2 monoclonal antibodies 6 . However, the development of novel anti-HER2 antibody-drug conjugates (ADCs) with demonstrated efficacy in HER2-low breast cancers has prompted the recognition of HER2-low as a potential new therapeutic subtype 7 . Approximately 45–55% of breast cancers exhibit HER2-low expression, emphasizing the need for accurate identification of this subgroup 4 . Currently, HER2 expression is assessed using immunohistochemistry (IHC) and in situ hybridization (ISH), typically performed on core needle biopsy specimens. These procedures are invasive and may fail to capture tumor heterogeneity, potentially leading to misclassification or underestimation of disease burden 8 . Moreover, HER2 expression is known to be biologically dynamic, with status changes observed during disease progression, possibly influenced by prior treatments 9 , 10 . Recent advances in imaging and radiomics have introduced non-invasive, dynamic frameworks for evaluating tumor biology. Radiomic analyses using ultrasound and magnetic resonance imaging (MRI) have shown promise in HER2 status assessment 11 , 12 . Nonetheless, their clinical application remains limited due to variability in diagnostic accuracy and poor reproducibility. Consequently, there is a growing demand for rapid, reproducible, and standardized imaging-based methods to evaluate HER2 expression levels in breast cancer. Synthetic magnetic resonance imaging (SyMRI) is an emerging quantitative MRI technique that employs a multi-dynamic, multi-echo (MDME) sequence within a single scan to acquire T1-weighted, T2-weighted, proton density (PD)-weighted, and inversion recovery images, along with generating quantitative T1, T2, and PD maps. This approach reduces scan time while producing measurements that reflect intrinsic tissue properties, independent of scanner model or imaging parameters at a given magnetic field strength 5 , 13 . SyMRI has been successfully applied in various oncologic contexts, including tumor grading, quantitative assessment of bone metastases in prostate cancer, and prediction of response to neoadjuvant therapy in locally advanced rectal cancer 14 – 17 . A prior study demonstrated that SyMRI-derived quantitative parameters could differentiate IHC expression profiles in breast cancer with greater accuracy than apparent diffusion coefficient (ADC) values 18 . However, the utility of SyMRI in identifying HER2-low breast cancer remains unconfirmed. We hypothesize that differences in HER2 expression among patients with invasive breast cancer are reflected in the quantitative parameters derived from SyMRI. This study aims to evaluate the diagnostic performance of SyMRI-based quantitative metrics in identifying HER2-low breast cancer. Materials and methods Ethics approval Due to the retrospective nature of this study, the Ethics Committee of Weifang People's Hospital waived the requirement for informed consent (Ethics Review No. KYLL20230526-9) and approved all experimental protocols, which adhered to the Declaration of Helsinki. Study participants Data were collected from 106 female patients who underwent breast magnetic resonance imaging (MRI) at Weifang People’s Hospital between March and September 2024 for evaluation of breast mass lesions. All patients subsequently underwent surgical resection, and final diagnoses were confirmed via histopathological examination of the surgical specimens. Inclusion criteria were as follows: (1) histopathological confirmation of invasive breast cancer and (2) completion of breast MRI before surgery. Exclusion criteria included: (1) non-invasive breast cancer on postoperative pathology; (2) prior neoadjuvant therapy or biopsy before MRI; and (3) suboptimal MRI image quality or significant artifacts that compromised image interpretation. A total of 70 patients met the eligibility criteria. All had unilateral lesions, although two patients exhibited multifocal tumors in the same breast; in these cases, the largest lesion was selected for analysis. HER2 expression status—categorized as HER2-zero, HER2-low, or HER2-positive—was determined using immunohistochemistry and in situ hybridization. A flowchart detailing the patient selection process is outlined in Fig. 1 . MRI examination All MRI scans were performed using a 3.0T scanner equipped with a dedicated 16-channel phased-array breast coil. Patients were positioned prone, feet-first, with both breasts suspended within the coil and arms elevated above the head. Standard sequences included axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fat-suppressed T2WI, and diffusion-weighted imaging (DWI). This was followed by acquisition of the axial SyMRI sequence (MAGnetic Resonance Image Compilation, MAGiC) and ultrafast dynamic contrast-enhanced imaging (DISCO). Prior to contrast administration, three baseline phases were obtained. A gadolinium-based contrast agent (Gd-DTPA, 0.1 mmol/kg) was then injected via the antecubital vein at 2.0 mL/s, followed by an equal volume of saline flush at the same rate. Each of the 23 imaging phases was acquired at 5-second intervals. Image analysis Post-processing was performed on a GE Healthcare workstation using proprietary SyMRI software to automatically generate quantitative T1, T2, and proton density (PD) maps. Apparent diffusion coefficient (ADC) maps were generated automatically during DWI acquisition (Fig. 2 , 3 ). Image analysis was independently conducted by two radiologists with over five years of experience in breast MRI, both blinded to clinical and pathological data. Discrepancies were resolved by consensus. Initially, the radiologists used DWI and contrast-enhanced T1WI to identify the slice with the largest tumor diameter. From this slice, the clearest corresponding quantitative parameter map was selected, and each radiologist manually delineated the region of interest (ROI). The ROIs were automatically propagated to the corresponding T1, T2, and PD maps. Each measurement was repeated three times, and the average values were recorded. During ROI placement, care was taken to exclude necrotic areas and peritumoral edema. In multifocal cases, only the largest lesion was evaluated. ADC values were obtained in a similar manner, with efforts made to maintain consistent ROI size across all parameter maps. Pathological evaluation Clinical and pathological data were extracted from the electronic medical record system. All pathological evaluations were based on surgical breast cancer specimens. Tumor size was determined using imaging-based measurements of maximum tumor diameter. Histological grade was classified as low grade (Grade I or II) or high grade (Grade III). HER2 status was categorized as HER2-zero (IHC score of 0), HER2-low (IHC score of 1 + or 2 + with negative FISH), or HER2-positive (IHC score of 3 + or 2 + with positive FISH). Estrogen and progesterone receptor (ER/PR) status was considered positive when expression exceeded 1%, and negative when ≤ 1%. The Ki-67 proliferation index was dichotomized using a 14% threshold, with values < 14% denoting low expression and ≥ 14% indicating high expression. Statistical analysis Statistical analyses were performed using R ( http://www.R-project.org ). Comparisons of T1, T2, proton density (PD), and apparent diffusion coefficient (ADC) values between HER2-low and non-HER2-low breast cancers were conducted using either independent sample t-tests or Mann–Whitney U tests, depending on data distribution and variance homogeneity. A p-value < 0.05 was considered statistically significant. Inter-observer agreement between the two radiologists was assessed using the intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic performance of quantitative parameters in distinguishing HER2-low expression, with the area under the curve (AUC) used to quantify discriminative ability. Results Patient characteristics A cohort of 70 female patients diagnosed with invasive breast cancer was analyzed, with a mean age of 53.43 ± 10.32 years. Based on immunohistochemistry and fluorescence in situ hybridization, patients were stratified into two groups: HER2-low expression (n = 48) and non-HER2-low expression (including both HER2-zero and HER2-positive cases, n = 22). The clinicopathological characteristics of the cohort are summarized in Table 1 . No statistically significant differences were observed between HER2-low and non-HER2-low groups in terms of age (52.25 ± 10.19 years vs. 56.00 ± 10.36 years, p = 0.16), tumor diameter (21.58 ± 6.99 mm vs. 20.89 ± 7.07 mm, p = 0.704), or Ki-67 proliferation index (29.42 ± 17.63% vs. 26.00 ± 15.66%, p = 0.439). Table 1 Clinicopathological Characteristics of Breast Cancer Patients Variables HER2-low (n = 48) Non-HER2-low (n = 22) P value Age(mean) 52.25 ± 10.19 56.00 ± 10.36 0.16 Diameter(mm) 21.58 ± 6.99 20.89 ± 7.07 0.704 Menstrual status 0.288 Premenopausal 24(50.0%) 8(36.36%) Postmenopausal 24(50.0%) 14(63.64%) Lesion location 0.724 Left breast 24(50.0%) 12(54.55%) Right breast 24(50.0%) 10(45.45%) Histologic grade 0.916 I or II 29(60.42%) 13(59.09%) III 19(39.58%) 9(40.91%) ER status 0986 Negative 11 (22.92%) 5 (22.73%) Positive 39 (77.08%) 17(77.27%) PR status 0.699 Negative 9(18.75%) 5(22.73%) Positive 39(81.25%) 17(77.27%) Ki-67 index(%) 0.699 ≤ 14 13(27.08%) 5(22.73%) > 14 35(72.92%) 17 (77.27%) ALN 0.840 Negative 36 (75.00%) 16 (72.73%) Positive 12 (25.00%) 6 (27.27%) HER2, human epidermal growth factor receptor 2; ER, estrogen receptor; PR, progesterone receptor; ALN, axillary lymph node. Assessment of interobserver agreement Interobserver agreement between the two radiologists was excellent, with intraclass correlation coefficients (ICC) of 0.994 (95% CI: 0.988–0.997) for T1, 0.991 (0.982–0.995) for T2, 0.995 (0.991–0.998) for PD, and 0.985 (0.970–0.993) for ADC. Comparisons of quantitative values between HER2-low BC and non-HER2-low BC Table 2 summarizes the differences in SyMRI-derived quantitative parameters and ADC values between HER2-low and non-HER2-low expression breast cancer. Notably, the T2 value was significantly lower in the HER2-low group (81.74 ± 12.61 ms) compared to the non-HER2-low group (98.68 ± 13.42 ms; p < 0.05). No significant differences were observed in T1, PD, or ADC values between the groups. Table 2 Comparison of Quantitative Parameter Values Between HER2-Low and Non-HER2-Low Expression Breast Cancer Parameters HER2-low Non-HER2-low P value T1 (ms) 1154.40 ± 305.72 1133.15 ± 246.92 0.776 T2 (ms) 81.74 ± 12.61 98.68 ± 13.42 0.043 PD (pu) 76.25 ± 14.21 71.29 ± 13.54 0.174 ADC(*10 − 3 mm 2 /s) 1.002.78 ± 170.81 1.03392 ± 183.32 0.503 PD, proton density; ADC, apparent diffusion coefficient Performance of the quantitative values in predicting HER2-low versus non-HER2-low status Univariate analysis indicated that lower T2 values ( p = 0.043) were significantly associated with HER2-low expression breast cancer (Table 3 ). Receiver operating characteristic (ROC) analysis (Table 4 ) yielded an area under the curve (AUC) of 0.812 for T2, with an optimal cutoff value of 96.167 ms for distinguishing HER2-low from non-HER2-low tumors (Fig. 4 ). The calibration curve of the HER2-low expression prediction model demonstrated excellent predictive performance (Fig. 5 ). Table 3 Univariate analysis of quantitative Parameter Values Variables Non-adjusted analysis Adjusted analysis* OR 95% CI P value OR 95% CI P value T1 1.00 1.00–1.00 0.7721 1.00 1.00–1.00 0.9623 T1 Tertile Low 1.00 1.00 Middle 1.24 0.35–4.32 0.7408 1.39 0.38–5.04 0.6138 High 0.96 0.26–3.52 0.9519 1.23 0.31–4.83 0.7674 PD 0.97 0.94–1.01 0.1742 0.98 0.94–1.02 0.3668 PD Tertile Low 1.00 1.00 Middle 0.72 0.22–2.40 0.5954 0.99 0.26–3.70 0.9861 High 0.38 0.10–1.40 0.1451 0.48 0.12–1.86 0.2853 ADC 1.00 1.00–1.00 0.4967 1.00 1.00–1.00 0.4308 ADC Tertile Low 1.00 1.00 Middle 1.00 0.27–3.77 1.00 0.84 0.21–3.29 0.7968 High 1.85 0.52–6.55 0.3425 1.81 0.50–6.54 0.3638 T2 1.09 1.04–1.15 0.0001 1.09 1.04–1.15 0.0003 T2 Tertile Low 1.00 1.00 Middle 2.92 0.50-16.89 0.2323 2.91 0.47–18.15 0.2528 High 17.50 3.30–92.90 0.0008 17.45 2.94–103.60 0.0017 * The parameter of age was adjusted Table 4 Diagnostic Performance of T2 Values in Identifying HER2-Low Breast Cancer Parameters AUC (95% CI) Cutoff value Sensitivity Specificity T2(ms) 0.817(0.707–0.928) 96.167 0.682 0.854 Discussion This study evaluated the clinical relevance of quantitative parameters derived from SyMRI in conjunction with ADC values for identifying HER2-low invasive breast cancer. Results revealed that the T2 relaxation time in HER2-low breast cancer was significantly lower than in non-HER2-low cases, with an area under the curve (AUC) of 0.817, underscoring the diagnostic potential of T2 mapping for HER2-low identification. In contrast, T1, proton density (PD), and ADC values did not demonstrate statistically significant differences between the two groups. Breast cancer is a heterogeneous disease comprising distinct molecular subtypes that differ in prognosis and therapeutic responsiveness. The HER2 gene plays a central role in breast cancer pathophysiology, informing both treatment decisions and prognostic assessments. The advent of anti-HER2 monoclonal antibodies targeting HER2 overexpression has significantly improved outcomes for patients with HER2-positive breast cancer. However, only approximately 15% of breast cancers are classified as HER2-positive, leaving a substantial proportion of patients ineligible for HER2-targeted therapies 19 . Recently, a Phase III clinical trial demonstrated that trastuzumab deruxtecan—an antibody–drug conjugate—markedly improved progression-free and overall survival in patients with HER2-low metastatic breast cancer 20 . These findings have elevated HER2-low status to clinical prominence, prompting the adoption of a three-tier classification system based on HER2 expression levels: HER2-positive, HER2-low, and HER2-zero. Current research efforts increasingly focus on the accurate identification of HER2-low breast cancer patients who may benefit from antibody–drug conjugate therapies. Bannier et al. developed a deep learning (DL) model to aid pathologists in diagnosing HER2-low cases, achieving identification rates as high as 97% for both HER2-low and HER2-positive breast cancer subtypes 21 . Zheng et al. reported that radiomics features derived from diffusion-weighted imaging (DWI) could effectively differentiate HER2-low breast cancers from HER2-overexpressing and HER2-zero tumors, with AUC values ranging from 0.778 to 0.782 across multiple validation cohorts 22 . Similarly, Liu et al. proposed an integrated model that combines conventional MRI features with radiomics data to predict HER2 status in invasive breast cancer, outperforming a radiomics-only model (AUC = 0.842 vs. 0.797), thereby enhancing noninvasive preoperative stratification for HER2-directed therapy 23 . Chen et al. demonstrated that radiomics features extracted from dynamic contrast-enhanced MRI (DCE-MRI) could be leveraged to build a machine learning model capable of reliably distinguishing HER2-low from HER2-positive breast cancer cases 24 . Quantitative MRI techniques offer objective and reproducible metrics that minimize inter-observer variability in disease characterization. DWI, in particular, provides the ADC as a quantitative measure that reflects tissue microstructure and histopathological features 25 – 27 . In clinical breast imaging, ADC values are widely used to discriminate between benign and malignant breast lesions and to evaluate prognostic biomarkers 18 , 28 – 30 . Several studies have explored associations between ADC values and HER2 expression, yielding inconsistent results. Park et al., in a study involving 110 invasive ductal carcinoma (IDC) cases, found significantly higher ADC values in HER2-positive tumors compared to HER2-negative ones (P = 0.02) 31 . Similarly, Lee et al. reported a statistically significant correlation between HER2 status and ADC measurements 32 . However, Duc et al., in a cohort of 49 breast cancer patients, observed no significant relationship between HER2 expression and either mean ADC (ADC mean ) or minimum ADC (ADC min ) values 29 . This lack of consensus is further reflected in additional studies reporting comparable null associations 18 , 33 . Previous investigations employed binary classifications of HER2 expression; however, the criteria for stratification varied across studies, potentially contributing to inconsistent findings. In the context of a ternary HER2 classification, we assessed the potential of ADC values to distinguish breast cancers exhibiting HER2-low expression. Our analysis revealed no significant difference in ADC values between HER2-low tumors and other breast cancer subtypes (P = 0.503). While earlier studies reported a significant inverse correlation between HER2 expression and ADC values, our results did not align with this trend. The established association between ADC values and tumor cell density—wherein high-grade tumors, due to increased cellularity, exhibit reduced ADC values—may not sufficiently explain the heterogeneity observed in HER2-low cases 31 . Whether HER2-low breast cancer constitutes a distinct biological or clinical entity remains unresolved. Prognostic studies on HER2-low expression have yielded inconsistent outcomes 9 , 34 , which may partly account for the lack of a significant correlation with ADC values observed in our study. Synthetic MRI (SyMRI) is an emerging, single-sequence multiparametric imaging technique that quantifies intrinsic tissue magnetic properties, including longitudinal (T1) and transverse (T2) relaxation times, as well as PD 35 . This contrast-free, time-efficient, and scanner-independent modality is increasingly applied in breast MRI. Gao et al. demonstrated that SyMRI-derived quantitative parameters could serve as imaging biomarkers for stratifying breast cancers by receptor status and proliferation rate 33 . Notably, HER2-positive tumors exhibited significantly lower PD values compared to HER2-negative tumors (P = 0.048; AUC = 0.629 for predicting HER2 status). Similarly, Li et al. divided 56 patients with invasive ductal carcinoma into high and low HER2 expression groups, reporting that the standard deviations of pre-contrast PD and T1 values were significantly associated with HER2 status (AUC = 0.746) 18 . In our study, HER2-low breast cancers showed significantly lower T2 values than other subtypes (81.74 ± 12.61 vs. 98.68 ± 13.42 ms; P = 0.043), although no significant differences were observed in T1 or PD values. The diagnostic performance of T2 values for identifying HER2-low expression yielded an AUC of 0.817, with an optimal cutoff of 96.167 ms, a sensitivity of 68.2%, and a specificity of 85.4%. These differences in SyMRI parameters across molecular subtypes likely reflect variations in tumor microstructure and interstitial fluid content associated with receptor status. Variations in intrinsic tissue properties—specifically T1, T2, and PD—are ultimately influenced by these biological differences 36 , 37 . Although the precise mechanisms remain unclear, the significantly reduced T2 values observed in HER2-low breast cancers may reflect a combination of factors, including altered intracellular and extracellular water distribution, microvascular perfusion, and increased tumor cell density. Further investigations with larger cohorts are warranted to elucidate the variation in SyMRI-derived quantitative parameters across more refined subgroups, such as HER2-negative, HER2-low, and HER2-positive breast cancers. Such research could enhance our understanding of the pathological underpinnings driving the quantitative differences observed in HER2-low expression tumors relative to other subtypes. This retrospective, single-center study is subject to inherent limitations, including potential selection bias and a relatively modest sample size, which may limit the generalizability and statistical power of the findings. Therefore, multicenter, large-scale prospective studies are essential to validate and extend these preliminary results. Moreover, our analysis was confined to mean values of SyMRI-derived quantitative parameters. Future work should consider incorporating texture analysis, which may offer a more nuanced assessment of tumor heterogeneity and provide deeper insights into the phenotypic characterization of breast cancer. In conclusion, our findings suggest that quantitative T2 values obtained via SyMRI hold promise for identifying patients with HER2-low expression breast cancer. This technique may serve as a non-invasive, dynamic imaging biomarker for monitoring HER2-low status, thereby contributing to the advancement of personalized and precision-based treatment strategies. Declarations Acknowledgements This study was supported by the Weifang Science and Technology Development Plan project (NO.2023YX008). Funding Open Access funding provided by Weifang People's Hospital. Authors and Affiliations Department of Radiology, Weifang People's Hospital, Shandong Second Medical University, Weifang, Shandong, 261041, China Junzhong Liu, Zhaofeng zheng, Yujing Chu, Mingyuan Pang, Longjiang Fang, Qi Wang, Wenjuan Wang, Linkun Li Author contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Junzhong Liu, Zhaofeng zheng, Yujing Chu, Mingyuan Pang, Longjiang Fang, Qi Wang, Wenjuan Wang and Linkun Li. The first draft of the manuscript was written by Junzhong Liu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Due to the retrospective nature of the study, the Ethics Committee of Weifang people’s hospital waived the need of obtaining informed consent (Ethics Review No.KYLL20230526-9). References Akram, M., Iqbal, M., Daniyal, M. & Khan, A. U. Awareness and current knowledge of breast cancer. Biol. Res. 50 , 33 (2017). Barzaman, K. et al. Breast cancer: biology, biomarkers, and treatments. Int. Immunopharmacol. 84 , 106535 (2020). Hamilton, E., Shastry, M., Shiller, S. M. & Ren, R. Targeting HER2 heterogeneity in breast cancer. Cancer Treat. Rev. 100 , 102286 (2021). Hong, B. et al. Consensus on clinical diagnosis and medical treatment of HER2-low breast cancer (2022 edition). J. Natl. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6966189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":515971820,"identity":"182ef3ff-c38b-441a-8922-772a5f29c290","order_by":0,"name":"Junzhong Liu","email":"","orcid":"","institution":"Weifang People's Hospital, Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junzhong","middleName":"","lastName":"Liu","suffix":""},{"id":515971821,"identity":"27795d57-7f4c-4bc4-bef1-1d888d3feb15","order_by":1,"name":"Zhaofeng Zheng","email":"","orcid":"","institution":"Weifang People's Hospital, Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhaofeng","middleName":"","lastName":"Zheng","suffix":""},{"id":515971822,"identity":"2fb352f8-f31d-42f8-863c-7bc1e8cdac9f","order_by":2,"name":"Yujing Chu","email":"","orcid":"","institution":"Weifang People's Hospital, Shandong Second Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujing","middleName":"","lastName":"Chu","suffix":""},{"id":515971823,"identity":"543c4ffd-5297-4d5c-b481-586804cc30bf","order_by":3,"name":"Mingyuan Pang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYJCCAx9+SPDwyz8+AGQQp4Px4MweGxnJhrREIIM4LcyHOdjSbAwacoyBDCLUm4udMTjMwHOYx4DhzAcgg0GeX+wAfi2Ws3MMDhdYHOYxZ+zdAGQwGM6cnYBfi8FtoJYZQFssm3k3ABkMCQa3idHCwwZ02DGeB0AG8VrSeAzO8DAQp8VydloBKJB5JGewGQAZEoT9Yi6dvPkDMCrt+SWYHwMZNvL80oQcxsBhgMyXwK8cooX9AWFVo2AUjIJRMLIBAGCESbt8nb/TAAAAAElFTkSuQmCC","orcid":"","institution":"Weifang People's Hospital, Shandong Second Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mingyuan","middleName":"","lastName":"Pang","suffix":""},{"id":515971824,"identity":"b3dbcf9e-d88a-4e70-9e2d-051f3eb1ff0c","order_by":4,"name":"Longjiang Fang","email":"","orcid":"","institution":"Weifang People's Hospital, Shandong Second Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Linkun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-24 13:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6966189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6966189/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-31322-5","type":"published","date":"2025-12-08T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91839542,"identity":"211192eb-44ad-447c-a79a-76e044e40f10","added_by":"auto","created_at":"2025-09-22 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09:51:56","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111809,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6966189/v1/1bcbaa2a596d8b000f5ce4eb.html"},{"id":91839539,"identity":"ad9e776d-bdf6-4467-bd42-debcf0f8b0b5","added_by":"auto","created_at":"2025-09-22 09:51:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228497,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the patient selection methodology utilized in the current study.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6966189/v1/29f12f3d48db68c14dcbc4aa.jpeg"},{"id":91839541,"identity":"a678da88-3b3f-4644-8420-dba6406222df","added_by":"auto","created_at":"2025-09-22 09:51:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392039,"visible":true,"origin":"","legend":"\u003cp\u003eHuman epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer confirmed by surgical pathology in the right breast of a 62 -year-old woman. The ADC (a) showed a solid mass (red arrow). The SyMRI values of ADC(a), PD(b), T1(c) and T2(d) are 1.01´10-3mm2/s,75ms,1155ms and 80.6ms respectively.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6966189/v1/605983972c1e347f2b51e618.jpeg"},{"id":91839544,"identity":"d251cd7f-6d51-447a-9dc3-5cd6cd526175","added_by":"auto","created_at":"2025-09-22 09:51:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":337606,"visible":true,"origin":"","legend":"\u003cp\u003eNon human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer confirmed by surgical pathology in the left breast of a 64 -year-old woman. The ADC (a) showed a solid mass (red arrow). The ADC (a) showed a solid mass (red arrow). The SyMRI values of ADC(a), PD(b), T1(c) and T2(d) are 1.03´10-3mm2/s,72ms,1135ms and 97.7ms respectively.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6966189/v1/c9c1d71c1795b2ef226bcc4d.jpeg"},{"id":91842357,"identity":"27bda378-8a20-4034-aa40-0b1a5cd4164c","added_by":"auto","created_at":"2025-09-22 09:59:56","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110875,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of quantitative values for differentiating human epidermal growth factor receptor 2 (HER2) expression levels. Receiver operating characteristic curve of the quantitative index for distinguishing HER2-low from non-HER2-low breast cancer.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6966189/v1/b4fe0aad99da2d7f4e884e93.jpeg"},{"id":91843772,"identity":"73b34673-905b-4cfc-99b9-949edcff17d7","added_by":"auto","created_at":"2025-09-22 10:07:56","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86328,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the HER2 low expression prediction model based on T2 logistic regression.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6966189/v1/eb2858827c581422cdefe679.jpeg"},{"id":98243738,"identity":"871c6adf-11d3-44ec-9e20-2077d49d0e65","added_by":"auto","created_at":"2025-12-15 16:10:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2016267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6966189/v1/1199b8dc-c0e5-4613-97a7-821bffcebb3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Biomarker for Identifying HER2-low Breast Cancer Using Synthetic MRI","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most prevalent malignancy among women globally, accounting for approximately one-third of all female cancers and representing the leading cause of cancer-related mortality in women\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is now widely recognized as a heterogeneous disease comprising distinct molecular and histological subtypes. Based on these classifications, breast cancer is broadly divided into three principal categories: hormone receptor-positive (estrogen receptor-positive [ER+] and/or progesterone receptor-positive [PR+]), human epidermal growth factor receptor 2-positive (HER2+), and triple-negative breast cancer (TNBC)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. HER2 is a transmembrane tyrosine kinase receptor that plays a crucial role in promoting tumor cell proliferation, invasion, and metastasis. It is both a key oncogenic driver and an important biomarker for prognostication and therapeutic targeting in breast cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Traditionally, breast cancers have been dichotomized into HER2-positive and HER2-negative groups based on the extent of HER2 expression. Standard treatment for HER2-positive breast cancer involves a combination of anti-HER2 monoclonal antibodies\u0026mdash;such as trastuzumab and pertuzumab\u0026mdash;with chemotherapy. In contrast, patients with HER2-negative tumors have historically derived no clinical benefit from anti-HER2 monoclonal antibodies\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, the development of novel anti-HER2 antibody-drug conjugates (ADCs) with demonstrated efficacy in HER2-low breast cancers has prompted the recognition of HER2-low as a potential new therapeutic subtype\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Approximately 45\u0026ndash;55% of breast cancers exhibit HER2-low expression, emphasizing the need for accurate identification of this subgroup\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, HER2 expression is assessed using immunohistochemistry (IHC) and in situ hybridization (ISH), typically performed on core needle biopsy specimens. These procedures are invasive and may fail to capture tumor heterogeneity, potentially leading to misclassification or underestimation of disease burden\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Moreover, HER2 expression is known to be biologically dynamic, with status changes observed during disease progression, possibly influenced by prior treatments\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Recent advances in imaging and radiomics have introduced non-invasive, dynamic frameworks for evaluating tumor biology. Radiomic analyses using ultrasound and magnetic resonance imaging (MRI) have shown promise in HER2 status assessment\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Nonetheless, their clinical application remains limited due to variability in diagnostic accuracy and poor reproducibility. Consequently, there is a growing demand for rapid, reproducible, and standardized imaging-based methods to evaluate HER2 expression levels in breast cancer.\u003c/p\u003e\u003cp\u003eSynthetic magnetic resonance imaging (SyMRI) is an emerging quantitative MRI technique that employs a multi-dynamic, multi-echo (MDME) sequence within a single scan to acquire T1-weighted, T2-weighted, proton density (PD)-weighted, and inversion recovery images, along with generating quantitative T1, T2, and PD maps. This approach reduces scan time while producing measurements that reflect intrinsic tissue properties, independent of scanner model or imaging parameters at a given magnetic field strength\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. SyMRI has been successfully applied in various oncologic contexts, including tumor grading, quantitative assessment of bone metastases in prostate cancer, and prediction of response to neoadjuvant therapy in locally advanced rectal cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. A prior study demonstrated that SyMRI-derived quantitative parameters could differentiate IHC expression profiles in breast cancer with greater accuracy than apparent diffusion coefficient (ADC) values\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, the utility of SyMRI in identifying HER2-low breast cancer remains unconfirmed.\u003c/p\u003e\u003cp\u003eWe hypothesize that differences in HER2 expression among patients with invasive breast cancer are reflected in the quantitative parameters derived from SyMRI. This study aims to evaluate the diagnostic performance of SyMRI-based quantitative metrics in identifying HER2-low breast cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003e Due to the retrospective nature of this study, the Ethics Committee of Weifang People's Hospital waived the requirement for informed consent (Ethics Review No. KYLL20230526-9) and approved all experimental protocols, which adhered to the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eData were collected from 106 female patients who underwent breast magnetic resonance imaging (MRI) at Weifang People\u0026rsquo;s Hospital between March and September 2024 for evaluation of breast mass lesions. All patients subsequently underwent surgical resection, and final diagnoses were confirmed via histopathological examination of the surgical specimens. Inclusion criteria were as follows: (1) histopathological confirmation of invasive breast cancer and (2) completion of breast MRI before surgery. Exclusion criteria included: (1) non-invasive breast cancer on postoperative pathology; (2) prior neoadjuvant therapy or biopsy before MRI; and (3) suboptimal MRI image quality or significant artifacts that compromised image interpretation. A total of 70 patients met the eligibility criteria. All had unilateral lesions, although two patients exhibited multifocal tumors in the same breast; in these cases, the largest lesion was selected for analysis. HER2 expression status\u0026mdash;categorized as HER2-zero, HER2-low, or HER2-positive\u0026mdash;was determined using immunohistochemistry and in situ hybridization. A flowchart detailing the patient selection process is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMRI examination\u003c/h3\u003e\n\u003cp\u003eAll MRI scans were performed using a 3.0T scanner equipped with a dedicated 16-channel phased-array breast coil. Patients were positioned prone, feet-first, with both breasts suspended within the coil and arms elevated above the head. Standard sequences included axial T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fat-suppressed T2WI, and diffusion-weighted imaging (DWI). This was followed by acquisition of the axial SyMRI sequence (MAGnetic Resonance Image Compilation, MAGiC) and ultrafast dynamic contrast-enhanced imaging (DISCO). Prior to contrast administration, three baseline phases were obtained. A gadolinium-based contrast agent (Gd-DTPA, 0.1 mmol/kg) was then injected via the antecubital vein at 2.0 mL/s, followed by an equal volume of saline flush at the same rate. Each of the 23 imaging phases was acquired at 5-second intervals.\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003ePost-processing was performed on a GE Healthcare workstation using proprietary SyMRI software to automatically generate quantitative T1, T2, and proton density (PD) maps. Apparent diffusion coefficient (ADC) maps were generated automatically during DWI acquisition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Image analysis was independently conducted by two radiologists with over five years of experience in breast MRI, both blinded to clinical and pathological data. Discrepancies were resolved by consensus. Initially, the radiologists used DWI and contrast-enhanced T1WI to identify the slice with the largest tumor diameter. From this slice, the clearest corresponding quantitative parameter map was selected, and each radiologist manually delineated the region of interest (ROI). The ROIs were automatically propagated to the corresponding T1, T2, and PD maps. Each measurement was repeated three times, and the average values were recorded. During ROI placement, care was taken to exclude necrotic areas and peritumoral edema. In multifocal cases, only the largest lesion was evaluated. ADC values were obtained in a similar manner, with efforts made to maintain consistent ROI size across all parameter maps.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePathological evaluation\u003c/h3\u003e\n\u003cp\u003eClinical and pathological data were extracted from the electronic medical record system. All pathological evaluations were based on surgical breast cancer specimens. Tumor size was determined using imaging-based measurements of maximum tumor diameter. Histological grade was classified as low grade (Grade I or II) or high grade (Grade III). HER2 status was categorized as HER2-zero (IHC score of 0), HER2-low (IHC score of 1\u0026thinsp;+\u0026thinsp;or 2\u0026thinsp;+\u0026thinsp;with negative FISH), or HER2-positive (IHC score of 3\u0026thinsp;+\u0026thinsp;or 2\u0026thinsp;+\u0026thinsp;with positive FISH). Estrogen and progesterone receptor (ER/PR) status was considered positive when expression exceeded 1%, and negative when \u0026le;\u0026thinsp;1%. The Ki-67 proliferation index was dichotomized using a 14% threshold, with values\u0026thinsp;\u0026lt;\u0026thinsp;14% denoting low expression and \u0026ge;\u0026thinsp;14% indicating high expression.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Comparisons of T1, T2, proton density (PD), and apparent diffusion coefficient (ADC) values between HER2-low and non-HER2-low breast cancers were conducted using either independent sample t-tests or Mann\u0026ndash;Whitney U tests, depending on data distribution and variance homogeneity. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Inter-observer agreement between the two radiologists was assessed using the intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic performance of quantitative parameters in distinguishing HER2-low expression, with the area under the curve (AUC) used to quantify discriminative ability.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics\u003c/h2\u003e\u003cp\u003eA cohort of 70 female patients diagnosed with invasive breast cancer was analyzed, with a mean age of 53.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32 years. Based on immunohistochemistry and fluorescence in situ hybridization, patients were stratified into two groups: HER2-low expression (n\u0026thinsp;=\u0026thinsp;48) and non-HER2-low expression (including both HER2-zero and HER2-positive cases, n\u0026thinsp;=\u0026thinsp;22). The clinicopathological characteristics of the cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No statistically significant differences were observed between HER2-low and non-HER2-low groups in terms of age (52.25\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19 years vs. 56.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36 years, p\u0026thinsp;=\u0026thinsp;0.16), tumor diameter (21.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99 mm vs. 20.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07 mm, p\u0026thinsp;=\u0026thinsp;0.704), or Ki-67 proliferation index (29.42\u0026thinsp;\u0026plusmn;\u0026thinsp;17.63% vs. 26.00\u0026thinsp;\u0026plusmn;\u0026thinsp;15.66%, p\u0026thinsp;=\u0026thinsp;0.439).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicopathological Characteristics of Breast Cancer Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHER2-low \u003c/p\u003e\u003cp\u003e (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-HER2-low \u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.25\u0026thinsp;\u0026plusmn;\u0026thinsp;10.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstrual status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(36.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostmenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(63.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLesion location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft breast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(54.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight breast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(45.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistologic grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI or II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29(60.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(59.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19(39.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9(40.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (22.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (22.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (77.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17(77.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(18.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(22.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39(81.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17(77.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi-67 index(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(27.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(22.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35(72.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (77.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (75.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (72.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (27.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHER2, human epidermal growth factor receptor 2; ER, estrogen receptor; PR, progesterone receptor; ALN, axillary lymph node.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAssessment of interobserver agreement\u003c/h2\u003e\u003cp\u003e Interobserver agreement between the two radiologists was excellent, with intraclass correlation coefficients (ICC) of 0.994 (95% CI: 0.988\u0026ndash;0.997) for T1, 0.991 (0.982\u0026ndash;0.995) for T2, 0.995 (0.991\u0026ndash;0.998) for PD, and 0.985 (0.970\u0026ndash;0.993) for ADC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eComparisons of quantitative values between HER2-low BC and non-HER2-low BC\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the differences in SyMRI-derived quantitative parameters and ADC values between HER2-low and non-HER2-low expression breast cancer. Notably, the T2 value was significantly lower in the HER2-low group (81.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61 ms) compared to the non-HER2-low group (98.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.42 ms; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed in T1, PD, or ADC values between the groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Quantitative Parameter Values Between HER2-Low and Non-HER2-Low Expression Breast Cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHER2-low\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-HER2-low\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1154.40\u0026thinsp;\u0026plusmn;\u0026thinsp;305.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1133.15\u0026thinsp;\u0026plusmn;\u0026thinsp;246.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e81.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e98.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD (pu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e76.25\u0026thinsp;\u0026plusmn;\u0026thinsp;14.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e71.29\u0026thinsp;\u0026plusmn;\u0026thinsp;13.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC(*10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003emm\u003csup\u003e2\u003c/sup\u003e/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.002.78\u0026thinsp;\u0026plusmn;\u0026thinsp;170.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.03392\u0026thinsp;\u0026plusmn;\u0026thinsp;183.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003ePD, proton density; ADC, apparent diffusion coefficient\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePerformance of the quantitative values in predicting HER2-low versus non-HER2-low status\u003c/h2\u003e\u003cp\u003eUnivariate analysis indicated that lower T2 values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) were significantly associated with HER2-low expression breast cancer (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Receiver operating characteristic (ROC) analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) yielded an area under the curve (AUC) of 0.812 for T2, with an optimal cutoff value of 96.167 ms for distinguishing HER2-low from non-HER2-low tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The calibration curve of the HER2-low expression prediction model demonstrated excellent predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate analysis of quantitative Parameter Values\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNon-adjusted analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAdjusted analysis*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 Tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.35\u0026ndash;4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38\u0026ndash;5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.26\u0026ndash;3.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.31\u0026ndash;4.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7674\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94\u0026ndash;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.3668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD Tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.22\u0026ndash;2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.26\u0026ndash;3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u0026ndash;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u0026ndash;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC Tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u0026ndash;3.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u0026ndash;3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7968\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52\u0026ndash;6.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u0026ndash;6.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.3638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u0026ndash;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u0026ndash;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 Tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50-16.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.47\u0026ndash;18.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.30\u0026ndash;92.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.94\u0026ndash;103.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e* The parameter of age was adjusted\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Performance of T2 Values in Identifying HER2-Low Breast Cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCutoff value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2(ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.817(0.707\u0026ndash;0.928)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the clinical relevance of quantitative parameters derived from SyMRI in conjunction with ADC values for identifying HER2-low invasive breast cancer. Results revealed that the T2 relaxation time in HER2-low breast cancer was significantly lower than in non-HER2-low cases, with an area under the curve (AUC) of 0.817, underscoring the diagnostic potential of T2 mapping for HER2-low identification. In contrast, T1, proton density (PD), and ADC values did not demonstrate statistically significant differences between the two groups.\u003c/p\u003e\u003cp\u003eBreast cancer is a heterogeneous disease comprising distinct molecular subtypes that differ in prognosis and therapeutic responsiveness. The HER2 gene plays a central role in breast cancer pathophysiology, informing both treatment decisions and prognostic assessments. The advent of anti-HER2 monoclonal antibodies targeting HER2 overexpression has significantly improved outcomes for patients with HER2-positive breast cancer. However, only approximately 15% of breast cancers are classified as HER2-positive, leaving a substantial proportion of patients ineligible for HER2-targeted therapies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Recently, a Phase III clinical trial demonstrated that trastuzumab deruxtecan\u0026mdash;an antibody\u0026ndash;drug conjugate\u0026mdash;markedly improved progression-free and overall survival in patients with HER2-low metastatic breast cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These findings have elevated HER2-low status to clinical prominence, prompting the adoption of a three-tier classification system based on HER2 expression levels: HER2-positive, HER2-low, and HER2-zero.\u003c/p\u003e\u003cp\u003eCurrent research efforts increasingly focus on the accurate identification of HER2-low breast cancer patients who may benefit from antibody\u0026ndash;drug conjugate therapies. Bannier et al. developed a deep learning (DL) model to aid pathologists in diagnosing HER2-low cases, achieving identification rates as high as 97% for both HER2-low and HER2-positive breast cancer subtypes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Zheng et al. reported that radiomics features derived from diffusion-weighted imaging (DWI) could effectively differentiate HER2-low breast cancers from HER2-overexpressing and HER2-zero tumors, with AUC values ranging from 0.778 to 0.782 across multiple validation cohorts\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Similarly, Liu et al. proposed an integrated model that combines conventional MRI features with radiomics data to predict HER2 status in invasive breast cancer, outperforming a radiomics-only model (AUC\u0026thinsp;=\u0026thinsp;0.842 vs. 0.797), thereby enhancing noninvasive preoperative stratification for HER2-directed therapy\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Chen et al. demonstrated that radiomics features extracted from dynamic contrast-enhanced MRI (DCE-MRI) could be leveraged to build a machine learning model capable of reliably distinguishing HER2-low from HER2-positive breast cancer cases\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eQuantitative MRI techniques offer objective and reproducible metrics that minimize inter-observer variability in disease characterization. DWI, in particular, provides the ADC as a quantitative measure that reflects tissue microstructure and histopathological features\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In clinical breast imaging, ADC values are widely used to discriminate between benign and malignant breast lesions and to evaluate prognostic biomarkers\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Several studies have explored associations between ADC values and HER2 expression, yielding inconsistent results. Park et al., in a study involving 110 invasive ductal carcinoma (IDC) cases, found significantly higher ADC values in HER2-positive tumors compared to HER2-negative ones (P\u0026thinsp;=\u0026thinsp;0.02)\u003csup\u003e31\u003c/sup\u003e. Similarly, Lee et al. reported a statistically significant correlation between HER2 status and ADC measurements\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. However, Duc et al., in a cohort of 49 breast cancer patients, observed no significant relationship between HER2 expression and either mean ADC (ADC\u003csub\u003emean\u003c/sub\u003e) or minimum ADC (ADC\u003csub\u003emin\u003c/sub\u003e) values\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This lack of consensus is further reflected in additional studies reporting comparable null associations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Previous investigations employed binary classifications of HER2 expression; however, the criteria for stratification varied across studies, potentially contributing to inconsistent findings. In the context of a ternary HER2 classification, we assessed the potential of ADC values to distinguish breast cancers exhibiting HER2-low expression. Our analysis revealed no significant difference in ADC values between HER2-low tumors and other breast cancer subtypes (P\u0026thinsp;=\u0026thinsp;0.503). While earlier studies reported a significant inverse correlation between HER2 expression and ADC values, our results did not align with this trend. The established association between ADC values and tumor cell density\u0026mdash;wherein high-grade tumors, due to increased cellularity, exhibit reduced ADC values\u0026mdash;may not sufficiently explain the heterogeneity observed in HER2-low cases\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Whether HER2-low breast cancer constitutes a distinct biological or clinical entity remains unresolved. Prognostic studies on HER2-low expression have yielded inconsistent outcomes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, which may partly account for the lack of a significant correlation with ADC values observed in our study.\u003c/p\u003e\u003cp\u003eSynthetic MRI (SyMRI) is an emerging, single-sequence multiparametric imaging technique that quantifies intrinsic tissue magnetic properties, including longitudinal (T1) and transverse (T2) relaxation times, as well as PD\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This contrast-free, time-efficient, and scanner-independent modality is increasingly applied in breast MRI. Gao et al. demonstrated that SyMRI-derived quantitative parameters could serve as imaging biomarkers for stratifying breast cancers by receptor status and proliferation rate\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Notably, HER2-positive tumors exhibited significantly lower PD values compared to HER2-negative tumors (P\u0026thinsp;=\u0026thinsp;0.048; AUC\u0026thinsp;=\u0026thinsp;0.629 for predicting HER2 status). Similarly, Li et al. divided 56 patients with invasive ductal carcinoma into high and low HER2 expression groups, reporting that the standard deviations of pre-contrast PD and T1 values were significantly associated with HER2 status (AUC\u0026thinsp;=\u0026thinsp;0.746)\u003csup\u003e18\u003c/sup\u003e. In our study, HER2-low breast cancers showed significantly lower T2 values than other subtypes (81.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61 vs. 98.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.42 ms; P\u0026thinsp;=\u0026thinsp;0.043), although no significant differences were observed in T1 or PD values. The diagnostic performance of T2 values for identifying HER2-low expression yielded an AUC of 0.817, with an optimal cutoff of 96.167 ms, a sensitivity of 68.2%, and a specificity of 85.4%. These differences in SyMRI parameters across molecular subtypes likely reflect variations in tumor microstructure and interstitial fluid content associated with receptor status. Variations in intrinsic tissue properties\u0026mdash;specifically T1, T2, and PD\u0026mdash;are ultimately influenced by these biological differences\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Although the precise mechanisms remain unclear, the significantly reduced T2 values observed in HER2-low breast cancers may reflect a combination of factors, including altered intracellular and extracellular water distribution, microvascular perfusion, and increased tumor cell density. Further investigations with larger cohorts are warranted to elucidate the variation in SyMRI-derived quantitative parameters across more refined subgroups, such as HER2-negative, HER2-low, and HER2-positive breast cancers. Such research could enhance our understanding of the pathological underpinnings driving the quantitative differences observed in HER2-low expression tumors relative to other subtypes.\u003c/p\u003e\u003cp\u003eThis retrospective, single-center study is subject to inherent limitations, including potential selection bias and a relatively modest sample size, which may limit the generalizability and statistical power of the findings. Therefore, multicenter, large-scale prospective studies are essential to validate and extend these preliminary results. Moreover, our analysis was confined to mean values of SyMRI-derived quantitative parameters. Future work should consider incorporating texture analysis, which may offer a more nuanced assessment of tumor heterogeneity and provide deeper insights into the phenotypic characterization of breast cancer.\u003c/p\u003e\u003cp\u003eIn conclusion, our findings suggest that quantitative T2 values obtained via SyMRI hold promise for identifying patients with HER2-low expression breast cancer. This technique may serve as a non-invasive, dynamic imaging biomarker for monitoring HER2-low status, thereby contributing to the advancement of personalized and precision-based treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Weifang Science and Technology Development Plan project (NO.2023YX008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Access funding provided by Weifang People\u0026apos;s Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Radiology, Weifang People\u0026apos;s Hospital, Shandong Second Medical University, Weifang, Shandong, 261041, China\u003c/p\u003e\n\u003cp\u003eJunzhong Liu, Zhaofeng zheng, Yujing Chu, Mingyuan Pang, Longjiang Fang, Qi Wang, Wenjuan Wang, Linkun Li\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Junzhong Liu, Zhaofeng zheng, Yujing Chu, Mingyuan Pang, Longjiang Fang, Qi Wang, Wenjuan Wang and Linkun Li. The first draft of the manuscript was written by Junzhong Liu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Due to the retrospective nature of the study, the Ethics Committee of Weifang people\u0026rsquo;s hospital waived the need of obtaining informed consent (Ethics Review No.KYLL20230526-9).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkram, M., Iqbal, M., Daniyal, M. \u0026amp; Khan, A. U. Awareness and current knowledge of breast cancer. \u003cem\u003eBiol. Res.\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 33 (2017).\u003c/li\u003e\n\u003cli\u003eBarzaman, K. et al. Breast cancer: biology, biomarkers, and treatments. \u003cem\u003eInt. Immunopharmacol.\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 106535 (2020).\u003c/li\u003e\n\u003cli\u003eHamilton, E., Shastry, M., Shiller, S. M. \u0026amp; Ren, R. Targeting HER2 heterogeneity in breast cancer. \u003cem\u003eCancer Treat. Rev.\u003c/em\u003e \u003cstrong\u003e100\u003c/strong\u003e, 102286 (2021).\u003c/li\u003e\n\u003cli\u003eHong, B. et al. 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T1 and T2 mapping for identifying malignant lymph nodes in head and neck squamous cell carcinoma. \u003cem\u003eCancer Imaging\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 125 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, HER2, Magnetic resonance imaging, Quantitative parameters, Imaging characteristics","lastPublishedDoi":"10.21203/rs.3.rs-6966189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6966189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo evaluate the diagnostic utility of quantitative parameters derived from synthetic magnetic resonance imaging (SyMRI) in differentiating HER2-low-expressing breast cancer from non-low-expressing subtypes. This retrospective study included 70 patients with pathologically confirmed unilateral invasive breast cancer who underwent preoperative 3.0T MRI with SyMRI sequences. Based on IHC/FISH results, patients were classified into HER2-low (n\u0026thinsp;=\u0026thinsp;48) and non-low (n\u0026thinsp;=\u0026thinsp;22) groups. Two radiologists independently measured T1, T2, PD, and ADC values of the lesions. Logistic regression analysis was used to identify the most effective predictors of HER2 expression status. ROC curve analysis was conducted to assess the discriminative performance of these predictors. Univariate logistic regression revealed that the T2 value was a significant predictor for differentiating HER2-zero from HER2-low expression. T2 values demonstrated moderate diagnostic performance, with an AUC of 0.817. The optimal cutoff value was 96.167 milliseconds, yielding a sensitivity of 68.2% and a specificity of 85.4%. T2 quantification derived from SyMRI shows potential as a noninvasive biomarker for identifying HER2-low-expressing breast cancer, supporting its potential role in guiding individualized treatment strategies.\u003c/p\u003e","manuscriptTitle":"A Novel Biomarker for Identifying HER2-low Breast Cancer Using Synthetic MRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:51:51","doi":"10.21203/rs.3.rs-6966189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-01T06:31:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T11:48:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227589892590100425603120813835807456256","date":"2025-09-24T17:04:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T04:27:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227185492383368360757199382194980416124","date":"2025-09-22T13:09:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158165347824364972173151982572776806841","date":"2025-09-12T03:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T12:48:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-11T11:14:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-26T10:27:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-26T06:00:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-24T13:09:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc52ab34-d78c-460f-87a1-554f803a6691","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54815365,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":54815366,"name":"Health sciences/Oncology/Cancer/Breast cancer"}],"tags":[],"updatedAt":"2025-12-15T16:02:26+00:00","versionOfRecord":{"articleIdentity":"rs-6966189","link":"https://doi.org/10.1038/s41598-025-31322-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-08 15:57:55","publishedOnDateReadable":"December 8th, 2025"},"versionCreatedAt":"2025-09-22 09:51:51","video":"","vorDoi":"10.1038/s41598-025-31322-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-31322-5","workflowStages":[]},"version":"v1","identity":"rs-6966189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6966189","identity":"rs-6966189","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}