Predicting of Ki-67 Expression Level Using Diffusion-Weighted and Synthetic Magnetic Resonance Imaging in Invasive Ductal Breast Cancer

Zhang, Liying; Hao, Jisen; Guo, Jia; Zhao, Xin; Yin, Xing

doi:https://doi.org/10.1155/2023/6746326

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Abstract Introduction Methods Results Discussion Conclusions Data Availability Ethical Approval Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 6746326 | https://doi.org/10.1155/2023/6746326

Predicting of Ki-67 Expression Level Using Diffusion-Weighted and Synthetic Magnetic Resonance Imaging in Invasive Ductal Breast Cancer

Liying Zhang,¹Jisen Hao,¹Jia Guo,¹Xin Zhao,¹and Xing Yin¹

Academic Editor: Pranshu Sahgal

Received24 Oct 2022

Revised26 Feb 2023

Accepted27 Mar 2023

Published05 Apr 2023

Abstract

Objectives. To investigate the association between quantitative parameters generated using synthetic magnetic resonance imaging (SyMRI) and diffusion-weighted imaging (DWI) and Ki-67 expression level in patients with invasive ductal breast cancer (IDC). Method. We retrospectively reviewed the records of patients with IDC who underwent SyMRI and DWI before treatment. Precontrast and postcontrast relaxation times (T1, longitudinal; T2, transverse), proton density (PD) parameters, and apparent diffusion coefficient (ADC) values were measured in breast lesions. Univariate and multivariate regression analyses were performed to screen for statistically significant variables to differentiate the high (≥30%) and low (<30%) Ki-67 expression groups. Their performance was evaluated by receiver operating characteristic (ROC) curve analysis. Results. We analyzed 97 patients. Multivariate regression analysis revealed that the high Ki-67 expression group (n = 57) had significantly higher parameters generated using SyMRI (pre-T1, ) and lower ADC values () compared with the low Ki-67 expression group (n = 40). Pre-T1 showed the best diagnostic performance for predicting the Ki-67 expression level in patients with invasive ductal breast cancer (areas under the ROC curve (AUC), 0.711; 95% confidence interval (CI), 0.609–0.813). Conclusions. Pre-T1 could be used to predict the pretreatment Ki-67 expression level in invasive ductal breast cancer.

1. Introduction

Breast cancer is a highly heterogeneous disease and the most frequently diagnosed cancer in almost all regions of the world [1, 2]. Because the various molecular subtypes of breast cancer lead to different clinical outcomes, molecular typing is critical for the accurate diagnosis and treatment of affected patients. According to the St. Gallen 2013 immunohistochemical (IHC) classification, IHC biomarkers, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki-67 can be used as molecular subtypes in breast cancer [3].

Ki-67 is a nuclear DNA-binding protein highly expressed in the G1, S, and G2 phases of the cell cycle but not in the quiescent G0 phase. This protein is a clinically important proliferation marker used for grading various cancers [4, 5]. Clinical studies demonstrated that patients with a high Ki-67 index showed a favorable response to chemotherapy but had a relatively poor prognosis [4, 6]. Thus, timely identification of this population is important for appropriate treatment planning. Currently, we determine the preoperative Ki-67 proliferation index using IHC, which requires sufficient tumor tissue, typically obtained by core needle biopsy. However, biopsies are invasive and cannot be used to evaluate the status of the entire lesion or follow changes in the tumor microenvironment after neoadjuvant therapy [5, 7]. Furthermore, it is impossible to obtain tumor tissue specimens in many cases.

Magnetic resonance imaging (MRI) is a noninvasive tool that can assess multiple biomarkers in patients with breast cancer [8, 9]. The use of several advanced sequences in probing breast cancer biology, including diffusion-weighted imaging (DWI) and synthetic MRI (SyMRI) is still under investigation.

DWI is a noncontrast imaging technique that characterizes tissues by their random movement of water molecules within them [10–12]. The apparent diffusion coefficient (ADC) derived from DWI is a very promising quantitative parameter for the differential diagnosis of breast lesions and monitoring the effect of neoadjuvant treatment [13, 14]. However, while some studies analyzed associations between DWI and histopathological features in breast cancer, including between ADC and the expression of Ki-67, their findings were quite divergent. For example, some authors [5, 15] reported significant correlations between ADC and Ki-67 status, but others [16–18] did not identify this relationship.

SyMRI is a compilation pulse sequence that uses a multidynamic and multiecho (MDME) acquisition method. SyMRI provides absolute values for tissue properties, such as T1 and T2 relaxation times and proton density (PD), from a single acquisition with good accuracy and reproducibility, even across instruments from different vendors [19–23]. Thus far, some studies have reported on the value of SyMRI in discriminating breast lesions [24–27]. However, findings describing the relationships between the parameters generated using SyMRI and Ki-67 status are scarce and inconsistent.

Given the above facts, further assessment of these potential imaging biomarkers is warranted. Therefore, we investigated, for the first time, the ability of quantitative parameters derived from DWI and SyMRI to accurately predict the pretreatment Ki-67 expression level in invasive ductal breast cancer (IDC).

2. Material and Methods

2.1. Patients

Our institutional review board approved this retrospective study and waived the requirement for informed consent. We enrolled consecutive female patients who underwent breast MRI (including SyMRI and DWI) for a breast lesion that was subsequently diagnosed as histopathological IDC after excision surgery or core needle biopsy between January 2020 and August 2022. The exclusion criteria were as follows: (1) neoadjuvant treatment before breast MRI, (2) incomplete scans, (3) nonmass enhancement lesions on dynamic contrast-enhanced MRI (DCE-MRI), and (4) lesions too small to be identified by the SyMRI or DWI sequence. For patients with multiple lesions, only the largest mass on the DCE-MRI was used for analysis. The patient selection is summarized in Figure 1.

2.2. MRI Protocol

All breast MR images were acquired using a 3T MRI scanner (Signa Pioneer, GE Healthcare, Chicago, IL, US) with an 8-channel phased-array breast coil. The patients entered the scanner feet first in the prone position. We used a power injector to administer a body weight-adjusted dose of Gd-DTPA (0.1 mmol/kg) intravenously at a rate of 3.0 ml/s to each patient, followed by 20 ml of saline flush at the same rate.

The scanning protocol consisted of conventional and quantitative scanning. T1- and T2-weighted images and DCE-MRI images were acquired. DCE-MRI was obtained using 3-dimensional (3D) differential subsampling with Cartesian ordering (DISCO) technology. Quantitative MR images included those obtained using SyMRI and DWI. SyMRI was performed before and after enhancement using a 2D fast spin-echo MDME sequence. The scan parameters are listed in Table 1.

2.3. Image Analysis

Two radiologists, each with more than seven years of breast MRI experience, performed a consensus review of the MRI findings. Lesion features, including size, shape, margin, enhancement pattern, and kinetic curves, were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon.

Synthetic MRI data were postprocessed using SyMRI 8.0 software (Synthetic MR, Linköping, Sweden). Using DCE-MRI for lesion localization, a region of interest (ROI) was manually drawn to cover the enhancing solid portion of the lesion on approximately the same slice on the synthetic images and ADC maps. For each radiologist, the best quality image among the synthetic images was chosen for analysis, and values for T1, T2 relaxation time, and PD were simultaneously calculated within an ROI. All of the quantitative parameters (T1, T2, PD, and ADC values) were automatically produced in the workstation. The average measurements of the two radiologists were used for analysis. We recorded preparameters (pre-T1, pre-T2, and pre-PD) and postparameters (post-T1, post-T2, and post-PD generated from SyMRI.

2.4. Histopathologic Analysis

Pathologic reports were reviewed to identify lymph node metastasis and histologic grade. The histological grade was assessed using the Elston–Ellis system. ER, PR, and HER2 and the Ki-67 expression level were evaluated by IHC. ER and PR studies were considered positive when at least 1% of the tumor cells showed positive nuclear staining. The HER2 scanning intensity was scored as 0, 1+, 2+, or 3+; we considered 0 and 1+ negative, 2+ equivocal, and 3+ positive. An immunohistochemistry HER2 score of 2+ was further explored by in situ hybridization to determine HER2 gene amplification. A Ki-67 level ≥30% was considered high and a level <30% was considered low [28].

2.5. Statistical Analysis

Statistical analyses were performed using R language, version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria). The R packages doBy and ggplot2 were applied. All continuous variables are expressed as means ± standard deviation (SD), while categorical variables are shown as totals and proportions. The interobserver consistencies for all quantitative parameters between the two radiologists were evaluated with the intraclass correlation coefficient (ICC) analysis. Agreement was defined as good (ICC > 0.75), moderate (ICC = 0.5–0.75), or poor (ICC < 0.5). Clinicopathological and DCE-MRI features were compared using the Mann–Whitney U and chi-square tests. Quantitative parameters (T1, T2, PD, and ADC values) were performed both univariate and multivariate logistic regression analyses with a variable selection criterion of . We estimated the area under the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the quantitative parameters. For all tests, was considered statistically significant.

3. Results

3.1. Patients

We enrolled 136 patients. After applying our exclusion criteria, 97 patients (age range, 30–71 years; mean age, 49.5 years) were included in the study.

3.2. Clinicopathological and DCE-MRI Features

The Ki-67 proliferation indices obtained by analyses of core needle biopsies or surgically excised specimens ranged from 8% to 90%. We categorized 40 tumors (41.2%) as showing low proliferation (the low proliferation group) and the remaining 57 (58.8%) as demonstrating high proliferation (the high proliferation group) using the 30% cutoff. Our comparison of clinicopathological and DCE-MRI features between the two groups is shown in Table 2.

3.3. Interobserver Agreement on Quantitative Parameters

All ICCs between the two radiologists for the quantitative parameters generated from SyMRI and ADC maps were all greater than 0.75, indicating good agreements (range, 0.782–0.890; ).

3.4. Quantitative Parameters

Histopathological and imaging findings of tumors and their associations with the quantitative parameters generated from SyMRI and DWI are summarized in Table 3. The post-T1 values were significantly lower in the noncircumscribed lesions than those in the circumscribed lesions (). Furthermore, pre-T1, post-T1, and post-T2 in the high proliferation group were significantly higher than those in the low proliferation group (pre-T1, ; post-T1, ; post-T2, ), and ADC values in the high proliferation group were significantly lower than those in the low proliferation group (). Among these, multiple logistic regression analysis showed that pre-T1 (OR = 1.003; ) and ADC (OR = 0.035; ) values were statistically significant parameters in predicting the Ki-67 expression level (Table 4). Representative images from tumors with a low and high Ki-67 proliferation index are presented in Figures 2 and 3, respectively. Table 5 shows the ROC analysis of the quantitative parameters. The area under the curve (AUC) of pre-T1 had the greatest discriminative ability with the highest AUC (AUC = 0.711; 95% confidence interval (CI), 0.609–0.813) (Figure 4).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

Figure 2

47-year-old female with invasive ductal carcinoma (IDC) of the left breast. Ki-67 expression was 20%. Axial contrast enhanced T1-weighted image (T1WI) showed an irregular homogeneous enhancing mass with circumscribed margin (a) The quantitative parameters were measured on SyMRI-T1WI before ((b) pre-T1 = 1495 ms, pre-T2 = 77 ms, and pre-PD = 80.8 pu) and after ((c) post-T1 = 683 ms, post-T2 = 69 ms, and post-PD = 67.4 pu) administration of the contrast medium. Similar images are shown for the mass on precontrast ((d) T1 mapping, (e) T2 mapping, and (f) PD mapping) and postcontrast ((g) T1 mapping, (h) T2 mapping, and (i) PD mapping) enhanced mapping images. The apparent diffusion coefficient (ADC) value was 0.936 × 10⁻³ mm²/s on the ADC map image (j).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

Figure 3

51-year-old female with invasive ductal carcinoma (IDC) of the right breast. Ki-67 expression was 50%. Axial contrast enhanced T1-weighted image (T1WI) showed an irregular heterogeneous enhancing mass with irregular margin (a) The quantitative parameters were measured on SyMRI-T2WI before ((b) pre-T1 = 1509 ms, pre-T2 = 80 ms, and pre-PD = 82 pu) and after ((c) post-T1 = 703 ms, post-T2 = 72 ms, and post-PD = 72.1 pu) administration of the contrast medium. Similar images are shown for the mass on precontrast ((d) T1 mapping, (e) T2 mapping, and (f) PD mapping) and postcontrast ((g) T1 mapping (h) T2 mapping, and (i) PD mapping) enhanced mapping images. The apparent diffusion coefficient (ADC) value was 0.763 × 10⁻³ mm²/s on the ADC map image (j).

4. Discussion

We compared multiple quantitative parameters (T1, T2, PD, and ADC values) to determine the expression level of Ki-67 in breast IDC. The results showed that the pre-T1 values were a significant predictor for the pretreatment Ki-67 expression level.

Matsuda et al. [29] reported that only the T1-Gd SD obtained from SyMRI was useful to predict Ki-67 status. However, their study only included patients with ER + breast cancer, and they used 14% as a cutoff value to define their low and high Ki-67 proliferation groups. According to the International Ki-67 in Breast Cancer Working Group (IKWG) Consensus Meeting Guideline (2019) [28], Ki-67 analysis should only be used to guide clinical decisions with 5% or less or 30% or more. Therefore, a reevaluation of the previous studies on associations between imaging parameters and Ki-67 expression was needed. We classified IDC lesions into two groups: low Ki-67 group (Ki-67 < 30%) and high Ki-67 group (Ki-67 ≥ 30%) in this study.

T1 values have been suggested to reflect extracellular expansion and underlying pathophysiological processes, using intrinsic and fundamental tissue property change [30, 31]. A previous research showed that hyperproliferative cancers (i.e., with high Ki-67 expression) might outgrow the oxygen supply of their vascular system, resulting in cell necrosis [32]. Therefore, we infer that there may be different native T1 values between the Ki-67 low and high groups. In the present study, tumors with high Ki-67 expression had higher native T1 values than those with low Ki-67 expression, and pre-T1 values differed significantly between the groups. The ROC analysis showed that a possible use of pre-T1 for discrimination of Ki-67 expression level in IDC has a relatively high AUC (AUC = 0.711). These results suggest the potential of pre-T1 values as a noninvasive surrogate biomarker for estimation of Ki-67 expression level.

Du et al. [33] showed that pre-T2 values had a statistically significant difference between the groups, and they used 14% as a Ki-67 cutoff value. In contrast to their results, we did not find a statistically significant association between pre-T2 values and Ki-67 expression level using 30% as a Ki-67 cutoff value.

Furthermore, our results showed that the post-T1 values were significantly lower in the noncircumscribed lesions than in the circumscribed lesions (). Further studies are required to address the meaning of this result. Nevertheless, we did not find a correlation between the quantitative parameters generated from SyMRI after contrast agent injection and the Ki-67 expression level. This may be because our study did not include all the different molecular subtypes of breast cancer.

Another interesting aspect of this study is the fact that ADC values cannot be used as a surrogate marker for proliferation activity in IDC. Our results showed that ADC values in the high proliferation group were significantly lower than those in the low proliferation group (). In spite of this, the ROC curve was not found to be useful in diagnostics (AUC = 0.683). The results are consistent with those reported previously [15, 34].

Our study had several limitations. First, patients were enrolled from a single institution with a limited number of relevant cases. Therefore, further studies with a large sample size are required to validate our findings. Second, patients with small lesions that could not be accurately located based on SyMRI were excluded from enrollment, possibly causing sampling bias. Third, we did not apply automatic image registration before and after enhancement to correct for the offset position between SyMRI and DWI, which could have reduced the stability of the analyzed data. Further study should be performed to confirm the accuracy of our findings.

5. Conclusions

In summary, synthetic MRI maybe a useful tool that can be used to predict the Ki-67 expression level in patients with IDC.

Data Availability

All data generated or analyzed during this study are included in this article.

Ethical Approval

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved (A research study was carried out in accordance with the Declaration of Helsinki (as revised in 2013).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the Medical Science and Technology Plan Joint-Project of Henan Province (grant number LHGJ20200468).

References

K. Barzaman, J. Karami, Z. Zarei et al., “Breast cancer: biology, biomarkers, and treatments,” International Immunopharmacology, vol. 84, Article ID 106535, 2020.
View at: Google Scholar
J. Ferlay, M. Colombet, I. Soerjomataram et al., “Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods,” International Journal of Cancer, vol. 144, no. 8, pp. 1941–1953, 2019.
View at: Google Scholar
A. Garcia Fernandez, C. Chabrera, M. Garcia Font et al., “Differential patterns of recurrence and specific survival between luminal A and luminal B breast cancer according to recent changes in the 2013 St Gallen immunohistochemical classification,” Clinical and Translational Oncology, vol. 17, no. 3, pp. 238–246, 2015.
View at: Publisher Site | Google Scholar
S. S. Menon, C. Guruvayoorappan, K. M. Sakthivel, and R. R. Rasmi, “Ki-67 protein as a tumour proliferation marker,” Clinica Chimica Acta, vol. 491, pp. 39–45, 2019.
View at: Publisher Site | Google Scholar
Y. Zhang, Y. Zhu, K. Zhang et al., “Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps,” Radiologia Medica, La, vol. 125, no. 2, pp. 109–116, 2020.
View at: Publisher Site | Google Scholar
B. Acs, V. Zambo, L. Vizkeleti et al., “Ki-67 as a controversial predictive and prognostic marker in breast cancer patients treated with neoadjuvant chemotherapy,” Diagnostic Pathology, vol. 12, no. 1, p. 20, 2017.
View at: Publisher Site | Google Scholar
D. Leithner, J. V. Horvat, M. A. Marino et al., “Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results,” Breast Cancer Research, vol. 21, no. 1, p. 106, 2019.
View at: Publisher Site | Google Scholar
N. Amornsiripanitch, V. T. Nguyen, H. Rahbar et al., “Diffusion-weighted MRI characteristics associated with prognostic pathological factors and recurrence risk in invasive ER+/HER2- breast cancers,” Journal of Magnetic Resonance Imaging, vol. 48, no. 1, pp. 226–236, 2018.
View at: Publisher Site | Google Scholar
M. I. Tsarouchi, G. F. Vlachopoulos, A. N. Karahaliou, K. G. Vassiou, and L. I. Costaridou, “Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis,” Physica Medica, vol. 80, pp. 101–110, 2020.
View at: Publisher Site | Google Scholar
S. C. Partridge, N. Nissan, H. Rahbar, A. E. Kitsch, and E. E. Sigmund, “Diffusion-weighted breast MRI: clinical applications and emerging techniques,” Journal of Magnetic Resonance Imaging, vol. 45, no. 2, pp. 337–355, 2017.
View at: Publisher Site | Google Scholar
S. C. Partridge, Z. Zhang, D. C. Newitt et al., “Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: the ACRIN 6698 multicenter trial,” Radiology, vol. 289, no. 3, pp. 618–627, 2018.
View at: Publisher Site | Google Scholar
R. M. Mann, N. Cho, and L. Moy, “Breast MRI: state of the art,” Radiology, vol. 292, no. 3, pp. 520–536, 2019.
View at: Publisher Site | Google Scholar
P. Baltzer, R. M. Mann, M. Iima et al., “Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group,” European Radiology, vol. 30, no. 3, pp. 1436–1450, 2020.
View at: Publisher Site | Google Scholar
D. Liu, Z. Ba, X. Ni, L. Wang, D. Yu, and X. Ma, “Apparent diffusion coefficient to subdivide breast imaging reporting and data system magnetic resonance imaging (BI-RADS-MRI) category 4 lesions,” Medical Science Monitor, vol. 24, pp. 2180–2188, 2018.
View at: Publisher Site | Google Scholar
L. Shen, G. Zhou, T. Tong et al., “ADC at 3.0T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast,” Clinical Imaging, vol. 52, pp. 16–22, 2018.
View at: Publisher Site | Google Scholar
H. Aydin, B. Guner, I. Esen Bostanci et al., “Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma?” British Journal of Radiology, vol. 91, no. 1084, Article ID 20170705, 2018.
View at: Publisher Site | Google Scholar
S. K. Jeh, S. H. Kim, H. S. Kim et al., “Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma,” Journal of Magnetic Resonance Imaging, vol. 33, no. 1, pp. 102–109, 2011.
View at: Publisher Site | Google Scholar
A. Surov, Y. W. Chang, L. Li et al., “Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis,” BMC Cancer, vol. 19, no. 1, p. 1043, 2019.
View at: Publisher Site | Google Scholar
C. Andica, A. Hagiwara, M. Hori et al., “Review of synthetic MRI in pediatric brains: basic principle of MR quantification, its features, clinical applications, and limitations,” Journal of Neuroradiology, vol. 46, no. 4, pp. 268–275, 2019.
View at: Publisher Site | Google Scholar
A. Hagiwara, M. Hori, J. Cohen-Adad et al., “Linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous relaxometry at 3 T: A validation study with a standardized phantom and healthy controls,” Investigative Radiology, vol. 54, no. 1, pp. 39–47, 2019.
View at: Publisher Site | Google Scholar
T. Maekawa, A. Hagiwara, M. Hori et al., “Effect of gadolinium on the estimation of myelin and brain tissue volumes based on quantitative synthetic MRI,” AJNR Am J Neuroradiol, vol. 40, no. 2, pp. 231–237, 2019.
View at: Publisher Site | Google Scholar
L. Saccenti, C. Andica, A. Hagiwara et al., “Brain tissue and myelin volumetric analysis in multiple sclerosis at 3T MRI with various in-plane resolutions using synthetic MRI,” Neuroradiology, vol. 61, no. 11, pp. 1219–1227, 2019.
View at: Publisher Site | Google Scholar
H. West, J. L. Leach, B. V. Jones et al., “Clinical validation of synthetic brain MRI in children: initial experience,” Neuroradiology, vol. 59, no. 1, pp. 43–50, 2017.
View at: Publisher Site | Google Scholar
W. Gao, S. Zhang, J. Guo et al., “Investigation of synthetic relaxometry and diffusion measures in the differentiation of benign and malignant breast lesions as compared to BI-rads,” Journal of Magnetic Resonance Imaging, vol. 53, no. 4, pp. 1118–1127, 2021.
View at: Publisher Site | Google Scholar
Y. Jung, S. M. Gho, S. N. Back, T. Ha, D. K. Kang, and T. H. Kim, “The feasibility of synthetic MRI in breast cancer patients: comparison of T2 relaxation time with multiecho spin echo T2 mapping method,” British Journal of Radiology, vol. 92, no. 1093, Article ID 20180479, 2018.
View at: Publisher Site | Google Scholar
M. Matsuda, T. Tsuda, R. Ebihara et al., “Enhanced masses on contrast-enhanced breast: differentiation using a combination of dynamic contrast-enhanced MRI and quantitative evaluation with synthetic MRI,” Journal of Magnetic Resonance Imaging, vol. 53, no. 2, pp. 381–391, 2021.
View at: Publisher Site | Google Scholar
T. Meng, N. He, H. He et al., “The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI,” Cancer Imaging, vol. 20, no. 1, p. 88, 2020.
View at: Publisher Site | Google Scholar
T. O. Nielsen, S. C. Y. Leung, D. L. Rimm et al., “Assessment of Ki67 in breast cancer: updated recommendations from the international Ki67 in breast cancer working group,” Journal of the National Cancer InstituteJournal of the National Cancer Institute, vol. 113, no. 7, pp. 808–819, 2021.
View at: Publisher Site | Google Scholar
M. Matsuda, T. Kido, T. Tsuda et al., “Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study,” Clinical Radiology, vol. 75, no. 5, pp. 398 e1–e8, 2020.
View at: Publisher Site | Google Scholar
L. C. Adams, B. Ralla, P. Jurmeister et al., “Native T1 mapping as an in vivo biomarker for the identification of higher-grade renal cell carcinoma: correlation with histopathological findings,” Investigative Radiology, vol. 54, no. 2, pp. 118–128, 2019.
View at: Publisher Site | Google Scholar
S. Yang, F. Shan, Q. Yan et al., “A pilot study of native T1-mapping for focal pulmonary lesions in 3.0 T magnetic resonance imaging: size estimation and differential diagnosis,” Journal of Thoracic Disease, vol. 12, no. 5, pp. 2517–2528, 2020.
View at: Publisher Site | Google Scholar
S. Y. Kim, E. K. Kim, H. J. Moon et al., “Association among T2 signal intensity, necrosis, ADC and Ki-67 in estrogen receptor-positive and HER2-negative invasive ductal carcinoma,” Magnetic Resonance Imaging, vol. 54, pp. 176–182, 2018.
View at: Publisher Site | Google Scholar
S. Du, S. Gao, L. Zhang, X. Yang, X. Qi, and S. Li, “Improved discrimination of molecular subtypes in invasive breast cancer: comparison of multiple quantitative parameters from breast MRI,” Magnetic Resonance Imaging, vol. 77, pp. 148–158, 2021.
View at: Publisher Site | Google Scholar
E. J. Kim, S. H. Kim, G. E. Park et al., “Histogram analysis of apparent diffusion coefficient at 3.0t: correlation with prognostic factors and subtypes of invasive ductal carcinoma,” Journal of Magnetic Resonance Imaging, vol. 42, no. 6, pp. 1666–1678, 2015.
View at: Publisher Site | Google Scholar

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Copyright © 2023 Liying Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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