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• Clinical Article •
Venous phase spectral CT parameters combined with MRI for preoperative assessment of Ki-67 expression levels in breast tumors
ZHI Yunfei  YU Xiaoqing  TANG Weixia  WANG Tianle  SHENG Meihong 

Cite this article as: ZHI Y F, YU X Q, TANG W X, et al. Venous phase spectral CT parameters combined with MRI for preoperative assessment of Ki-67 expression levels in breast tumors[J]. Chin J Magn Reson Imaging, 2026, 17(10): 102-108, 116. DOI:10.12015/issn.1674-8034.2026.10.012.


[Abstract] Objective To investigate the preoperative value of venous-phase spectral computed tomography (CT) quantitative parameters combined with magnetic resonance imaging (MRI) parameters for predicting Ki-67 expression levels in breast tumors and to compare the predictive performance of single-modality and combined models.Materials and Methods A total of 104 consecutive patients with suspected malignant breast tumors who underwent venous-phase contrast-enhanced spectral CT and breast MRI examinations (including dynamic contrast-enhanced imaging and diffusion-weighted imaging) before surgery or biopsy between October 2023 and October 2025 were prospectively enrolled. After screening according to the inclusion and exclusion criteria, 100 patients were included in the final analysis. Based on the immunohistochemical Ki-67 proliferation index, patients were divided into a high-expression group (≥ 20%, n = 58) and a low-expression group (< 20%, n = 42). Spectral CT parameters included iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number (Zeff), normalized effective atomic number (nZeff), and spectral curve slope (λHU). MRI features included early enhancement value, apparent diffusion coefficient (ADC), and time–signal intensity curve (TIC) pattern. Variables with significant differences identified by univariate analysis were entered into logistic regression analyses. A spectral CT model incorporating NIC, an MRI model incorporating ADCmean, and a combined model integrating NIC and ADCmean were established. Receiver operating characteristic (ROC) curve analysis was performed to evaluate and compare the diagnostic performance of the models using the area under the curve (AUC).Results Compared with the low-expression group, the high-expression group showed significantly higher CT40keV, IC, NIC, Zeff, nZeff, λHU, and MRI lower ADCmean values (all P < 0.05). The combined model, incorporating NIC and ADCmean, achieved an AUC of 0.877(95% CI: 0.805 to 0.953), outperforming both the MRI model (AUC = 0.819; 95% CI: 0.725 to 0.913) and the spectral CT model (AUC = 0.713; 95% CI: 0.609 to 0.817).Conclusions Venous-phase spectral CT quantitative parameters and MRI features provide complementary information for assessing Ki-67 expression in breast tumors. The combined model significantly improves the preoperative prediction of high Ki-67 expression and may serve as a valuable imaging-based tool for clinical risk stratification and treatment decision-making.
[Keywords] breast tumor；Ki-67；spectral computed tomography；magnetic resonance imaging；apparent diffusion coefficient

ZHI Yunfei   YU Xiaoqing   TANG Weixia   WANG Tianle   SHENG Meihong*  

Department of Radiology, Affiliated Nantong Clinical College of Nantong University, Nantong First People's Hospital, School of Medicine, Nantong University, Nantong 226001, China

Corresponding author: SHENG M H, E-mail: smh4127@163.com

Conflicts of interest   None.

Received  2026-02-28
Accepted  2026-08-21
DOI: 10.12015/issn.1674-8034.2026.10.012
Cite this article as: ZHI Y F, YU X Q, TANG W X, et al. Venous phase spectral CT parameters combined with MRI for preoperative assessment of Ki-67 expression levels in breast tumors[J]. Chin J Magn Reson Imaging, 2026, 17(10): 102-108, 116. DOI:10.12015/issn.1674-8034.2026.10.012.

[1]
GIAQUINTO A N, SUNG H, NEWMAN L A, et al. Breast cancer statistics 2024[J]. CA A Cancer J Clin, 2024, 74(6): 477-495. DOI: 10.3322/caac.21863.
[2]
SIEGEL R L, KRATZER T B, GIAQUINTO A N, et al. Cancer statistics, 2025[J]. CA A Cancer J Clin, 2025, 75(1): 10-45. DOI: 10.3322/caac.21871.
[3]
LEON-FERRE R A, JONAS S F, SALGADO R, et al. Tumor-infiltrating lymphocytes in triple-negative breast cancer[J/OL]. Jama, 2024, 331(13): 1135 [2026-08-20]. https://jamanetwork.com/journals/jama/fullarticle/2816923. DOI: 10.1001/jama.2024.3056.
[4]
HOHMANN L, NACER D F, AINE M, et al. Molecular profiling of the Basal-like intrinsic molecular subtype in primary ER-positive HER2-negative breast cancer[J/OL]. Genome Med, 2025, 17(1): 146 [2026-08-20]. https://link.springer.com/article/10.1186/s13073-025-01576-9. DOI: 10.1186/s13073-025-01576-9.
[5]
WENG L J, ZHOU J L, GUO S C, et al. The molecular subtyping and precision medicine in triple-negative breast cancer: -based on Fudan TNBC classification[J/OL]. Cancer Cell Int, 2024, 24(1): 120 [2026-08-20]. https://pubmed.ncbi.nlm.nih.gov/38555429/. DOI: 10.1186/s12935-024-03261-0.
[6]
OKCU O, ÖZTÜRK Ç, ŞEN B. Tumor budding and Ki-67 proliferation index as biomarkers for NAC response and prognosis in breast cancer[J]. Future Oncol, 2025, 21(22): 2885-2893. DOI: 10.1080/14796694.2025.2547407.
[7]
ZILENAITE-PETRULAITIENE D, RASMUSSON A, BESUSPARIS J, et al. Intratumoral heterogeneity of Ki67 proliferation index outperforms conventional immunohistochemistry prognostic factors in estrogen receptor-positive HER2-negative breast cancer[J]. Virchows Arch, 2025, 486(2): 287-298. DOI: 10.1007/s00428-024-03737-4.
[8]
LOIBL S, ANDRÉ F, BACHELOT T, et al. Early breast cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up[J]. Ann Oncol, 2024, 35(2): 159-182. DOI: 10.1016/j.annonc.2023.11.016.
[9]
YONG K R, LEE S, UPPALURI A, et al. Spectral computed tomography (CT) in breast lesion evaluation: clinical utility and imaging spectrum: a pictorial review[J/OL]. Clin Radiol, 2026, 92: 107161 [2026-08-20]. https://www.clinicalradiologyonline.net/article/S0009-9260(25)00366-6/abstract. DOI: 10.1016/j.crad.2025.107161.
[10]
DAVEY M G, HYNES S O, KERIN M J, et al. Ki-67 as a prognostic biomarker in invasive breast cancer[J/OL]. Cancers, 2021, 13(17): 4455 [2026-08-20]. https://pubmed.ncbi.nlm.nih.gov/34503265/. DOI: 10.3390/cancers13174455.
[11]
CHEN L J, XIAO Z Y, FU J M, et al. The diagnostic performance of dual-layer spectral detector CT for distinguishing breast cancer biomarker expression and molecular subtypes[J/OL]. Sci Rep, 2024, 14: 1500 [2026-08-20]. https://www.nature.com/articles/s41598-024-51285-3. DOI: 10.1038/s41598-024-51285-3.
[12]
WU Z X, ZHOU Z L, YANG P P, et al. The value of spectral CT combined with MRI in differentiating Luminal and non-Luminal breast cancer[J]. Zhejiang Med J, 2025, 47(20): 2176-2181. DOI: 10.12056/j.issn.1006-2785.2025.47.20.2025-422.
[13]
WANG X X, LIU D H, ZENG X F, et al. Dual-energy CT quantitative parameters for evaluating Immunohistochemical biomarkers of invasive breast cancer[J/OL]. Cancer Imaging, 2021, 21(1): 4 [2026-08-20]. https://pubmed.ncbi.nlm.nih.gov/33413654/. DOI: 10.1186/s40644-020-00370-7.
[14]
ZHANG L Y, HAO J S, GUO J, et al. Predicting of ki-67 expression level using diffusion-weighted and synthetic magnetic resonance imaging in invasive ductal breast cancer[J/OL]. Breast J, 2023, 2023: 6746326 [2026-08-20]. https://pubmed.ncbi.nlm.nih.gov/37063453/. DOI: 10.1155/2023/6746326.
[15]
XU M Z, SHAN D Q, ZHANG R Z, et al. Differentiation of breast cancer subtypes and correlation with biological status using functional magnetic resonance imaging: comparison with amide proton transfer-weighted imaging and diffusion-weighted imaging[J]. Quant Imaging Med Surg, 2025, 15(7): 6102-6117. DOI: 10.21037/qims-24-2174.
[16]
The Society of Breast Cancer China AntiCancer Association, Breast Oncology Group of the Oncology Branch of the Chinese Medical Association, SHAO Z M, et al. Guidelines for breast cancer diagnosis and treatment by China Anti-Cancer Association (2026 edition)[J]. Oncoradiology, 2026, 35(1): 114-212. DOI: 10.19401/j.cnki.1007-3639.2025.12.009.
[17]
BAE K T. Intravenous contrast medium administration and scan timing at CT: considerations and approaches[J]. Radiology, 2010, 256(1): 32-61. DOI: 10.1148/radiol.10090908.
[18]
VALENTIN J. Managing patient dose in multi-detector computed tomography(MDCT). ICRP Publication 102[J]. Ann ICRP, 2007, 37(1): 1-79, iii. DOI: 10.1016/j.icrp.2007.09.001.
[19]
WILEY N, ZECEVIC M, HO V, et al. Dual-energy CT iodine concentration as a biomarker for immunotherapy treatment response in metastatic melanoma and renal cell carcinoma patients[J]. Eur Radiol, 2025, 35(8): 4417-4428. DOI: 10.1007/s00330-025-11351-4.
[20]
METIN N O, BALCı S, METIN Y, et al. Correlation between quantitative parameters obtained by dual energy spectral CT and prognostic histopathological factors and biomarkers in breast cancer[J/OL]. Clin Breast Cancer, 2024, 24(4): e279-e288 [2026-08-20]. https://www.clinical-breast-cancer.com/article/S1526-8209(24)00037-5/abstract. DOI: 10.1016/j.clbc.2024.01.022.
[21]
JOHNSON T R C, KRAUSS B, SEDLMAIR M, et al. Material differentiation by dual energy CT: initial experience[J]. Eur Radiol, 2007, 17(6): 1510-1517. DOI: 10.1007/s00330-006-0517-6.
[22]
MA Y C, ZHANG S H, XIE Z Y, et al. Comparison of spectral computed tomography imaging parameters between squamous cell carcinoma and adenocarcinoma at the gastroesophageal junction[J]. Technol Health Care, 2021, 29(4): 619-627. DOI: 10.3233/thc-202343.
[23]
GREFFIER J, VILLANI N, DEFEZ D, et al. Spectral CT imaging: Technical principles of dual-energy CT and multi-energy photon-counting CT[J]. Diagn Interv Imaging, 2023, 104(4): 167-177. DOI: 10.1016/j.diii.2022.11.003.
[24]
SUN J, HUANG Y Y, LU T, et al. Evaluation of Ki-67 expression levels in esophageal squamous cell carcinoma using dual-energy CT quantitative parameters[J/OL]. Front Oncol, 2025, 15: 1561256 [2026-08-20]. https://pubmed.ncbi.nlm.nih.gov/40852477/. DOI: 10.3389/fonc.2025.1561256.
[25]
LIN G H, CHEN W Y, CHEN C M, et al. Construction of prediction model combined dual-energy CT quantitative parameters and conventional CT features for assessing the Ki-67 expression levels in invasive breast cancer[J]. Natl Med J China, 2022, 102(23): 1753-1759. DOI: 10.3760/cma.j.cn112137-20220101-00006.
[26]
LIU F J, ZHANG S H, GAO Z, et al. Exploration of dynamic contrast-enhanced MRI and diffusion weighted imaging in predicting the expression of ki-67 state value[J]. J Clin Radiol, 2025, 44(2): 248-252. DOI: 10.13437/j.cnki.jcr.2025.02.005.
[27]
PICKLES M D, GIBBS P, LOWRY M, et al. Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer[J]. Magn Reson Imag, 2006, 24(7): 843-847. DOI: 10.1016/j.mri.2005.11.005.
[28]
YANKEELOV T E, GORE J C. Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples[J]. Curr Med Imaging Rev, 2009, 3(2): 91-107. DOI: 10.2174/157340507780619179.
[29]
TUAN LINH L, MINH DUC N, TRA MY T T, et al. Correlations between dynamic contrast-enhanced magnetic resonance imaging parameters and histopathologic factors in breast cancer[J]. Clin Ter, 2021, 172(5): 453-460. DOI: 10.7417/CT.2021.2358.
[30]
XIE F Y, LIN Z Q, ZHANG T T. The value of MRI quantitative parameters in the differential diagnosis of different molecular subtypes of breast cancer[J]. J Pract Radiol, 2022, 38(12): 1976-1981. DOI: 10.3969/j.issn.1002-1671.2022.12.016.
[31]
ZHANG L, SHEN M Y, ZHANG D Y, et al. Radiomics nomogram based on dual-sequence MRI for assessing ki-67 expression in breast cancer[J]. J Magn Reson Imaging, 2024, 60(3): 1203-1212. DOI: 10.1002/jmri.29149.
[32]
SUROV A, MEYER H J, WIENKE A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis[J]. Oncotarget, 2017, 8(35): 59492-59499. DOI: 10.18632/oncotarget.17752.
[33]
KIM C, LEE K Y, SHIN C, et al. Comparison of filtered back projection, hybrid iterative reconstruction, model-based iterative reconstruction, and virtual monoenergetic reconstruction images at both low- and standard-dose settings in measurement of emphysema volume and airway wall thickness: a CT phantom study[J]. Korean J Radiol, 2018, 19(4): 809-817. DOI: 10.3348/kjr.2018.19.4.809.
[34]
TAO S M, WANG L L, LI M D, et al. Cancer risk associated with low-dose ionizing radiation: a systematic review of epidemiological and biological evidence[J/OL]. Mutat Res Rev Mutat Res, 2024, 794: 108517 [2026-08-20]. https://www.sciencedirect.com/science/article/abs/pii/S1383574224000309via%3Dihub. DOI: 10.1016/j.mrrev.2024.108517.

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