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• Clinical Article •
Value of MRI based on haemodynamic parameters and apparent diffusion coefficient in the differential diagnosis of breast phyllodes tumours and fibroadenomas
XIE Yidai-sulitan  WEI Zifan  CHEN Yuxuan  WENG Chunjiao  JIANG Nan  HU Chunhong  MA Xinxing 

Cite this article as: XIE Y S, WEI Z F, CHEN Y X, et al. Value of MRI based on haemodynamic parameters and apparent diffusion coefficient in the differential diagnosis of breast phyllodes tumours and fibroadenomas[J]. Chin J Magn Reson Imaging, 2025, 16(7): 22-29. DOI:10.12015/issn.1674-8034.2025.07.004.


[Abstract] Objective To investigate the diagnostic value of magnetic resonance imaging (MRI), based on morphological features, hemodynamic characteristics and apparent diffusion coefficient (ADC), in differentiating phyllodes tumors (PT) from fibroadenomas (FA) of the breast.Materials and Methods MRI data of 26 pathologically confirmed PT cases (26 lesions) and 53 FA cases (59 lesions) were retrospectively analyzed. The morphological features, dynamic contrast-enhanced MRI (DCE-MRI) parameters, mean ADC, and relative apparent diffusion coefficient (rADC) between the two groups were measured and calculated. The χ2 test and independent sample t test were used to assess intergroup differences. Logistic regression was used to establish a combined model. The non-parametric receiver operating characteristic (ROC) curves were generated for mean ADC value and rADC values. The DeLong test was employed to compare the differences in diagnostic efficacy between mean ADC value, rADC values and the combined model. Calibration curve was drawn to evaluate the model's consistency, and finally assessed the model's clinical application value through decision curve analysis (DCA).Results The mean age of patients in the PT group (39.92 ± 8.96 years) was significantly higher than that in the FA group (33.37 ± 10.22 years) (P < 0.05). PT lesions exhibited more lobulated shape, irregular or spiculated margins, low-signal segregation in T2-weighted imaging (T2WI), and cystic degeneration or necrosis. DCE-MRI showed fast enhancement in the initial phase and heterogeneous enhancement, with time-intensity curves (TIC) type II compared to FAs. The mean ADC, rADC1, and rADC2 values of the PTs were (1.500 ± 0.153) ×10⁻³ mm²/s, 0.870 ± 0.070 and 0.760 ± 0.070, these values were significantly lower than those of the FAs (P < 0.05). These values were statistically significant (P < 0.05). The diagnostic threshold of mean ADC value was 1.525×10⁻³ mm²/s, the area under the curve (AUC) was 0.730, the sensitivity was 65.4%, and the specificity was 83.1%. The sensitivity of the rADC value was higher than the mean ADC value, but its specificity was lower. The diagnostic threshold of rADC1 value was 0.923 corresponding to AUC was 0.791, the diagnostic threshold of rADC2 value was 0.847, the corresponding AUC was reduced to 0.647, and the diagnostic threshold of the combined model (lesion margin, T2WI low signal separation feature, rADC1 value) was 0.636 corresponding to AUC was 0.904, with sensitivity of 91.5% and specificity of 80.8%. The DeLong test was used to compare the AUC differences between rADC1 value, rADC2 and the combined model, and the diagnostic performance of the combined model was better than rADC1 value (P = 0.007) and rADC2 value (P < 0.001). The calibration curve demonstrated excellent agreement between the predicted probability of the combined model and actual outcome. DCA further confirmed the superior clinical utility of the combined model, providing a higher net benefit than single-parameter models across threshold probabilities of 0.2 to 0.9.Conclusions The multiparametric MRI model based on hemodynamic features (tumor margins, T2WI hypointense septations) and rADC1 values can effectively predict PT preoperatively, demonstrating favorable discriminative ability, calibration accuracy, and clinical utility. This approach facilitates clinical diagnosis, treatment planning, and prognosis improvement for PT.
[Keywords] phyllodes tumor of the breast；fibroadenoma of the breast；apparent diffusion coefficient；dynamic contrast-enhanced；magnetic resonance imaging

XIE Yidai-sulitan1, 2   WEI Zifan1, 3   CHEN Yuxuan1, 3   WENG Chunjiao1   JIANG Nan1   HU Chunhong1   MA Xinxing1*  

1 Department of Radiology, the First Affiliated Hospital of Suzhou University, Suzhou 215006, China

2 Department of Radiology, Xinjiang Kezhou People's Hospital, Kizilsu Kirghiz Autonomous Prefecture 845350, China

3 Department of Medical Imaging Science, Suzhou Medical College of Soochow University, Suzhou 215006, China

Corresponding author: MA X X, E-mail: xinxingma@suda.edu.cn

Conflicts of interest   None.

Received  2025-04-01
Accepted  2025-06-10
DOI: 10.12015/issn.1674-8034.2025.07.004
Cite this article as: XIE Y S, WEI Z F, CHEN Y X, et al. Value of MRI based on haemodynamic parameters and apparent diffusion coefficient in the differential diagnosis of breast phyllodes tumours and fibroadenomas[J]. Chin J Magn Reson Imaging, 2025, 16(7): 22-29. DOI:10.12015/issn.1674-8034.2025.07.004.

[1]
TSUCHIYA M, MASUI T, TERAUCHI K, et al. MRI-based radiomics analysis for differentiating Phyllodes tumors of the breast from fibroadenomas[J]. Eur Radiol, 2022, 32(6): 4090-4100. DOI: 10.1007/s00330-021-08510-8.
[2]
SHAO M, ZHANG L, LI X, et al. Breast Phyllodes tumour with epithelioid feature predisposes to malignant transformation[J]. J Clin Pathol, 2025, 78(6): 390-398. DOI: 10.1136/jcp-2024-209489.
[3]
VALENZA C, DE PAS T M, GAETA A, et al. Primary malignant Phyllodes tumors of the breast: a retrospective analysis from a referral center[J/OL]. Eur J Cancer, 2024, 196: 113423 [2025-03-30]. https://pubmed.ncbi.nlm.nih.gov/37977104/. DOI: 10.1016/j.ejca.2023.113423.
[4]
CHEN C, SUN Q, LI Y. Consensus on the diagnosis and treatment of Phyllodes tumor of the breast for Chinese women[J]. Chin Res Hosp, 2023, 10(4): 1-14. DOI: 10.19450/j.cnki.jcrh.2023.04.001.
[5]
ZHANG C Z, WANG C Y, JIN X. CDFI combined with MRI in the differential diagnosis of breast fibroadenoma and small-volume phyllode tumor of breast[J]. Chin J CT MRI, 2021, 19(10): 91-94. DOI: 10.3969/j.issn.1672-5131.2021.10.029.
[6]
AN R, DING H Y. Diagnosis and differential diagnosis of Phyllodes tumor of breast[J]. Chin J Diagn Pathol, 2022, 29(5): 477-480. DOI: 10.3969/j.issn.1007-8096.2022.05.027.
[7]
ZHANG M, ARJMANDI F K, POREMBKA J H, et al. Imaging and management of fibroepithelial lesions of the breast: radiologic-pathologic correlation[J/OL]. Radiographics, 2023, 43(11): e230051 [2025-03-30]. https://pubmed.ncbi.nlm.nih.gov/37856317/. DOI: 10.1148/rg.230051.
[8]
SHAO M M, BI J X. Thinking and practical experience of pathological diagnostic criteria for Phyllodes tumor of breast[J]. Chin J Clin Exp Pathol, 2024, 40(9): 907-912. DOI: 10.13315/j.cnki.cjcep.2024.09.003.
[9]
KAMITANI T, MATSUO Y, YABUUCHI H, et al. Differentiation between benign Phyllodes tumors and fibroadenomas of the breast on MR imaging[J]. Eur J Radiol, 2014, 83(8): 1344-1349. DOI: 10.1016/j.ejrad.2014.04.031.
[10]
FENG H, ZHONG J Q, WU H P, et al. Differential diagnosis value of magnetic resonance diffusionweighted imaging for suspicious breast cancer[J]. Chin J Magn Reson Imag, 2020, 11(5): 364-366. DOI: 10.12015/issn.1674-8034.2020.05.009.
[11]
XU L, WANG D B, WANG L J, et al. A comparative study in the differential diagnosis of benign and malignant breast diseases using DWI apparent diffusion coefficient and the relative apparent diffusion coefficient[J]. Radiol Pract, 2014, 29(10): 1103-1107. DOI: 10.13609/j.cnki.1000-0313.2014.10.002.
[12]
AJMAL M, KHAN M, VAN FOSSEN K. Breast fibroadenoma[M]. Statpearls. Treasure Island, 2025.
[13]
SHUANG P, SHENG F G, ZHOU J, et al. The radiologic features of pure invasive cribriform carcinoma of breast and its differential diagnosis with Phyllodes tumors[J]. J China Clin Med Imag, 2023, 34(10): 705-709. DOI: 10.12117/jccmi.2023.10.005.
[14]
LI T T, LI Y J, YANG Y Q, et al. Logistic regression analysis of ultrasound findings in predicting the malignant and benign Phyllodes tumor of breast[J/OL]. PLoS One, 2022, 17(3): e0265952 [2025-03-30]. https://pubmed.ncbi.nlm.nih.gov/35325009/. DOI: 10.1371/journal.pone.0265952.
[15]
WEI Z F, CHEN X, YANG Y W, et al. The potential role of breast MRI in evaluation of triple-negative breast cancer and fibroadenoma of less than 3 cm[J]. Transl Cancer Res, 2024, 13(8): 4042-4051. DOI: 10.21037/tcr-24-498.
[16]
PAPAS Y, ASMAR A E, GHANDOUR F, et al. Malignant Phyllodes tumors of the breast: a comprehensive literature review[J]. Breast J, 2020, 26(2): 240-244. DOI: 10.1111/tbj.13523.
[17]
XU J W, LI X X, YU T H, et al. Current status and research progress of diagnosis and treatment of Phyllodes tumor of the breast[J]. Chin J Gen Surg, 2022, 31(11): 1527-1534. DOI: 10.7659/j.issn.1005-6947.2022.11.015.
[18]
GANESH V, DROST L, LEE J, et al. A retrospective review of Phyllodes tumours of the breast: a single institution experience[J]. Breast, 2018, 38: 52-57. DOI: 10.1016/j.breast.2017.12.008.
[19]
LUO R, ZHAO Y E, WANG D B, et al. MRI features of Phyllodes tumor in breast with pathological correlation[J]. Chin J Magn Reson Imag, 2014, 5(4): 253-258. DOI: 10.3969/j.issn.1674-8034.2014.04.003.
[20]
PENG T F, HUANG B, LUO Y H, et al. Differentiation of breast benign and borderline Phyllodes tumors from fibroadenomas on MRI[J]. Chin J Magn Reson Imag, 2018, 9(11): 813-818. DOI: 10.12015/issn.1674-8034.2018.11.003.
[21]
MA X, GONG J, HU F, et al. Pretreatment multiparametric MRI‐based radiomics analysis for the diagnosis of breast phyllodes tumors[J]. Journal of Magnetic Resonance Imaging, 2023, 57(2): 633-645. DOI: 10.1002/jmri.28286.
[22]
WEI M Z, GU Y J, XIAO Q, et al. Imaging analysis of different pathological types of breast Phyllodes tumor[J]. Chin Comput Med Imag, 2013, 19(3): 217-221. DOI: 10.19627/j.cnki.cn31-1700/th.2013.03.007.
[23]
MAO Y, ZHANG W G, XU Y, et al. Differentiation of breast Phyllodes tumors from fibroadenomas on MRI[J]. J Clin Radiol, 2016, 35(1): 32-36. DOI: 10.13437/j.cnki.jcr.2016.01.009.
[24]
YUEN S, MONZAWA S, YANAI S, et al. The association between MRI findings and breast cancer subtypes: focused on the combination patterns on diffusion-weighted and T2-weighted images[J]. Breast Cancer, 2020, 27(5): 1029-1037. DOI: 10.1007/s12282-020-01105-z.
[25]
GUAN C G, SHAO S, ZHENG N, et al. Application value of radiomics based on DCE-MRI combined with DKI in predicting triple-negative breast cancer[J]. Chin J Magn Reson Imag, 2025, 16(2): 35-43. DOI: 10.12015/issn.1674-8034.2025.02.006.
[26]
WANG W W, LIU Y C, WANG Q. Value of 3.0T MRI-DWI in the differential diagnosis of breast fibladenoma and phyllode tumor of the breast[J]. Chin J CT MRI, 2023, 21(9): 114-116. DOI: 10.3969/j.issn.1672-5131.2023.09.038.
[27]
XUE M, LI J, ZHOU C W, et al. Differentiation of Phyllodes breast tumors from fibroadenomas using MRI[J]. Chin J Magn Reson Imag, 2014, 5(4): 246-252. DOI: 10.3969/j.issn.1674-8034.2014.04.05.
[28]
HOXHAJ A, MILANTS A, TECHANITHISAWAT P, et al. Exploring MRI and mammography lesion features for breast cancer detection in PTEN hamartoma tumor syndrome[J]. Cancers (Basel), 2025, 17(5): 856. DOI: 10.3390/cancers17050856.
[29]
GOU J, ZI L H, HE Q Y, et al. Comparison of the value of dynamic contrast-enhanced MRI and MSCT in the diagnosis of breast cancer [J]. Chin J CT MRI, 2021, 19 (6): 86-88. DOI: 10.3969/j.issn.1672-5131.2021.06.028.
[30]
XU M L, XIE D, LI F, et al. MRI features and correlation with pathologic findings in breast Phyllodes tumors[J]. J Clin Radiol, 2019, 38(7): 1203-1207. DOI: 10.13437/j.cnki.jcr.2019.07.005.
[31]
PARSAEI M, SANJARI MOGHADDAM H, MAZAHERI P. The clinical utility of diffusion-weighted imaging in diagnosing and predicting treatment response of laryngeal and hypopharyngeal carcinoma: a systematic review and meta-analysis[J/OL]. Eur J Radiol, 2024, 177: 111550 [2025-03-30]. https://pubmed.ncbi.nlm.nih.gov/38878501/. DOI: 10.1016/j.ejrad.2024.111550.
[32]
ZHANG B T, WANG F, SUN Y N, et al. The value of whole-volume histogram analysis of DWI based on fractional-order Calculus model in differential diagnosis of benign and malignant breast lesions[J]. Radiol Pract, 2025, 40(3): 331-338. DOI: 10.13609/j.cnki.1000-0313.2025.03.008.
[33]
WANG Y W, SUN M, LIU F H, et al. Value of intratumoral and peritumoral radiomics based on DCE-MRI and DWI in predicting HER-2 status in breast cancer[J]. Chin J Magn Reson Imag, 2024, 15(12): 116-123. DOI: 10.12015/issn.1674-8034.2024.12.017.
[34]
CHEN Z W, ZHAO Y F, LIU H R, et al. Assessment of breast lesions by the kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling[J]. European Radiology, 2022, 32(10): 6608-6618. DOI: 10.1007/s00330-022-08899-w.
[35]
KAWASHIMA H, MIYATI T, OHNO N, et al. Differentiation between phyllodes tumours and fibroadenomas using intravoxel incoherent motion magnetic resonance imaging: comparison with conventional diffusion-weighted imaging[J/OL]. Brit J Radiol, 2018, 91(1084): 20170687 [2025-03-30]. https://pubmed.ncbi.nlm.nih.gov/29231040/. DOI: 10.1259/bjr.20170687.
[36]
WANG C, WANG G, ZHANG Y, et al. Differentiation of benign and malignant breast lesions using diffusion-weighted imaging with a fractional-order calculus model[J/OL]. Eur J Radiol, 2023, 159: 110646 [2025-03-30]. https://pubmed.ncbi.nlm.nih.gov/36577184/. DOI: 10.1016/j.ejrad.2022.110646.
[37]
YANG Y W, HU C H, ZHU M, et al. The value of MRI apparent diffusion coefficient combined with dynamic enhanced TIC type in the differential diagnosis of mass plasma cell mastitis and breast cancer [J]. Chin J Magn Reson Imaging, 2019, 10(7): 530-534. DOI: 10.12015/issn.1674-8034.2019.07.010.

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