Share：
Share this content in WeChat
X
[Chinese][PDF]391
• Clinical Article •
Value of dynamic contrast-enhanced magnetic resonance imaging combined with intratumoral peritumoral radiomics in predicting benign and malignant non-mass enhanced breast lesions
YANG Ting  LIU Xuewen  LIU Yao  BAI Furong  YAO Juan 

Cite this article as: YANG T, LIU X W, LIU Y, et al. Value of dynamic contrast-enhanced magnetic resonance imaging combined with intratumoral peritumoral radiomics in predicting benign and malignant non-mass enhanced breast lesions[J]. Chin J Magn Reson Imaging, 2025, 16(5): 157-163, 203. DOI:10.12015/issn.1674-8034.2025.05.024.


[Abstract] Objective To explore the differences in diagnostic performance of varying peritumoral region (PTR) extents for breast non-mass enhancement (NME) lesions by utilizing the advantages of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with intratumoral region (ITR) and PTR radiomics.Materials and Methods Data of 168 patients from September 2021 to September 2024 were included in this retrospective study. They were randomly divided into training set (n = 117) and validation set (n=51) according to 7∶3. Based on DCE-MRI images, ITK-SNAP software was used to manually outline the ITR of the lesion and automatically expand the PTR. The least absolute shrinkage and selection operator (LASSO) was used to extract radiomics features in the tumor and in the extended area of 3 mm, 4 mm and 5 mm around the tumor. LASSO was used to select features and construct imaging models for ITR, PTR 3 mm, PTR 4 mm and PTR 5 mm. The optimal PTR model and ITR model were combined to form the optimal radiomics model. Clinical characteristics were added and a clinical model was developed using multivariate logistic regression analysis. Finally, the clinical model, the optimal radiomics model, and the combined model incorporating clinical features and optimal radiomics features were evaluated. The calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the model, and Shapley additive explanations (SHAP) diagram was used to explain the performance of the model.Results The ITR-PTR 4 mm radiomics model was found to have the best area under the curve (AUC) (training set: 0.822, validation set: 0.782) for constructing the combined model. In the clinical model, only the type of time signal intensity curve (TIC) was found to be significantly positively correlated with benign and malignant lesions by multivariate analysis (r = 0.681, P < 0.001). The AUC of the final combined model in the training set reached 0.912. In the validation set, the AUC was 0.806. DCA curve showed that the combined model had the highest clinical efficacy and was close to the diagonal in the calibration curve, so the fitting effect and generalization ability of the combined model were better.Conclusions The study found that the combined model combining radiomics features and clinical features can effectively distinguish benign and malignant NME lesions of undetermined nature in breast MRI, which provides a new reference for clinical diagnosis.
[Keywords] breast cancer；non-mass enhancement lesions；radiomics；peritumoral region；magnetic resonance imaging

YANG Ting   LIU Xuewen   LIU Yao   BAI Furong   YAO Juan*  

Department of Radiology, the First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China

Corresponding author: YAO J, E-mail: yaoj324@163.com

Conflicts of interest   None.

Received  2025-03-18
Accepted  2025-05-09
DOI: 10.12015/issn.1674-8034.2025.05.024
Cite this article as: YANG T, LIU X W, LIU Y, et al. Value of dynamic contrast-enhanced magnetic resonance imaging combined with intratumoral peritumoral radiomics in predicting benign and malignant non-mass enhanced breast lesions[J]. Chin J Magn Reson Imaging, 2025, 16(5): 157-163, 203. DOI:10.12015/issn.1674-8034.2025.05.024.

[1]
KIM J, HARPER A, MCCORMACK V, et al. Global patterns and trends in breast cancer incidence and mortality across 185 countries[J]. Nat Med, 2025, 31(4): 1154-1162. DOI: 10.1038/s41591-025-03502-3.
[2]
CHO S M, CHA J H, KIM H H, et al. Prevalence and outcomes of nonmass lesions detected on screening breast ultrasound based on ultrasound features[J/OL]. J Ultrasound, 2024 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39722092/. DOI: 10.1007/s40477-024-00981-x.
[3]
CHOI J S, TSUNODA H, MOON W K. Nonmass lesions on breast US: an international perspective on clinical use and outcomes[J]. J Breast Imaging, 2024, 6(1): 86-98. DOI: 10.1093/jbi/wbad077.
[4]
TANG J H, TANG W X, SHENG M H. Research progress of breast MRI in distinguishing benign and malignant non-mass enhancement lesions[J]. Chin J Radiol, 2022, 56(3): 335-340. DOI: 10.3760/cma.j.cn112149-20210319-00249.
[5]
AHMADINEJAD N, AZIZINIK F, KHOSRAVI P, et al. Evaluation of features in probably benign and malignant nonmass enhancement in breast MRI[J/OL]. Int J Breast Cancer, 2024, 2024: 6661849 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/38523651/. DOI: 10.1155/2024/6661849.
[6]
WANG X, LI M, WANG Z F, et al. Morphological features of mass-like non-special type of invasive breast cancer based on dynamic contrast-enhanced MRI radiomics for identifying ductal carcinoma in situ component[J]. Chin J Med Imag Technol, 2024, 40(12): 1856-1860. DOI: 10.13929/j.issn.1003-3289.2024.12.011.
[7]
LIU Y, LI X, ZHU L N, et al. Preoperative prediction of axillary lymph node metastasis in breast cancer based on intratumoral and peritumoral DCE-MRI radiomics nomogram[J/OL]. Contrast Media Mol Imaging, 2022, 2022: 6729473 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/36051932/. DOI: 10.1155/2022/6729473.
[8]
XIE T W, GONG J, ZHAO Q F, et al. Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer[J/OL]. BMC Med Imaging, 2024, 24(1): 136 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/38844842/. DOI: 10.1186/s12880-024-01311-7.
[9]
YANG H, WANG W X, CHENG Z Y, et al. Radiomic machine learning in invasive ductal breast cancer: prediction of ki-67 expression level based on radiomics of DCE-MRI[J/OL]. Technol Cancer Res Treat, 2024, 23: 15330338241288751 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39431304/. DOI: 10.1177/15330338241288751.
[10]
LI X G, TIAN J, ZHANG C L, et al. Overview of MRI-based radiomics in breast cancer diagnosis and treatment[J]. Chin J Magn Reson Imag, 2024, 15(7): 196-203. DOI: 10.12015/issn.1674-8034.2024.07.033.
[11]
QI Y J, SU G H, YOU C, et al. Radiomics in breast cancer: Current advances and future directions[J/OL]. Cell Rep Med, 2024, 5(9): 101719 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39293402/. DOI: 10.1016/j.xcrm.2024.101719.
[12]
KUNITAKE J A M R, SUDILOVSKY D, JOHNSON L M, et al. Biomineralogical signatures of breast microcalcifications[J/OL]. Sci Adv, 2023, 9(8): eade3152 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/36812311/. DOI: 10.1126/sciadv.ade3152.
[13]
IONESCU C A, ASCHIE M, MATEI E, et al. Characterization of the tumor microenvironment and the biological processes with a role in prostatic tumorigenesis[J/OL]. Biomedicines, 2022, 10(7): 1672 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/35884977/. DOI: 10.3390/biomedicines10071672.
[14]
CIERNIKOVA S, SEVCIKOVA A, STEVURKOVA V, et al. Tumor microbiome-an integral part of the tumor microenvironment[J/OL]. Front Oncol, 2022, 12: 1063100 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/36505811/. DOI: 10.3389/fonc.2022.1063100.
[15]
ZHANG S H, WANG X L, YANG Z, et al. Intra- and peritumoral radiomics model based on early DCE-MRI for preoperative prediction of molecular subtypes in invasive ductal breast carcinoma: a multitask machine learning study[J/OL]. Front Oncol, 2022, 12: 905551 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/35814460/. DOI: 10.3389/fonc.2022.905551.
[16]
QU Y H, HE Y J, ZHU H T, et al. Preoperative MRI radiomics model for predicting the feasibility of treating breast cancer with breast conserving surgery[J]. Chin J Med Imag Technol, 2023, 39(6): 848-852. DOI: 10.13929/j.issn.1003-3289.2023.06.011.
[17]
BAE J, HUANG Z N, KNOLL F, et al. Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach[J]. Magn Reson Med, 2022, 87(5): 2536-2550. DOI: 10.1002/mrm.29148.
[18]
LIU Z, YAO B Y, WEN J, et al. Voxel-wise mapping of DCE-MRI time-intensity-curve profiles enables visualizing and quantifying hemodynamic heterogeneity in breast lesions[J]. Eur Radiol, 2024, 34(1): 182-192. DOI: 10.1007/s00330-023-10102-7.
[19]
TIAN R H, LU G X, ZHAO N N, et al. Constructing the optimal classification model for benign and malignant breast tumors based on multifeature analysis from multimodal images[J]. J Imaging Inform Med, 2024, 37(4): 1386-1400. DOI: 10.1007/s10278-024-01036-7.
[20]
BIAN T T, WU Z J, LIN Q, et al. Evaluating tumor-infiltrating lymphocytes in breast cancer using preoperative MRI-based radiomics[J]. J Magn Reson Imaging, 2022, 55(3): 772-784. DOI: 10.1002/jmri.27910.
[21]
CAI L, SIDEY-GIBBONS C, NEES J, et al. Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?[J]. Eur Radiol, 2024, 34(4): 2560-2573. DOI: 10.1007/s00330-023-10238-6.
[22]
BRAMAN N M, ETESAMI M, PRASANNA P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J/OL]. Breast Cancer Res, 2017, 19(1): 57 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/28521821/. DOI: 10.1186/s13058-017-0846-1.
[23]
ZHANG W Y, WANG S Y, WANG Y C, et al. Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer[J]. Radiol Med, 2024, 129(2): 211-221. DOI: 10.1007/s11547-024-01768-0.
[24]
DING J, CHEN S L, SERRANO SOSA M, et al. Optimizing the peritumoral region size in radiomics analysis for sentinel lymph node status prediction in breast cancer[J/OL]. Acad Radiol, 2022, 29(Suppl 1): S223-S228 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/33160860/. DOI: 10.1016/j.acra.2020.10.015.
[25]
IKUSHIMA H, WATANABE K, SHINOZAKI-USHIKU A, et al. A machine learning-based analysis of nationwide cancer comprehensive genomic profiling data across cancer types to identify features associated with recommendation of genome-matched therapy[J/OL]. ESMO Open, 2024, 9(12): 103998 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39591805/. DOI: 10.1016/j.esmoop.2024.103998.
[26]
LIU J F, GUO W, ZENG P E, et al. Vertebral MRI-based radiomics model to differentiate multiple myeloma from metastases: influence of features number on logistic regression model performance[J]. Eur Radiol, 2022, 32(1): 572-581. DOI: 10.1007/s00330-021-08150-y.
[27]
YAN M Y, HE D L, SUN Y, et al. Comparative analysis of nomogram and machine learning models for predicting axillary lymph node metastasis in early-stage breast cancer: a study on clinically and ultrasound-negative axillary cases across two centers[J]. Ultrasound Med Biol, 2025, 51(3): 463-474. DOI: 10.1016/j.ultrasmedbio.2024.11.003.
[28]
MA W M, LI J, HE N, et al. The application value of a nomogram based on breast MRI and axillary ultrasonography for predicting sentinel lymph node metastasis of early-stage breast cancer[J]. Chin J Radiol, 2020, 54(7): 694-701. DOI: 10.3760/cma.j.cn112149-20200420-00576.
[29]
GUO F, SUN S, DENG X, et al. Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model[J/OL]. Front Immunol, 2024, 15: 1482020 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39735531/. DOI: 10.3389/fimmu.2024.1482020.
[30]
HAN X R, GONG Z Z, GUO Y, et al. Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction[J/OL]. Eur J Radiol, 2024, 175: 111441 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/38537607/. DOI: 10.1016/j.ejrad.2024.111441.
[31]
JIN Y, WANG W, LI J B. Multi-omics prediction of lymph node metastasis status in breast cancer[J]. Chin J Oncol, 2024, 46(5): 391-398. DOI: 10.3760/cma.j.cn112152-20230822-00093.
[32]
ZHENG G Y, PENG J X, SHU Z Y, et al. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms[J/OL]. J Cancer Res Clin Oncol, 2024, 150(3): 147 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/38512406/. DOI: 10.1007/s00432-024-05680-y.
[33]
ZHAO W J, HOU M Y, WANG J, et al. Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features[J/OL]. BMC Med Imaging, 2024, 24(1): 353 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39736623/. DOI: 10.1186/s12880-024-01548-2.
[34]
YAN R, MURAKAMI W, MORTAZAVI S, et al. Quantitative assessment of background parenchymal enhancement is associated with lifetime breast cancer risk in screening MRI[J]. Eur Radiol, 2024, 34(10): 6358-6368. DOI: 10.1007/s00330-024-10758-9.
[35]
XU H, LIU J K, CHEN Z, et al. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer[J]. Eur Radiol, 2022, 32(7): 4845-4856. DOI: 10.1007/s00330-022-08539-3.
[36]
NGUYEN V T, DUONG D H, NGUYEN Q T, et al. The association of magnetic resonance imaging features with five molecular subtypes of breast cancer[J/OL]. Eur J Radiol Open, 2024, 13: 100585 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39041054/. DOI: 10.1016/j.ejro.2024.100585.
[37]
LIU W, LI L, DENG J, et al. A comprehensive approach for evaluating lymphovascular invasion in invasive breast cancer: Leveraging multimodal MRI findings, radiomics, and deep learning analysis of intra- and peritumoral regions[J/OL]. Comput Med Imaging Graph, 2024, 116: 102415 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39032451/. DOI: 10.1016/j.compmedimag.2024.102415.
[38]
LU Y, JIN L, DING N, et al. The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer[J/OL]. BMC Med Imaging, 2025, 25(1): 11 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39773380/. DOI: 10.1186/s12880-025-01553-z.
[39]
HU Y L, CAI Z H, AIERKEN N, et al. Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: a multicentre study[J/OL]. Radiat Oncol, 2025, 20(1): 27 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/40022114/. DOI: 10.1186/s13014-025-02605-y.
[40]
KAYADIBI Y, SARACOGLU M S, KURT S A, et al. Differentiation of malignancy and idiopathic granulomatous mastitis presenting as non-mass lesions on MRI: radiological, clinical, radiomics, and clinical-radiomics models[J]. Acad Radiol, 2024, 31(9): 3511-3523. DOI: 10.1016/j.acra.2024.03.025.
[41]
MAGNUSKA Z A, ROY R, PALMOWSKI M, et al. Combining radiomics and autoencoders to distinguish benign and malignant breast tumors on US images[J/OL]. Radiology, 2024, 312(3): e232554 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39254446/. DOI: 10.1148/radiol.232554.
[42]
YU T, YU R Q, LIU M Q, et al. Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: a multicenter study comparing performance with radiologists[J/OL]. Eur J Radiol, 2024, 177: 111556 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/38875748/. DOI: 10.1016/j.ejrad.2024.111556.
[43]
YANG L, ZHANG N W, JIA J Y, et al. Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions[J/OL]. Sci Rep, 2024, 14(1): 31479 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/39733121/. DOI: 10.1038/s41598-024-83347-x.
[44]
WAN W J, ZHU K, RAN Z C, et al. Development of a nomogram-integrated model incorporating intra-tumoral and peri-tumoral ultrasound radiomics alongside clinical parameters for the prediction of histological grading in invasive breast cancer[J]. Ultrasound Med Biol, 2025, 51(2): 262-272. DOI: 10.1016/j.ultrasmedbio.2024.09.025.
[45]
NIJMAN S, LEEUWENBERG A M, BEEKERS I, et al. Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review[J/OL]. J Clin Epidemiol, 2022, 142: 218-229 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/34798287/. DOI: 10.1016/j.jclinepi.2021.11.023.
[46]
MURAD M, TOUIR A, BEN ISMAIL M M. MRI-based meningioma firmness classification using an adversarial feature learning approach[J/OL]. Sensors (Basel), 2025, 25(5): 1397 [2025-03-17]. https://pubmed.ncbi.nlm.nih.gov/40096246/. DOI: 10.3390/s25051397.
[47]
YE J Y, CHEN Y T, PAN J W, et al. US-based radiomics analysis of different machine learning models for differentiating benign and malignant BI-RADS 4A breast lesions[J]. Acad Radiol, 2025, 32(1): 67-78. DOI: 10.1016/j.acra.2024.08.024.

PREV Evaluation of the influence of left ventricular myocardial fibrosis on biventricular function in patients with coronary artery disease based on cardiac magnetic resonance feature tracking
NEXT Interpretable machine learning model for predicting preoperative histological grade of invasive breast cancer based on high resolution delay period of magnetic resonance imaging
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn