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• Clinical Article •
Radiomics based on multiparametric MRI for prediction of breast cancers sensitive to neoadjuvant chemotherapy
ZHAO Qing  SU Tong  DAI Ting  WANG Rui  ZHANG Shuo  TAO Yang  LÜ Fajin  OUYANG Zubin 

Cite this article as: ZHAO Q, SU T, DAI T, et al. Radiomics based on multiparametric MRI for prediction of breast cancers sensitive to neoadjuvant chemotherapy[J]. Chin J Magn Reson Imaging, 2024, 15(6): 79-86. DOI:10.12015/issn.1674-8034.2024.06.012.


[Abstract] Objective To predict the sensitivity of breast cancer to neoadjuvant therapy (NAT) based on multiparametric magnetic resonance imaging (mpMRI) combined with clinical variables.Materials and Methods A total of 248 patients with pathologically confirmed breast cancer were enrolled in this study and randomly divided into a training group (173 cases) and a validation group (75 cases) in a 7∶3 ratio. All patients underwent mpMRI examination before NAT. The Miller-Payne (MP) grading system was used to assess the effectiveness of NAT, with MP grades 1-2 considered as insensitive to NAT response, and MP grades 3-5 as sensitive. Based on dynamic contrast-enhanced MRI (DCE-MRI), T2WI, and diffusion weighted imaging (DWI) sequence images to delineate tumor regions, to extract and filter imaging radiomics features. A radiomics score (Rad-score) was derived using the least absolute shrinkage and selection operator algorithm. Univariate logistic regression was ultilized to analyze clinical and pathological variables, including age, menstrual status, molecular subtype, chemotherapy regimen, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), and tumor proliferative index Ki-67. Significant clinical and pathological variables, along with the Rad-score, were included in the multivariate logistic regression analysis to establish an radiomics-clinical combined model and nomogram. The predictive performance of model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).Results Univariate logistic regression analysis showed that Rad-score (P<0.001), ER expression status (P=0.001), and chemotherapy regimen (P=0.031) were significantly associated with the sensitivity of NAT in breast cancer. The AUC of the radiomics-clinical combined model constructed by Rad-score with ER expression status and chemotherapy regimen was 0.845 (95% CI: 0.780-0.910) in the training cohort, and 0.820 (95% CI: 0.718-0.923) in the validation cohort. The nomogram in prediction of breast cancer susceptibility to NAT had a higher degree of differentiation (C index: training queue is 0.842, validation queue is 0.822), the calibration curve shows good consistency. The clinical decision curve showed that the nomogram had a high overall net benefit.Conclusions The integration of radiomics and clinical variables and nomogram show promise in predicting sensitivity of breast cancer to neoadjuvant therapy.
[Keywords] breast cancer；radiomics；multi-parametric magnetic resonance imaging；magnetic resonance imaging；neoadjuvant therapy；sensitivity

ZHAO Qing1   SU Tong1   DAI Ting2   WANG Rui3   ZHANG Shuo1   TAO Yang1   LÜ Fajin1   OUYANG Zubin1*  

1 Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

2 Department of Radiology, Jiangbei Traditional Chinese Medicine Hospital, Chongqing 400020, China

3 Department of Radiology, Hechuan District People's Hospital, Chongqing 401519, China

Corresponding author: OUYANG Z B, E-mail: ouyangzubin@aliyun.com

Conflicts of interest   None.

Received  2023-12-30
Accepted  2024-06-03
DOI: 10.12015/issn.1674-8034.2024.06.012
Cite this article as: ZHAO Q, SU T, DAI T, et al. Radiomics based on multiparametric MRI for prediction of breast cancers sensitive to neoadjuvant chemotherapy[J]. Chin J Magn Reson Imaging, 2024, 15(6): 79-86. DOI:10.12015/issn.1674-8034.2024.06.012.

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