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MRI-based radiomics for prediction of tumor regression pattern to neoadjuvant chemotherapy in breast cancer
LIU Chen  CHEN Xiaobo  HUANG Xiaomei  CHEN Minglei  CHEN Xin  WANG Ying  LIU Zaiyi 

Cite this article as: LIU C, CHEN X B, HUANG X M, et al. MRI-based radiomics for prediction of tumor regression pattern to neoadjuvant chemotherapy in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 28-35. DOI:10.12015/issn.1674-8034.2023.03.006.


[Abstract] Objectives To develop a model by combining pretreatment MRI-based quantitative radiomics and qualitative image features and clinicopathologic information for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer.Materials and Methods Clinical data of 420 patients with breast cancer who received neoadjuvant chemotherapy and surgery from Guangdong Provincial People's Hospital from February 2012 to August 2020 were retrospectively analyzed. Pathologic findings of surgical specimens were used as the gold standard to classify the tumor regression patterns into concentric and non-concentric shrinkage. The training cohort (n=294) and the validation cohort (n=126) were divided into 7∶3 according to the chronological order of MRI examinations. In the 2nd phase images of dynamic contrast-enhanced MRI, the regions of interest (ROI) were delineated and the radiomics features of the ROI were extracted. Two independent-samples t test or Mann-Whitney U test, correlation analysis, least absolute shrinkage and selection operator (LASSO)-logistic regression were used for dimension reduction of radiomics features and artificial neural networks were used to establish a radiomics signature. Clinical prediction models were constructed by screening the significant clinicopathological features by univariate and multifactorial logistic regression. In addition, a predictive model combining qualitative image features, clinicopathologic features and radiomics signatures was constructed. The performance of the model was assessed using the receiver operating characteristic (ROC) curves and calibration curves. The decision curve analysis (DCA) was conducted to assess the clinical use of these predictive models.Results Eight radiomics signatures significantly correlated with tumor regression patterns were selected. In the training cohort and validation cohort, the radiomics signature yielded an area under curve (AUC) value of 0.738 (95% CI: 0.705-0.754) and 0.696 (95% CI: 0.585-0.712), respectively; the clinical predictive model yielded an AUC value of 0.676 (95% CI: 0.636-0.741) and 0.619 (95% CI: 0.601-0.716), respectively; the combined predictive model yielded an AUC value of 0.802 (95% CI: 0.753-0.824) and 0.764 (95% CI: 0.685-0.820), respectively. DCA showed the clinical use of the combined predictive models.Conclusions Prediction models combining pretreatment MRI-based quantitative radiomics and qualitative MRI image features and clinicopathologic information are useful for predicting tumor regression pattern in breast cancer, which can assist in selecting patients who can benefit from NAC for de-escalation of breast surgery, in order to optimize the individualized diagnosis as well as treatment plan, and improve the prognosis of patients.
[Keywords] reast neoplasms；tumor regression pattern；radiomics；neoadjuvant therapy；breast conserving surgery；magnetic resonance imaging

LIU Chen1, 2, 3   CHEN Xiaobo2, 3   HUANG Xiaomei4   CHEN Minglei2, 3   CHEN Xin5   WANG Ying6   LIU Zaiyi1, 2, 3*  

1 The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China

2 Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China

3 Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China

4 Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

5 Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China

6 Department of Ultrasound, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China

Corresponding author: Liu ZY, E-mail: liuzaiyi@gdph.org.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82272088); Key-Area Research and Development Program of Guangdong Province (No. 2021B0101420006); Science and Technology Projects in Guangzhou (No. 202201020001, 202201010513).
Received  2022-11-27
Accepted  2023-02-28
DOI: 10.12015/issn.1674-8034.2023.03.006
Cite this article as: LIU C, CHEN X B, HUANG X M, et al. MRI-based radiomics for prediction of tumor regression pattern to neoadjuvant chemotherapy in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 28-35. DOI:10.12015/issn.1674-8034.2023.03.006.

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