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• Clinical Article •
Interpretable machine learning model for predicting preoperative histological grade of invasive breast cancer based on high resolution delay period of magnetic resonance imaging
KUANG Jing  HUANG Songtao  HUANG Xiaohua  HU Yuntao 

Cite this article as: KUANG J, HUANG S T, HUANG X H, et al. Interpretable machine learning model for predicting preoperative histological grade of invasive breast cancer based on high resolution delay period of magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2025, 16(5): 164-169, 216. DOI:10.12015/issn.1674-8034.2025.05.025.


[Abstract] Objective To explore the value of Shapley additive explanations (SHAP) interpretable machine learning models based on high-resolution enhanced delayed-phase magnetic resonance imaging in preoperatively predicting histologic grade of non-special type invasive breast cancer.Materials and Methods Retrospectively collected the clinical-pathological-imaging data of 154 patients with invasive breast carcinoma of no special type from January 2019 to December 2023. Based on pathological biopsy results, Grade Ⅰ and Ⅱ were classified into the low-grade group, while Grade Ⅲ was classified into the high-grade group. They were randomly divided into a training group of 107 cases and a validation group of 47 cases in a 7∶3 ratio. 3D Slicer was used to delineate the lesion edges and extract radiomics features. Features were screened through multifactorial analysis. Radiomics feature models were established using Random forest (RF) and logistic regression, while clinical models, radiology models, and a combined radiology-clinical-radiomics feature model were developed using logistic regression. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy, while model comparison was conducted using DeLong test. SHAP analysis was used to visualize the contribution and importance of features in the model.Results There were significant differences in progesterone receptor (PR), tumor boundary, Ki-67 and estrogen receptor (ER) between low-grade group and high-grade group (P < 0.05). The AUC of the combined model based on radiology-clinical-radiomics features for preoperative prediction of the histological grade of invasive breast cancer was relatively good, with AUC values of 0.807 (95% CI: 0.723 to 0.891) in the training group and 0.890 (95% CI: 0.795 to 0.984) in the validation group. Among the two independent radiomics feature models, the logistic radiomics model showed no obvious overfitting, with AUC values of 0.750 (95% CI: 0.655 to 0.846) in the training group and 0.801 (95% CI: 0.667 to 0.936) in the validation group. The AUC of the clinical model and the radiology model in the training group were 0.661 (95% CI: 0.551 to 0.771) and 0.600 (95% CI: 0.493 to 0.706), respectively, and in the validation group were 0.789 (95% CI: 0.645 to 0.933) and 0.708 (95% CI: 0.565 to 0.850), respectively.Conclusions The joint model showed good efficacy in preoperatively predicting histologic grade of non-special type invasive breast cancer, providing guidance for preoperative treatment of breast cancer patients in clinical practice.
[Keywords] magnetic resonance imaging；explainable machine learning；high-resolution delayed-phase imaging；invasive breast cancer；grading

KUANG Jing1   HUANG Songtao1   HUANG Xiaohua2*   HU Yuntao3  

1 Department of Radiology, Sichuan University Huaxi Guang'an Hospital Guang'an City People's Hospital, Guangan 638500, China

2 Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China

3 Department of Radiology, Sichuan Cancer Hospital, Chengdu 610000, China

Corresponding author: HUANG X H, E-mail: 15082797553@163.com

Conflicts of interest   None.

Received  2024-11-11
Accepted  2025-05-09
DOI: 10.12015/issn.1674-8034.2025.05.025
Cite this article as: KUANG J, HUANG S T, HUANG X H, et al. Interpretable machine learning model for predicting preoperative histological grade of invasive breast cancer based on high resolution delay period of magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2025, 16(5): 164-169, 216. DOI:10.12015/issn.1674-8034.2025.05.025.

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