Application of machine learning in predicting perineural invasion of invasive breast cancer based on MRI imaging features
10.3969/j.issn.1002-1671.2025.05.012
- VernacularTitle:机器学习基于MRI影像学特征预测浸润性乳腺癌神经周围侵犯的应用研究
- Author:
Jiayu YIN
1
;
Yixin LU
;
Xianting LUO
;
Liangsen LIU
;
Danke SU
Author Information
1. 广西医科大学附属肿瘤医院医学影像中心,广西 南宁 530012;南宁市第一人民医院放射科,广西 南宁 530022
- Publication Type:Journal Article
- Keywords:
breast cancer;
perineural invasion;
magnetic resonance imaging;
radiomics;
machine learning
- From:
Journal of Practical Radiology
2025;41(5):771-774
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the diagnostic efficacy of machine learning in predicting perineural invasion(PNI)of invasive breast cancer based on MRI imaging features of breast cancer.Methods The data of 294 patients with invasive breast cancer confirmed by surgical pathology were retrospectively analyzed,and the patients were randomly divided into training set(205 cases,PNI 77 cases)and validation set(89 cases,PNI 33 cases)at a ratio of 7∶3.10 machine learning models were constructed by selecting training set clinical and radiographic features using single factor logistic regression.The area under the curve(AUC),accuracy(ACC),sensitivity(SE),specificity(SP),positive predictive value(PPV),and negative predictive value(NPV)were used to evaluate the predictive effi-cacy of different models for PNI,and the best model was determined.SHapley Additive exPlanation(SHAP)was used to visuaize the diagnosis process of the model.Results In the validation set,the multi-layer perceptron(MLP)model performed best,with AUC,ACC,SE,SP,PPV,and NPV of 0.91,0.89,0.79,0.95,0.90,and 0.88,respectively.Conclusion The model of MRI imaging fea-tures of breast cancer constructed by MLP machine learning model can effectively predict the preoperative PNI of invasive breast cancer.
