Preoperative prediction of lymphovascular invasion in breast cancer with digital breast tomosynthesis-based intratumoral and peritumoral radiomics
10.3969/j.issn.1002-1671.2025.01.011
- VernacularTitle:基于数字乳腺断层摄影的瘤内和瘤周影像组学对乳腺癌淋巴血管浸润状态的术前预测
- Author:
Suxin ZHANG
1
;
Haiyan LI
;
Yiqun ZHENG
;
Wenqing CHEN
;
Sheng HE
;
Caixian YANG
;
Gang LIANG
;
Jianding LI
;
Zengyu JIANG
Author Information
1. 山西医科大学医学影像学院,山西 太原 030001
- Publication Type:Journal Article
- Keywords:
breast cancer;
radiomics;
digital breast tomosynthesis;
lymphovascular invasion
- From:
Journal of Practical Radiology
2025;41(1):46-51
- CountryChina
- Language:Chinese
-
Abstract:
Objective To predict the lymphovascular invasion(LVI)status of breast cancer patients based on digital breast tomo-synthesis(DBT)intratumoral and peritumoral radiomics nomogram.Methods A total of 192 breast cancer patients from 2 institu-tions were retrospectively selected,in which institution 1 was used for train(n=113)and test(n=49),while institution 2 was used for external validation(n=30).Radiomics features were extracted and selected based on intratumoral and peritumoral 1 mm regions from DBT images.Different machine learning algorithms were used to construct intratumoral,peritumoral,and combined intratumoral and peritumoral models,respectively.Patient clinical data were analyzed by both univariate and multivariate logistic regression analy-ses to identify independent risk factors for the clinical imaging model.The performance of the models was evaluated using the receiver operating characteristic(ROC)curve.The radiomics features with the optimal diagnostic performance and the selected clinical imaging features were combined to construct a comprehensive clinical-radiomics model,and a nomogram was drawn.Results The combined intratumoral and peritumoral model was the optimal radiomics model.Maximum tumor diameter[odds ratio(OR)=1.486,P=0.014],suspicious malignant calcifications(OR=2.898,P=0.015),and axillary lymph node(ALN)metastasis(OR=3.615,P<0.001)were independent risk factors for LVI positive.Furthermore,the area under the curve(AUC)of the comprehensive clinical-radiomics model in the training set,test set and external valida-tion set was 0.889,0.916,and 0.862,respectively,which was higher than those of the combined intratumoral and peritumoral model(0.858,0.849,0.844)and the clinical imaging model(0.743,0.759,0.732).Conclusion The predictive nomogram,derived from both radiomics and clinical imaging features,is relatively accurate in identifying future LVI occurrence in breast cancer,demonstra-ting its potential as an assistive tool for clinicians to devise individualized treatment regimes.
