Predictive value of intratumor and peritumoral edema radiomics combined with autoencoder algorithm for HER-2 status in breast cancer
10.19745/j.1003-8868.2025155
- VernacularTitle:瘤内及瘤周水肿影像组学参数联合自编码器算法对乳腺癌HER-2状态的预测价值研究
- Author:
Zhao-lei LU
1
;
Yuan XU
;
Chao MA
;
Ya-wei LIU
;
Wang CHEN
;
Guan SUN
Author Information
1. 南京大学医学院附属盐城第一医院(盐城市第一人民医院)影像科,江苏 盐城 224000
- Publication Type:Journal Article
- Keywords:
breast cancer;
human epidermal growth factor receptor-2;
radiomics;
autoencoder algorithm;
intratumor edema;
peritumoral edema;
magnetic resonance imaging;
T2WI-FS sequence
- From:
Chinese Medical Equipment Journal
2025;46(9):9-15
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the predictive value of intratumor and peritumoral edema radiomics combined with the autoencoder algorithm for human epidermal growth factor receptor(HER-2)status in breast cancer to provide a new idea for preoperative noninvasive prediction of HER-2 status.Methods Totally 145 breast cancer patients from Yancheng Hospital Affiliated to Nanjing University Medical College(Center 1)and 52 ones from Jianhu Hospital Affiliated to Nantong University(Center 2)had their clinical and imaging data collected retrospectively,who were divided into a HER-2 positive group including 87 ones from Center 1 and 30 ones from Center 2 and a HER-2 negative group including 58 ones from Center 1 and 22 ones from Center 2.From December 2018 to October 2024 there were 78 patients with peritumoral edema from Center 1 randomly enrolled into a training set(55 patients)and a validation set(23 patients)in a ratio of 7∶3,and from November 2024 to March 2025 another 26 ones placed into a time validation set.The 52 patients with peritumoral edema from Center 2 were included into an external test set.Firstly,the Mazda software was used to delineate the regions of interest for the largest tumor layer and the peritumoral edema area.Secondly,multivariate analysis of variance(ANOVA),Kruskal-Wallis test,recursive feature elimination(RFE)and Relief algorithm were respectively employed to screen the radiomics features;finally,combined with ten-fold cross validation,the receiver operating characteristic curve was drawn,and the diagnostic efficacy of the models respectively constructed with radiomics parameters and ten types of machine learning algorithms,including auto encoder,support vector machine,linear discriminant analysis,random forest,Logistic regression,Logistic regression via Lasso,adaptive boosting,Gaussian process,native Bayes and decision tree,was evaluated for the HER-2 status in breast cancer.Results The model established by the auto encoder algorithm combined with three feature parameters including intratumor MaxNorm and Variance and peritumoral edema SumAverg behaved the best.The average AUC values of the training and validation sets were 0.808 and 0.735 resepctively,and the AUC values of the time validation and external test sets were 0.746 and 0.732 respectively.Conclusion The model developed with intratumor and peritumoral edema radiomics combined with the auto encoder algorithm can be used for preoperative noninvasive prediction of HER-2 status of breast cancer,which provides references for the preparation of individualized treatment scheme of breast cancer patients.[Chinese Medical Equipment Journal,2025,46(9):9-15]
