Prediction model for pectoralis major myofascial metastasis in breast cancer based on imaging features and clinical data
10.3969/j.issn.1005-202X.2025.08.008
- VernacularTitle:基于影像学特征与临床数据的乳腺癌胸大肌筋膜转移预测模型
- Author:
Xuzhen WANG
1
;
Yiyi FAN
;
Min ZHOU
;
Can ZHAO
;
Liping JIANG
Author Information
1. 江南大学附属妇产医院乳腺科,江苏 无锡 214000
- Publication Type:Journal Article
- Keywords:
breast cancer;
pectoralis major myofascial metastasis;
physical characteristics;
dual-stream parallel network
- From:
Chinese Journal of Medical Physics
2025;42(8):1036-1041
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct an innovative CNN-Transformer dual-stream parallel network architecture integrating clinical data and imaging features for improving the predictive accuracy of pectoralis major myofascial metastasis in breast cancer,and to optimize the model performance by screening the optimal feature subset through genetic algorithm.Methods The proposed architecture concurrently processed clinical records and imaging data,including physical characteristics such as resolution,contrast,grayscale distribution,and texture features to identify their latent correlations.Meanwhile,genetic algorithms were employed to remove redundant features while retaining the most clinically and physically relevant features for pectoralis major myofascial metastasis prediction.Results The CNN-Transformer model that integrated imaging and clinical features showed superior performance across all evaluation metrics such as weighted F1 score and AUCROC,outperforming models relying only on imaging or clinical data.Conclusion The proposed dual-stream parallel network architecture combined with feature selection strategy significantly enhances the predictive accuracy of pectoralis major myofascial metastasis in breast cancer,and demonstrates the critical role of imaging features in improving model performance.
