Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer

Abstract

Abstract Background Current methods utilizing preoperative magnetic resonance imaging (MRI)‐based radiomics for assessing lymphovascular invasion (LVI) in patients with early‐stage breast cancer lack precision, limiting the options for surgical planning. Purpose This study aimed to develop a sophisticated deep learning framework called “Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolutional Neural Network (PCMM‐Net)” to improve the accuracy of LVI prediction in breast cancer. By incorporating multiparameter MRI and prior clinical knowledge, PCMM‐Net should enhance the precision of LVI assessment. Methods A total of 341 patients with breast cancer were randomly divided into training and validation groups at a ratio of 7:3. Imaging features were extracted from T1‐weighted, T2‐weighted, and contrast‐enhanced T1‐weighted MRI sequences. Stepwise univariate and multivariate logistic regression were employed to establish a clinico‐radiological model for LVI prediction. The radiomics model was built using redundancy and the least absolute shrinkage and selection operator. Then, two deep learning frameworks were developed: the Multi‐Modal MR Images Convolutional Neural Network (MM‐Net), which does not consider prior radiological features, and PCMM‐Net, which incorporates multiparameter MRI and prior clinical knowledge. Receiver operating characteristic curves were used, and the corresponding areas under the curves (AUCs) were calculated for evaluation. Results PCMM‐Net achieved the highest AUC of 0.843. The clinico‐radiological features displayed the lowest AUC value of 0.743, followed by MM‐Net with an AUC of 0.774, and radiomics with an AUC of 0.795. Conclusions This study introduces PCMM‐Net, an innovative deep learning framework that integrates prior clinico‐radiological features for accurate LVI prediction in breast cancer. PCMM‐Net demonstrates excellent diagnostic performance and facilitates the application of precision medicine.

Keywords

• breast cancer
• lymphovascular invasion
• Prior Clinico‐Radiological Features Informed Channel Attention Neural Network
• radiomics