Prediction Modeling for Breast Cancer Treatment Planning Using Contrast-Enhanced Mammography

Abstract The goal of this study was to develop a deep learning model to support clinicians in the diagnosis and treatment planning of breast cancer. Specifically, we aim to leverage Grad-CAM (Gradient-weighted Class Activation Mapping)-based classification techniques for the weakly supervised localization of breast lesions in contrast-enhanced mammography (CEM) images. Furthermore, by integrating radiomics features and histopathological data, the model seeks to predict treatment outcomes more accurately. This approach helps in clinical decision-making, improve personalized care, and ultimately contribute to better patient survival rates. The proposed model extracts radiomics features from 2,289 CEM images, sourced from both a public data set CDD (categorized digital database)-CEM and a private data set, employing pretrained convolutional neural network (CNN) architectures such as Xception, Inception V3, DenseNet 201, ResNet 152, and VGG16. The model's performance was evaluated using three key metrics—accuracy, precision, and area under the curve (AUC)—for the classification task, and HD (Hausdorff distance) and Dice coefficient for the segmentation task. Experimental results demonstrate that the support vector machine model is particularly effective for breast cancer diagnosis, achieving an overall accuracy of 83%, precision of 80%, and an AUC of 0.83, based on a threefold cross-validation method applied specifically to the prediction modeling phase. Five different models were trained and tested on features selected differently from Least Absolute Shrinkage and Selection Operator (LASSO) and Minimum Redundancy Maximum Relevance; LASSO selected features gave better result with every model used. These findings suggest that integrating advanced CNN architectures with radiomics features can significantly enhance the accuracy and reliability of both lesion segmentation and treatment prediction in breast cancer, potentially leading to better clinical outcomes.