Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning

Abstract

This research aimed to explore the effect of using magnetic resonance imaging (MRI) radiomic features to establish a model for predicting distant metastasis under dynamic contrast-enhanced MRI imaging with deep learning algorithms. The deep learning algorithm was used to segment the images. A total of 96 cases with 100 lesions were included in the metastatic group, including 2 cases of bifocal breast cancer and 2 cases of multifocal breast cancer. There were 192 cases in the nonmetastatic group, with 197 lesions, including 5 cases of multifocal breast cancer. After dynamic contrast-enhancement, the morphological features and grayscale statistical features were extracted from the lesions to establish a prediction model through sum-sum check and feature dimension reduction. The accuracy, sensitivity, specificity, and area under receiver operator characteristic curve (AUC) of prediction models based only on imaging features were compared with those created by combining radiomic features with clinical and pathological features. The created predictive model based on radiomic features for distant metastases in breast cancer showed a sensitivity of 66.7%, a specificity of 84.2%, an accuracy of 78.3%, and an AUC of 0.744. The sensitivity of the prediction model for distant metastasis of breast cancer was 67.7%, the specificity was 86.8%, the accuracy was 80.5%, and the AUC was 0.763. Bone, lung, and liver were the most common distant metastatic sites of breast cancer. Under the dynamic contrast-enhanced MRI of deep learning, the prediction model combining radiomic features with clinical and pathological features showed better predictive performance.

Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning

“…Li et al developed a deep learning algorithm that predicts bone metastasis in breast cancer by incorporating MRI radiological features from 96 cases of metastatic breast tumors and 192 cases of non-metastatic breast tumors. The predictive performance of the model is evaluated using statistical morphology and grayscale characteristics, employing metrics such as AUC, sensitivity, and specificity 30 . Nonetheless, due to the high demand for front-end MRI images and data, this model cannot be widely adopted.…”

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study

“…This AI-based approach holds promise for providing faster and more accurate diagnoses, while potentially reducing the need for expensive imaging studies, thereby alleviating the economic burden on patients. The current research primarily focuses on predicting the risk of breast cancer metastasis in the future (1 year, 3 years, or 5 years) (10,12,13,(15)(16)(17)(18), while there is relatively less emphasis on diagnostic predictions for distant metastasis of breast cancer (11,14,(19)(20)(21). In the study by Huang et al, the SEER database was used to predict bone metastasis in invasive ductal carcinoma; however, their study did not mention a validation set (11).…”

How has the field of metastatic breast cancer in bones evolved over the past 22 years?

“…Studies have reported that distant metastatic lesions in patients with advanced BC are mostly found in the bones, simultaneously the most common primary site of bone metastasis is also breast [11] , [12] , [13] . Once bone metastasis occurs, patients often present with bone or joint pain, pathological fractures, or neuropathic pain [14] , [15] .…”