Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Abstract

Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

“…Preoperative breast MRI, for its highest resolution and abundant information, becomes the most promising imaging modality for different AI applications, mainly for lesion detection and classification ( 12 , 29 ). Automatically detecting and classifying (limited to benign versus malignant) breast lesions on MRI are relatively well-established techniques ( 30 – 33 ).…”

Neuroimaging in the Era of Artificial Intelligence: Current Applications

“…33,[39][40][41][42][43][44][45] AI also can assist triage, patient screening, providing a "second opinion" rapidly, shortening the time needed for attaining a diagnosis, monitoring disease progression, and predicting prognosis. [37][38][39]43,[45][46][47] Downstream applications of AI in neuroradiology and neurology include using CT to aid in detecting hemorrhage or ischemic stroke; using MRI to automatically segment lesions, such as tumors or MS lesions; assisting in early diagnosis and predicting prognosis in MS; assisting in treating paralysis, including from spinal cord injury; determining seizure type and localizing area of seizure onset; and using cameras, wearable devices, and smartphone applications to diagnose and assess treatment response in neurodegenerative disorders, such as Parkinson or Alzheimer diseases (Figure). 37,[48][49][50][51][52][53][54][55][56] Several AI tools have been deployed in the clinical setting, particularly triaging intracranial hemorrhage and moving these studies to the top of the radiologist's worklist.…”

Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results

“…Our results also show overall better performance of our ML model in the prediction of PD-L1 status compared with previous studies, probably due to the fact that radiomics features were extracted from MRI images rather than PET/CT or CT images in our study. Breast MRI is the most sensitive modality for breast cancer detection [ 52 ] and provides not only excellent morphologic information due to higher tissue contrast but also functional information related to vascularization with dynamic imaging; moreover, it is probably the imaging modality for which data for AI studies on breast cancer is most commonly available [ 39 ].…”

“…Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most sensitive and accurate test for breast cancer diagnosis, characterization, and response assessment. Artificial intelligence (AI)-enhanced DCE-MRI has shown potential to further improve molecular breast cancer subtyping, treatment planning, and treatment monitoring [ 39 ]. To date, the potential of radiomics analysis coupled with ML based on DCE-MRI for the prediction of PD-L1 expression status in triple negative breast cancer has not been explored.…”