Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction

Abstract

Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.

FIGURE 1

Schematic description of workflow of artificial intelligence–enhanced imaging biomarker development using machine learning and deep learning approaches. For deep learning, the image segmentation step is not necessarily required.

FIGURE 2

Receiver operation characteristic (ROC) curves of the multiparametric magnetic resonance imaging model using the XGBoost classifier with 4-fold cross-validations for predicting of (A) RCB class and (B) RFS, and 3-fold cross-validation in prediction of (C) DSS. The solid orange lines represent the average ROC curves, the lighter lines depict the ROC curve for each fold, and the gray-shaded areas indicate the confidence interval for the predictions using multiparametric magnetic resonance imaging model. Reprinted with permission from Tahmassebi et al.

FIGURE 3

Schematic of the proposed transfer learning method encompassing 5 steps: (1) feature extraction, (2) dynamic feature selection, (3) optimal feature selection, (4) classification on the fine-tuning data set, (5) classification on the independent test. Reprinted under a Creative Commons (CC BY 4.0) license from Comes et al.

FIGURE 4

Flowchart illustrating the radiomics workflow and the application of the RQS. The workflow encompasses the essential steps in radiomic analysis. The RQS both rewards and penalizes a study's methodology and analyses, thus promoting best scientific practice. RQS, radiomics quality score; VOI, volume of interest. Reprinted with permission from Lambin et al.