Artificial Intelligence—Enhanced Breast MRI and DWI: Current Status and Future Applications

Background

Personalized medicine is yielding increasingly precise treatment and prevention strategies for groups of individuals based on their genetic makeup, environment, and lifestyle, as enabled by approaches including genomics, transcriptomics, proteomics, metabolomics, and so forth. In oncology, the goal of using such approaches is to increasingly harness individual-level information versus population-level or traditional clinical information (e.g., tumor stage, age, gender) to select the most successful cancer treatment regimen for each patient.

Molecular tumor characterization can be performed using genomic and proteomic approaches, but this requires tissue sampling from invasive surgery or biopsy. Currently, large-scale genome cancer characterization that would include genetic testing for every individual is not feasible because of the high costs, considerable time burden, and technically complex data analysis and interpretation. Moreover, even when molecular characterization is performed using tissue sampling, samples may not accurately represent the entire lesion as they are often obtained from a small portion of a heterogeneous lesion with inherent selection bias during biopsy.

By contrast, imaging can provide a more comprehensive view of the tumor in its entirety via radiomics and radiogenomics. Radiomics is an approach pertaining to the extraction and correlation of multiple imaging parameters with different variables of interest (patient characteristics as well as histopathologic, genomic, molecular, or outcome data) to create decision support models. Such models can be used for multiple purposes, such as treatment planning, risk assessment, and outcome prediction. When imaging data is correlated with genetic data in particular, this approach is referred to as radiogenomics. Coupling with artificial intelligence (AI) techniques allows us to more fully harness the power of radiomics/genomics. Because of the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of AI-enhanced imaging is rapidly evolving.

With continuous advances in radiomics analysis and machine learning (ML), such as deep learning (DL), we are now on the cusp of providing more effective, more efficient, and even more patient-centric breast cancer care than ever before. In this chapter, we will begin with the basic concepts of radiomics, radiogenomics, and AI methodology in breast magnetic resonance imaging (MRI). The rest of the chapter will be devoted to reviewing AI-enhanced MRI and AI-enhanced diffusion-weighted imaging (DWI), whereby we will present the current knowledge and future applications of AI-enhanced MRI and DWI in clinical practice and address their challenges and limitations.

Basic Concepts of Radiomics and AI Methodology

Radiomics analysis can be divided into two arms based on how imaging information is transformed into mineable data: handcrafted and AI based ( Fig. 10.1 ). Handcrafted radiomics extracts features that are used to fingerprint phenotypical characteristics in images, whereas AI uses a complex network to create its own features.

Handcrafted radiomics methodology usually follows this workflow: image acquisition (2D, 3D, 4D); normalization to pixel intensities evenly across a data set and within a standardized range; image annotation and segmentation (manual, semiautomatic, or fully automatic; Fig. 10.2 ) and for definition of region of interest (ROI) and feature extraction; radiomics analysis (feature selection and reduction); and classification and modeling. Handcrafted radiomics analysis includes first-order features based on the distribution of pixel intensities (histogram based) and higher-order features based on how pixels are positioned in relation to each other (e.g., co-occurrence matrices, run length matrices, size zone matrices, neighborhood gray-level dependence matrices, Minkowski functionals, local binary patterns, wavelet analysis). Because a large quantity of imaging features are extracted, which are not necessarily all relevant to the task proposed to the model, feature selection and reduction is an essential step, followed by classification and modeling to answer the specific question we are proposing. Handcrafted radiomics studies typically use AI methods (e.g., decision trees, support vector machines, random forests, neural networks) to select features and construct models. Ideally, the model’s performance should be validated in external data sets to avoid overfitting, which refers to spurious correlations in the data that do not allow generalization to other similar data sets. If no external validation data set is available, cross-validation techniques can be applied to split the data into different subsets (training and validation sets). To be able to expand the interoperability of models to all hardware, acquisition, and reconstruction parameters in general clinical practice, rigorous standardization is necessary but is hard to achieve. Standardized data collection, evaluation criteria, and reporting guidelines will be required for radiomics to mature as a discipline. To gauge the quality of published radiomics studies, a radiomics quality score has been proposed ( Fig. 10.3 ), and to address the issue of radiomic feature reproducibility, some harmonization methods such as Combine Batches (ComBat) have been investigated in the literature.

DL is a new class of ML that uses neural networks with multiple layers of processing inspired by human brain architecture. In contrast to traditional radiomics-based ML approaches, where humans engage in handcrafted feature extraction, DL networks learn both the feature extraction and classification steps in tandem and are able to extract very high-level features from imaging data. These recent advances in software and also hardware (to support higher computational power requirements) have given DL models the potential to surpass human performance in some image analyses tasks.

To date, most DL applications in medical imaging use convolutional neural networks (CNNs), which are particularly well suited to visual tasks. CNNs can be used for both image classification and image segmentation. In supervised learning approaches, which constitute almost all the DL in medical imaging literature to date, it is necessary to supply the CNN with large numbers of labeled imaging data. To develop a CNN model, imaging data sets must be divided into three independent groups: training, validation, and testing data sets. First the DL model is trained on the training set images and learns to predict the label. This process is repeated many times with different model hyperparameters, with intermittent evaluation of performance using the validation data set to prevent overfitting. Then, once the DL model parameters and hyperparameters have been finalized, the held-out test set is used to evaluate final CNN performance and results are reported with a standard set of relevant statistical metrics (e.g., area under the curve [AUC], precision recall, sensitivity, specificity). DL studies must pass through rigorous validation steps, which includes definition of the image sets (training, validation, and test sets) and ground truth reference standard; detailed description of the model, training approach, and metrics of model performance; and validation or testing on external data.

Further reviews of the process of radiomics/genomics analysis coupled with AI (image acquisition, volume of interest selection, segmentation, feature extraction and quantification, database building, classifier modeling, and data sharing) are described in detail elsewhere.

AI-Enhanced Breast MRI

Breast MRI is the most sensitive modality for breast cancer detection, with a pooled sensitivity of 93% and pooled specificity of 71%. Dynamic contrast-enhanced MRI (DCE MRI) is the primary sequence of the breast MRI examination, which relies on intravenous injection of a gadolinium-based contrast agent and provides excellent morphological information and functional information about abnormal vascularization as a tumor-specific feature. DCE MRI is regarded as the most sensitive imaging technique for breast cancer detection. However, it has been criticized for its variable specificity.

To overcome limitations in DCE MRI specificity and to obtain more valuable functional data, additional MRI sequences have been combined with DCE MRI; this approach is known as multiparametric MRI (mpMRI). In the multiparametric context, DWI with apparent diffusion coefficient (ADC) mapping or more advanced markers (see Chapter 8 ) has emerged as the most robust and valuable additional parameter, with a reported sensitivity of up to 96% for breast cancer detection and a specificity of up to 100% for breast tumor characterization; it is therefore increasingly implemented in clinical routine.

Compared with other imaging modalities, breast MRI is the most sensitive for detection and additionally offers quantitative biomarkers with value for breast cancer diagnosis. As a result, it is well suited to AI-based research, and AI-enhanced breast MRI is increasingly studied for a variety of applications, particularly for lesion detection and classification. Table 10.1 summarizes the current studies and its use cases for AI-enhanced breast DWI.