Machine and deep learning methods for radiomics

Abstract

Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

“…Machine learning (ML) is the discipline that builds mathematical models and computer algorithms to perform specific tasks by learning patterns and inferences directly from data using computers, without being explicitly programmed to conduct these tasks [10]. ML algorithms can be either used for supervised learning, where the machine is provided with output labels to be associated with a set of input variables, or unsupervised learning.…”

“…To reduce overfitting in DL, data augmentation (e.g., by the affine transformation of the images) during training is commonly implemented [10], and layers in the networks are specialized in reducing overfitting, such as dropout layers [108]. On the other side, DL suffers from other sources of uncertainties (e.g., the presence of many local minima in the loss function and the stochastic nature of training algorithms), so that repeating model training multiple times does not necessarily produce the same model [2].…”

Radiomics in radiation oncology—basics, methods, and limitations

“…Repeatability is commonly assessed by the extraction of radiomics features from repeated acquisitions of images under identical or near-identical acquisition and processing parameters. In contrast, reproducibility of radiomics features, also called robustness, is measured if the acquisition parameters and applied measuring systems differ [36]. A recent review performed an extensive literature search and identified radiomics features that were shown to be repeatable and reproducible among the investigated studies [37].…”

“…Another limitation to the application of radiomics models in clinical routine is the problem of interpretability of the extracted features and the generated models. Mostly, radiomics analysis are perceived as a "black box", i.e., it is very difficult to (clinically) interpret the generated predictions [36]. However, some methods to improve the interpretability of radiomics analyses have been developed, such as graph-based approaches for feature-based radiomics [44] or visualization tools for deep learning-based radiomics that highlight regions of the segmented tumor according to their importance for the prediction of the generated classifier [45].…”

Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice

“…There are many publications and special issues dedicated to the usage of radiomics to support clinical applications in combination with recent spread of advanced machine learning methods. 3,4 Yet questions remain if the development of radiomics makes it ready for prospective clinical use. Herein, we brought in two medical physics experts both of whom have extensive knowledge in clinical practice and radiomics research.…”