Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Abstract

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study

“…For instance, the batch effect correction method “ComBat” has recently been demonstrated to substantially reduce inter-scanner biases [ 78 , 79 ], thus allowing for the large-scale harmonisation and pooling of inhomogeneous cohorts [ 80 ]. Furthermore, efforts to simplify radiomics workflows, in particular by automating lesion segmentation and feature processing steps via deep learning, promise to minimise the effect of variable clinical practices on radiomic signatures [ 81 , 82 ]. Ultimately, we again highlight the importance of high-powered prospective studies, with the expectation that a large enough sample size could overcome the inherent heterogeneities in clinical imaging [ 83 ].…”

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images

“…Radiomics features can be divided into morphological features, first-order features, and texture features, which represent different statistical significance [ 40 ]. Morphological features describe the geometric features of the ROI, such as volume, surface area, the surface-to-volume ratio.…”

Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

“…Then, we applied the PyRadiomics tool to combine CT images and masked CT images to obtain textual features based on volumetric data. The features extracted from PyRadiomics contain information about the size, shape, spatial relationship, and image intensity of medical images 23 . A total of 116 radiomics features were obtained for further model development in predicting a 3-year survival rate.…”