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).
Highlights
In the new era of precision medicine, interest has grown in exploring potential biomarkers embedded in different images
Considering that deep learning methods are not the focus of this study, we have focused on their application in radiomic analysis
5.3.1 Radiotherapy Toxicity This approach initially arose in the task of predicting gastrointestinal and genitourinary toxicity after RT for prostate cancer, and the findings revealed that dosiomic features containing spatial relationships between voxel doses improved predictive performance [157]
Summary
In the new era of precision medicine, interest has grown in exploring potential biomarkers embedded in different images. The development of advanced machine and deep learning algorithms has enabled capturing the shape and texture of tissues of concern from multimodality images such as X-ray, computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET), and ultrasound (US). These integrated computational and analytical methods for medical images called radiomics [1, 2] are an emerging field of study. Radiomics and deep learning-based radiomics were reviewed, focusing on the types of characteristics, approaches for extraction and selection, statistical analysis, predictive models, and depth feature-based methods Their latest applications and advances in radiology, multimodality, pathology images, and 3D RT dose distribution are summarized and analyzed. Best of our knowledge, no systemic reports are available on the progress and challenges associated with dosiomics
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