QIN “Radiomics: The Process and the Challenges”

By Virendra Kumar, Yuhua Gu, Satrajit Basu, A. Berglund, S. Eschrich, Matthew B. Schabath, K. Forster, Hugo J. W. L. Aerts, Andre L. A. J. Dekker, David A. Fenstermacher and 6 more. Magnetic Resonance Imaging, 2012

2,066 citations recorded by the source corpus.

“Radiomics” refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography (CT), positron emission tomography (PET) or magnetic resonance imaging (MRI). Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomic data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (i) image acquisition and reconstruction (ii) image segmentation and rendering (iii) feature extraction and feature qualification (iv) databases and data sharing for eventual (v) ad hoc informatic analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small cell lung cancer, NSCLC.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/934a2055975c7953d3587acd2d91827660d8a180

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

From big data analysis to personalized medicine for all: challenges and opportunities (2015, neutral): “integrate, analyze, and interpret omics data [10–12].”
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, background): “lung lobe, distance to the organ at risk) have been used.(72)”
Repeatability and Reproducibility of Radiomic Features: A Systematic Review (2018, positive): ““Reproducibility” refers to features that remain the same when imaged using different equipment, different software, different image acquisition settings, or different operators (eg, other clinics), be that in the same subject or in different subjects (14, 20).”
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. (2015, background): “Radiomics [7–9] aims to provide a comprehensive quantification of the tumor phenotype by analyzing robustly [10–12] a large set of quantitative data characterization algorithms.”
Data Analysis Strategies in Medical Imaging (2018, background): “In its current youthful state, data analysis in radiology faces similar challenges (3, 5, 15, 22).”
Introduction to Radiomics (2020, neutral): “, interpatient variability) are preferable (39).”
Radiomics: the facts and the challenges of image analysis (2018, background): “4) and not always feasible for radiomics ’ analysis, requiring very large data sets [46].”
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches (2017, background): “to deepen our understanding of imaging characteristics in cancer.(1,2) The identified quantitative features can be used to alert”
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation (2014, methodology): “Radiomics addresses this issue, and refers to the high throughput extraction of a large number of quantitative and minable imaging features, assuming that these features convey prognostic and predictive information [3,4].”
The role of quantitative PET in predicting cancer treatment outcomes (2014, background): “The extraction of quantitative information from imaging modalities and the relating of this extracted information to biological and clinical endpoints is the subject of a new emerging field referred to as ‘radiomics’ [34, 35].”