Radiomics for Precision Medicine in Glioblastoma

Aftab, Kiran; Aamir, Faiqa Binte; Mallick, Saad; Mubarak, Fatima; Pope, Whitney; Mikkelsen, Tom; Rock, Jack; Enam, Ather

doi:10.21203/rs.3.rs-905421/v1

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…Classifiers based on various combinations of MRI sequences, genetic information, and clinical data can predict tumor diagnosis, overall survival, and treatment response with reasonable accuracy and non-invasively. Radiomics has the potential to transform the scope of glioma management through personalized medicine, but the application in glioma is still in its infancy and has not yet been translated into clinical decision making (61). Larger sample sizes, standardized image acquisition and data extraction techniques will be needed in the future to develop machine learning models that can be effectively translated into clinical practice.…”

Section: Radiomics Applied To Individualized Treatment Of Gliomamentioning

confidence: 99%

Research on application of radiomics in glioma: a bibliometric and visual analysis

Chen,

Du,

Yang

et al. 2023

Front. Oncol.

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BackgroundWith the continuous development of medical imaging informatics technology, radiomics has become a new and evolving field in medical applications. Radiomics aims to be an aid to support clinical decision making by extracting quantitative features from medical images and has a very wide range of applications. The purpose of this study was to perform a bibliometric and visual analysis of scientific results and research trends in the research application of radiomics in glioma.MethodsWe searched the Web of Science Core Collection (WOScc) for publications related to glioma radiomics. A bibliometric and visual analysis of online publications in this field related to countries/regions, authors, journals, references and keywords was performed using CiteSpace and R software.ResultsA total of 587 relevant literature published from 2012 to September 2022 were retrieved in WOScc, and finally a total of 484 publications were obtained according to the filtering criteria, including 393 (81.20%) articles and 91 (18.80%) reviews. The number of relevant publications increases year by year. The highest number of publications was from the USA (171 articles, 35.33%) and China (170 articles, 35.12%). The research institution with the highest number of publications was Chinese Acad Sci (24), followed by Univ Penn (22) and Fudan Univ (21). WANG Y (27) had the most publications, followed by LI Y (22), and WANG J (20). Among the 555 co-cited authors, LOUIS DN (207) and KICKINGEREDER P (207) were the most cited authors. FRONTIERS IN ONCOLOGY (42) was the most published journal and NEURO-ONCOLOGY (412) was the most co-cited journal. The most frequent keywords in all publications included glioblastoma (187), survival (136), classification (131), magnetic resonance imaging (113), machine learning (100), tumor (82), and feature (79), central nervous system (66), IDH (57), and radiomics (55). Cluster analysis was performed on the basis of keyword co-occurrence, and a total of 16 clusters were formed, indicating that these directions are the current hotspots of radiomics research applications in glioma and may be the future directions of continuous development.ConclusionIn the past decade, radiomics has received much attention in the medical field and has been widely used in clinical research applications. Cooperation and communication between countries/regions need to be enhanced in future research to promote the development of radiomics in the field of medicine. In addition, the application of radiomics has improved the accuracy of pre-treatment diagnosis, efficacy prediction and prognosis assessment of glioma and helped to promote the development into precision medicine, the future still faces many challenges.

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Section: Radiomics Applied To Individualized Treatment Of Gliomamentioning

confidence: 99%

Research on application of radiomics in glioma: a bibliometric and visual analysis

Chen,

Du,

Yang

et al. 2023

Front. Oncol.

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“…These methods enable extraction and quantification of imaging characteristics in radiological imaging, specifically pattern or texture analysis, which computationally allocates imaging signatures to pathological imaging changes 214,215 . Combined radiomics features (RF) can be used to predict disease status and changes during specific therapy regimens, on the basis of machine learning approaches in high-throughput agnostic analyses (Figure 6) 216 . Because RF based imaging is increasingly commonly used and, access to computational equipment and powerful computational hardware is increasing, RF based image interpretation is expected to be applied in clinical diagnostics in the coming years 217 .…”

Section: Medical Image Computing In Translational Cancer Researchmentioning

confidence: 99%

Imaging in translational cancer research

Kurz

2022

Cancer Biol Med

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This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.

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“…Previously, it has been shown that both radiomic and pathomic image-based signatures can independently predict outcomes of interest in GBM (5,6,21). Moreover, some studies on GBM and other cancer types support the hypothesis that combining radiomic and pathomic features will even further improve prognostication and enhance the understanding of the disease by means of predictive models (28)(29)(30)(31).…”

Section: Introductionmentioning

confidence: 96%

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

Brancato¹,

Cavaliere²,

Garbino³

et al. 2022

Front. Oncol.

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Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman’s correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as “virtual biopsy”, introducing new insights for omics integration toward a personalized medicine approach.

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Radiomics for Precision Medicine in Glioblastoma

Cited by 3 publications

References 33 publications

Research on application of radiomics in glioma: a bibliometric and visual analysis

Research on application of radiomics in glioma: a bibliometric and visual analysis

Imaging in translational cancer research

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study

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