Technical advances allow us to extract quantifiable data at a pixel level from imaging
data and curate it to define phenotypes, assess severity of disease and develop predictive
and prognostic models (
1
). Radiomic data, can be combined with genomic information to create signatures that
can be matched to therapy in a variety of diseases and cancers (
2
). This approach can assist in personalizing therapy by combining anatomical, functional,
and pathological information with blood and tissue biomarkers and demographic and
toxicity profiles of drugs to optimize treatment decisions (
3
). Radiomics allows us to see beyond the human eye with post processing of the data
used for image interpretation.To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Introduction to radiomics.J Nucl Med. 2020; 61: 488-495
- Radiogenomics: bridging imaging and genomics.Abdom Radiol. 2019; 44: 1960-1984
- Radiomics: extracting more information from medical images using advanced feature analysis.Eur J Cancer. 2012; 48: 441-446
- The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges.Theranostics. 2019; 9: 1303-1322
- Artificial intelligence in healthcare: a primer for medical education in radiomics.Per Med. 2022; 19: 445-456
- Big data and machine learning algorithms for health-care delivery.Lancet Oncol. 2019; 20: e262-e273
- Radiomics and digital image texture analysis in oncology (Review).Sovrem Tehnol v Med. 2021; 13: 97-106
- Clinical development of targeted and immune based anti-cancer therapies.J Exp Clin Cancer Res. 2019; 38: 156https://doi.org/10.1186/s13046-019-1094-2
- Success of genomic profiling of non–small cell lung cancer biopsies obtained by trans-thoracic percutaneous needle biopsy.J Surg Oncol. 2018; 118: 1170-1177
- Clinical application of pharmacokinetic analysis as a biomarker in solitary pulmonary nodules : dynamic contrast enhanced MR imaging.Magn Reson Med. 2011; 19: 1-9
- Computer-aided diagnosis of ground-glass opacity nodules using open-source software for quantifying tumor heterogeneity.Am J Roentgenol. 2017; 209: 1216-1227
- Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)–a pilot study.J Thorac Oncol. 2013; 8: 452-460
- Radiomics for response and outcome assessment for non-small cell lung cancer.Technol Cancer Res Treat. 2018; : 17https://doi.org/10.1177/1533033818782788
- Controversies in lung cancer screening.J Am Coll Radiol. 2013; 10: 931-936https://doi.org/10.1016/j.jacr.2013.09.013
- Incremental value of contrast enhanced computed tomography on diagnostic accuracy in evaluation of small pulmonary ground glass nodules.J Thorac Dis. 2015; : 1606-1615https://doi.org/10.3978/j.issn.2072-1439.2015.09.37
- Differentiation of lung metastases originated from different primary tumors using radiomics features based on CT imaging.Acad Radiol. 2022; https://doi.org/10.1016/j.acra.2022.04.008
- Application of dynamic 18F-FDG PET/CT for distinguishing intrapulmonary metastases from synchronous multiple primary lung cancer.Mol Imaging. 2022; : 8081299https://doi.org/10.1155/2022/8081299
- Apparent diffusion coefficient from magnetic resonance imaging as a biomarker in oncology drug development.Eur J Cancer. 2012; 48: 425-431https://doi.org/10.1016/j.ejca.2011.11.034
- A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging.Radiology. 2011; 258: 906-914
- False discovery rates in PET and CT studies with texture features: a systematic review.PLoS One. 2015; 10https://doi.org/10.1371/journal.pone.0124165
- Metrology standards for quantitative imaging biomarkers1.Radiology. 2015; 277: 813-825
Article info
Publication history
Published online: November 09, 2022
Accepted:
October 31,
2022
Received:
October 31,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
