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Virtual Image-based Biopsy of Lung Metastases: The Promise of Radiomics

Published:November 09, 2022DOI:https://doi.org/10.1016/j.acra.2022.10.030
      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 (
      • Mayerhoefer ME
      • Materka A
      • Langs G
      • et al.
      Introduction to radiomics.
      ). Radiomic data, can be combined with genomic information to create signatures that can be matched to therapy in a variety of diseases and cancers (
      • Bodalal Z
      • Trebeschi S
      • Nguyen-Kim TDL
      • et al.
      Radiogenomics: bridging imaging and genomics.
      ). 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 (
      • Lambin P
      • Rios-Velazquez E
      • Leijenaar R
      • et al.
      Radiomics: extracting more information from medical images using advanced feature analysis.
      ). Radiomics allows us to see beyond the human eye with post processing of the data used for image interpretation.
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      References

        • Mayerhoefer ME
        • Materka A
        • Langs G
        • et al.
        Introduction to radiomics.
        J Nucl Med. 2020; 61: 488-495
        • Bodalal Z
        • Trebeschi S
        • Nguyen-Kim TDL
        • et al.
        Radiogenomics: bridging imaging and genomics.
        Abdom Radiol. 2019; 44: 1960-1984
        • Lambin P
        • Rios-Velazquez E
        • Leijenaar R
        • et al.
        Radiomics: extracting more information from medical images using advanced feature analysis.
        Eur J Cancer. 2012; 48: 441-446
        • Liu Z
        • Wang S
        • Dong D
        • et al.
        The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges.
        Theranostics. 2019; 9: 1303-1322
        • Waldman CE
        • Hermel M
        • Hermel JA
        • et al.
        Artificial intelligence in healthcare: a primer for medical education in radiomics.
        Per Med. 2022; 19: 445-456
        • Ngiam KY
        • Khor IW.
        Big data and machine learning algorithms for health-care delivery.
        Lancet Oncol. 2019; 20: e262-e273
        • Litvin AA
        • Burkin DA
        • Kropinov AA
        • et al.
        Radiomics and digital image texture analysis in oncology (Review).
        Sovrem Tehnol v Med. 2021; 13: 97-106
        • Seebacher NA
        • Stacy AE
        • Porter GM
        • et al.
        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
        • Gill RR
        • Murphy DJ
        • Kravets S
        • et al.
        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
        • Mamata H
        • Tokuda J
        • Gill RR
        • et al.
        Clinical application of pharmacokinetic analysis as a biomarker in solitary pulmonary nodules : dynamic contrast enhanced MR imaging.
        Magn Reson Med. 2011; 19: 1-9
        • Li M
        • Narayan V
        • Gill RR
        • et al.
        Computer-aided diagnosis of ground-glass opacity nodules using open-source software for quantifying tumor heterogeneity.
        Am J Roentgenol. 2017; 209: 1216-1227
        • Maldonado F
        • Boland JM
        • Raghunath S
        • et al.
        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
        • Shi L
        • He Y
        • Yuan Z
        • et al.
        Radiomics for response and outcome assessment for non-small cell lung cancer.
        Technol Cancer Res Treat. 2018; : 17https://doi.org/10.1177/1533033818782788
        • Gill RR
        • Jaklitsch MT
        • Jacobson FL.
        Controversies in lung cancer screening.
        J Am Coll Radiol. 2013; 10: 931-936https://doi.org/10.1016/j.jacr.2013.09.013
        • Li M
        • Gao F
        • Jagadeesan J
        • et al.
        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
        • Shang H
        • Li J
        • Jiao T
        • et al.
        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
        • Lv W
        • Yang M
        • Zhong H
        • et al.
        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
        • Sinkus R
        • Van Beers BE
        • Vilgrain V
        • et al.
        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
        • Buckler AJ
        • Bresolin L
        • Dunnick NR
        • et al.
        A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging.
        Radiology. 2011; 258: 906-914
        • Chalkidou A
        • O’Doherty MJ
        • Marsden PK
        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
        • Sullivan DC
        • Obuchowski NA
        • Kessler LG
        • et al.
        Metrology standards for quantitative imaging biomarkers1.
        Radiology. 2015; 277: 813-825