Cited by 63Radiomics and Machine Learning in Medical ImagingCancer Genomics and DiagnosticsMedical Imaging Techniques and Applications

Non-Invasive Tumor Genotyping Using Radiogenomic Biomarkers, a Systematic Review and Oncology-Wide Pathway Analysis

Open access
Jansen, Robin W., van Amstel, Paul, Martens, Roland M., Kooi, Irsan E., Wesseling, Pieter, de Langen, Adrianus J., Menke-Van der Houven van Oordt, Catharina W., Jansen, Bernard H.E. +5 more·2018·Oncotarget

Abstract

// Robin W. Jansen 1 , Paul van Amstel 1 , Roland M. Martens 1 , Irsan E. Kooi 2 , Pieter Wesseling 3, 4 , Adrianus J. de Langen 5 , Catharina W. Menke-Van der Houven van Oordt 6 , Bernard H.E. Jansen 1 , Annette C. Moll 7 , Josephine C. Dorsman 2 , Jonas A. Castelijns 1 , Pim de Graaf 1 and Marcus C. de Jong 1 1 Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands 2 Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands 3 Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands 4 Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Utrecht, The Netherlands 5 Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands 6 Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands 7 Department of Ophthalmology, VU University Medical Center, Amsterdam, The Netherlands Correspondence to: Marcus C. de Jong, email: mc.dejong@vumc.nl Keywords: radiogenomics; non-invasive; genotyping; biomarker; precision medicine Received: August 11, 2017 Accepted: February 26, 2018 Published: April 13, 2018 ABSTRACT With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR -mutations and ALK -rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

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  • Multimodal deep learning integrates histopathology, radiology, genomics, and clinical records to create unified patient-level representations.

    “The most active line of work is multimodal deep learning (MDL), which integrates histopathology (“pathomics”), radiology (“radiomics”), genomics, and clinical records to create unified patient-level representations [70,71,72] (Figure 4).”

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Authors
Jansen, R. W., van Amstel, P., Martens, R. M., Kooi, I. E., Wesseling, P., de Langen, A. J., Menke-Van der Houven van Oordt, C. W., Jansen, B. H. E., Moll, A. C., Dorsman, J. C., Castelijns, J. A., de Graaf, P., & de Jong, M. C.
Affiliations
  • Gemeente Amsterdam (NL)
  • Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
  • Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
  • University Medical Center Utrecht (NL)
  • Department of Respiratory Diseases, VU University Medical Center, Amsterdam, The Netherlands
  • Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands
Venue
Oncotarget
Published
2018-04-13
Volume
9
Issue
28
Pages
20134-20155
ISSN
1949-2553 (ISSN-L)
OpenAlex
W2797022893
Cited by
63
References
239
Citations by year
2026: 3 · 2025: 9 · 2024: 3 · 2023: 10 · 2022: 6 · 2021: 5 · 2020: 15 · 2019: 10 · 2018: 2
FWCI
1.25
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Oncotarget (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • Pure Amsterdam UMC (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Keywords
radiogenomics, imaging biomarkers, 1p/19q codeletion, glioma, non-small cell lung cancer, EGFR mutation, EGFR amplification, EGFR tyrosine kinase inhibitor resistance, tumor heterogeneity, FDG-PET

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