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Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, Leijenaar RTH, Haibe-Kains B, Lambin P, Gillies RJ, Aerts HJWL. Defining the biological basis of radiomic phenotypes in lung cancer. eLife 2017; 6:e23421. [PMID: 28731408 PMCID: PMC5590809 DOI: 10.7554/elife.23421] [Citation(s) in RCA: 252] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Accepted: 07/17/2017] [Indexed: 02/06/2023] Open
Abstract
Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.
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Affiliation(s)
- Patrick Grossmann
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, United States
| | - Olya Stringfield
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, United States
| | - Nehme El-Hachem
- Integrative systems biology, Institut de recherches cliniques de Montreal, Montreal, Canada.
| | - Marilyn M Bui
- Department of Anatomic Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, United States
| | - Emmanuel Rios Velazquez
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
- Department of Radiation Oncology, Research Institute GROW, Maastricht University, Maastricht, Netherlands
| | - Ralph TH Leijenaar
- Department of Radiation Oncology, Research Institute GROW, Maastricht University, Maastricht, Netherlands
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Canada
- Medical Biophysics Department, University of Toronto, Toronto, Canada
| | - Philippe Lambin
- Department of Radiation Oncology, Research Institute GROW, Maastricht University, Maastricht, Netherlands
| | - Robert J Gillies
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, United States
| | - Hugo JWL Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, United States
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, United States
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Rabbani MG, Hossain SA, Islam KK, Uddin SN. Constitutive Photomorphogensis Protein1 (COP1) mediated p53 pathway and its oncogenic role. BIOMEDICAL RESEARCH AND THERAPY 2015. [DOI: 10.7603/s40730-014-0022-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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