Special Section on Radiomics and Imaging Genomics

Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine

[+] Author Affiliations
Sandy Napel

Stanford University School of Medicine, Radiology Department, 318 Campus Drive #S323, Stanford, California 94305-5014

Maryellen Giger

The University of Chicago, Radiology Department, 5841 S. Maryland Avenue, Chicago, Illinois 60637-1447

J. Med. Imag. 2(4), 041001 (Dec 11, 2015). doi:10.1117/1.JMI.2.4.041001
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Throughout the history of radiology—a medical specialty that came into being shortly after the discovery of x rays in 1895—its practice involved a skilled observer (the radiologist) looking at images and transcribing observations in relation to the indications for the imaging examination and any incidental findings. Radiologists are trained to understand how appearance on imaging correlates with underlying disease/health and strive to report it in unambiguous terms. However, there is variation in interpretation among radiologists,1,2 and even among radiologists speaking the same language, descriptive terminology varies,3,4 thereby making impractical the mass mining of radiological interpretations for discovery of linkages between observations and specific diseases.

Despite these limitations, radiologists continued to study and report on the linkage between specific image features and underlying disease, e.g., contrast enhancement patterns of focal liver lesions on CT and malignant/benign classifications of tumors on breast images. While radiologists were busy understanding and characterizing these “imaging phenotypes,” biologists were making great strides understanding the genomic basis of intracellular processes,5 leading to the ability to characterize the “molecular phenotype” (“-omics,” e.g., genomics, proteomics, metabolomics, transcriptomics, copy number, methylation) through advanced sequencing of tissue from biopsy and/or resection samples.

In the 1980s and 1990s, quantitative imaging scientists and engineers were developing algorithms for the extraction of imaging phenotypes from radiographic images for use in computer-aided detection/diagnosis and for risk assessment and prognostic/predictive tasks.6,7 However, it wasn’t until the early part of the century when researchers began exploring links between the imaging and molecular phenotypes. For example, in 2002, Huo et al. showed the relationship between computerized texture analysis of the breast parenchyma on mammography and presence of the BRAC1/BRCA2 gene mutation.8 In 2007, Segal et al. reported that radiological observations of tumors seen on CT “systematically correlate with the global gene expression programs of primary human liver cancer” derived using microarray analysis of the resected tumor.9 In 2008, Diehn et al. reported linkages between the imaging phenotype of glioblastoma multiforme (GBM) on MRI to the molecular phenotype derived using DNA microarray analysis10 and survival. And in 2010, Bhooshan et al. demonstrated relationships between computer-extracted MRI phenotypes and breast cancer subtype and aggressiveness.11 Many papers have since expanded the literature on deriving quantitative image features, deriving and reducing the interobserver variability of semantic image features, associating image features with molecular phenotypes, genetics, and outcomes, and the results of mining these associations for discovery (e.g., see Refs. 12131415161718).

These and other early studies gave birth to two terms that are increasingly prevalent in the literature today. Radiomics19,20 is a name given to the science of converting medical images into computer-accessible and -searchable data. While the term radiogenomics has previously been used to describe the study of genetic variation associated with response to radiation (radiation genomics),21 in the present context we use radiogenomics (or imaging genomics) to describe relationships between molecular and imaging phenotypes.22 To highlight recent ongoing work in the areas covered by these terms, and promoted through the efforts of various programs including the National Cancer Institute’s Quantitative Imaging Network (QIN),23 the Quantitative Imaging Biomarkers Alliance (QIBA),24 and the American Association of Physicists in Medicine (AAPM),25 this issue of the Journal of Medical Imaging contains a Special Section on Radiomics and Imaging Genomics.

These ten JMI articles describe advances in radiomics and imaging genomics along several fronts. Nyflot  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041002]] and , Echegaray  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041011]] explore variations in radiomic signatures as a function of stochastic noise and region-of-interest segmentation, respectively. Nyflot concludes that radiomics studies should specify standard acquisition protocols, while Echegaray demonstrates that there may be many radiomics features (specifically some gray-value statistics and textures) that are minimally affected by differences in segmentation boundaries.

Also within this special section, the value of one-dimensional gray-value statistics, as well as multiscale and -orientation gray-level variations (i.e., image textures), are demonstrated for several purposes. For example, , Lee  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041006]] apply these metrics to tumor habitats (regions with different intensity characteristics) in MR scans of patients with GBM, and show associations with 12-month survival. , Ghosh  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041009]] show that texture features of tumors in CT scans of patients with clear cell renal carcinoma can predict specific gene mutations. , Mattonen  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041010]] show that the image texture within automatically generated regions of interest in CT scans of patients who have had stereotactic ablative radiotherapy for lung cancer treatment can be used to separate radiation necrosis from recurrence. , Tiwari  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041008]] use texture metrics on different types of MRI scans of patients treated by laser ablation for neuropathic cancer pain that were predictive of early treatment response. Finally, while most studies of texture have been centered on the tumors themselves, , Dilger  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041004]] show that texture metrics computed from regions of interest surrounding lung nodules have value in the prediction of malignancy.

Other investigators report novel frameworks for integrating radiomic and -omics data and mining the resulting databases for associations with clinical data. For example, for breast cancer, , Wu  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041005]] integrate mammographic features and SNPs with traditional risk factors to improve risk prediction, and , Guo  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041007]] show significant correlations of DCE-MRI radiomic features to clinical and genomic characteristics. Both of these and many other studies argue for continued development and expansion of large imaging26 and -omics27 databases utilizing standardized protocols. Finally, lest one conclude that image features are only useful in cancer research, see , Xie  et al.CrossRef[[XSLOpenURL/10.1117/1.JMI.2.4.041003]] for a report on detecting ventricular-septal defects in mouse embryos through segmentation and pixel analysis.

A word of caution, however. While radiomics and imaging genomics articles continue to populate the literature, many of them (including some in this special section) (a) involve small numbers of subjects with respect to the number of radiomics features investigated, thereby raising concerns of over fitting; or (b) do not report validations in external cohorts, thereby limiting generalizability to additional patient populations, imaging by different scanner types, etc. These articles are important landmarks and vehicles for disseminating ideas, but themselves should be seen as pilot studies, suggestive of further investigation and validation. Those of us in this research community should remain conscious that correlation does not imply causation28 and that we need to strive to fully validate and generalize our methods and results.

References

and Armato  S. G.  III  et al., “The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans,” Acad. Radiol.. 14, (11 ), 1409 –1421 (2007).CrossRef
Hillman  B. J.  et al., “Improving diagnostic accuracy: a comparison of interactive and Delphi consultations,” Invest. Radiol.. 12, (2 ), 112 –115 (1977). 0020-9996 CrossRef
Lowe  H. J.  et al., “Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record,” Methods Inf. Med.. 38, (4–5 ), 303 –307 (1999).
Korenblum  D.  et al., “Managing biomedical image metadata for search and retrieval of similar images,” J. Digit. Imaging. 24, (4 ), 739 –748 (2011).CrossRef
Mirnezami  R., , Nicholson  J., and Darzi  A., “Preparing for precision medicine,” N. Engl. J. Med.. 366, (6 ), 489 –491 (2012). 0028-4793 CrossRef
Giger  M. L., , Chan  H.P., and Boone  J., “Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM,” Med. Phys.. 35, (12 ), 5799 –5820 (2008). 0094-2405 CrossRef
Giger  M. L., , Karssemeijer  N., and Schnabel  J. A., “Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer,” Annu. Rev. Biomed. Eng.. 15, , 327 –357 (2013). 1523-9829 CrossRef
Huo  Z.  et al., “Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers,” Radiology. 225, (2 ), 519 –526 (2002). 0033-8419 CrossRef
Segal  E.  et al., “Decoding global gene expression programs in liver cancer by noninvasive imaging,” Nat. Biotechnol.. 25, , 675 –680 (2007). 1087-0156 CrossRef
Diehn  M.  et al., “Identification of noninvasive imaging surrogates for brain tumor gene-expression modules,” Proc. Nat. Acad. Sci. U.S.A.. 105, (13 ), 5213 –5218 (2008). 0027-8424 CrossRef
Bhooshan  N.  et al., “Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers,” Radiology. 254, (3 ), 680 –690 (2010). 0033-8419 CrossRef
Gevaert  O.  et al., “Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results,” Radiology. 264, (2 ), 387 –396 (2012). 0033-8419 CrossRef
Aerts  H. J.  et al., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nat. Commun.. 5, , 4006  (2014).CrossRef
Colen  R.  et al., “NCI workshop report: clinical and computational requirements for correlating imaging phenotypes with genomics signatures,” Transl. Oncol.. 7, (5 ), 556 –569 (2014).CrossRef
Itakura  H.  et al., “Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities,” Sci. Transl. Med.. 7, (303 ) 303ra138  (2015).CrossRef
Grove  O.  et al., “Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma,” PLoS One. 10, (3 ), e0118261  (2015). 1932-6203 CrossRef
Li  H.  et al., “Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers,” Med. Phys.. 41, (3 ), 031917  (2014). 0094-2405 CrossRef
Jaffe  C. C., “Imaging and genomics: is there a synergy?,” Radiology. 264, (2 ), 329 –331 (2012). 0033-8419 CrossRef
Lambin  P.  et al., “Extracting more information from medical images using advanced feature analysis,” Eur. J. Cancer. 48, (4 ), 441 –446 (2012). 0959-8049 CrossRef
Kumar  V.  et al., “Radiomics: the process and the challenges,” Magn. Reson. Imaging. 30, (9 ), 1234 –1248 (2012). 0730-725X CrossRef
Burnet  N. G.  et al., “Radiosensitivity, radiogenomics and RAPPER,” Clin. Oncol.. 18, (7 ), 525 –528 (2006).CrossRef
Rutman  A. M., and Kuo  M. D., “Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging,” Eur. J. Radiol.. 70, (2 ), 232 –241 (2009). 0720-048X CrossRef
Clarke  L. P.  et al., “The quantitative imaging network: NCI’s historical perspective and planned goals,” Transl. Oncol.. 7, , 1 –4 (2014).CrossRef
Buckler  A. J.  et al., “Quantitative imaging test approval and biomarker qualification: interrelated but distinct activities,” Radiology. 259, (3 ), 875 –884 (2011). 0033-8419 CrossRef
AAPM FOREM on Imaging Genomics, Conference Agenda, 30 September–1 October 2014, Houston, Texas http://www.aapm.org/meetings/documents/revfinalAgendaforFOREM09242014.pdf (Accessed 13  November  2015).
Clark  K.  et al., “The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository,” J. Digit. Imaging. 26, (6 ), 1045 –1057 (2013).CrossRef
Tomczak  K., , Czerwinska  P., and Wiznerowicz  M., “The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge,” Contemp Oncol.. 19, (1A ), A68 –77 (2015).CrossRef
Kuo  M. D., and Jamshidi  N., “Behind the numbers: decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations,” Radiology. 270, (2 ), 320 –325 (2014). 0033-8419 CrossRef

Sandy Napel received his BSES from SUNY Stony Brook in 1974 and his MSEE and PhD in EE from Stanford University in 1976 and 1981, respectively. He was formerly VP of engineering at Imatron Inc, and is currently a professor of radiology and, by courtesy, of electrical engineering and medicine (biomedical informatics research) at Stanford University. He co-leads the Stanford Radiology 3D and Quantitative Imaging Lab and the Radiology Department’s Section on Integrative Biomedical Imaging Informatics, where he is developing techniques for linkage of image features to molecular properties of disease.

Maryellen Giger received her BS from Illinois Benedictine College in 1978; MSc from University of Exeter, England, in 1979; and PhD from University of Chicago in 1985. She is the A. N. Pritzker Professor of Radiology of the Committee on Medical Physics and the College at The University of Chicago. She is vice chair of radiology (basic science research) and leads an NIH-funded lab on computer-aided diagnosis and radiomics in collaboration with other scientists to develop predictive image-based signatures for precision medicine.

© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Sandy Napel and Maryellen Giger
"Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine", J. Med. Imag. 2(4), 041001 (Dec 11, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.4.041001


Figures

Tables

References

and Armato  S. G.  III  et al., “The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans,” Acad. Radiol.. 14, (11 ), 1409 –1421 (2007).CrossRef
Hillman  B. J.  et al., “Improving diagnostic accuracy: a comparison of interactive and Delphi consultations,” Invest. Radiol.. 12, (2 ), 112 –115 (1977). 0020-9996 CrossRef
Lowe  H. J.  et al., “Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record,” Methods Inf. Med.. 38, (4–5 ), 303 –307 (1999).
Korenblum  D.  et al., “Managing biomedical image metadata for search and retrieval of similar images,” J. Digit. Imaging. 24, (4 ), 739 –748 (2011).CrossRef
Mirnezami  R., , Nicholson  J., and Darzi  A., “Preparing for precision medicine,” N. Engl. J. Med.. 366, (6 ), 489 –491 (2012). 0028-4793 CrossRef
Giger  M. L., , Chan  H.P., and Boone  J., “Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM,” Med. Phys.. 35, (12 ), 5799 –5820 (2008). 0094-2405 CrossRef
Giger  M. L., , Karssemeijer  N., and Schnabel  J. A., “Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer,” Annu. Rev. Biomed. Eng.. 15, , 327 –357 (2013). 1523-9829 CrossRef
Huo  Z.  et al., “Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers,” Radiology. 225, (2 ), 519 –526 (2002). 0033-8419 CrossRef
Segal  E.  et al., “Decoding global gene expression programs in liver cancer by noninvasive imaging,” Nat. Biotechnol.. 25, , 675 –680 (2007). 1087-0156 CrossRef
Diehn  M.  et al., “Identification of noninvasive imaging surrogates for brain tumor gene-expression modules,” Proc. Nat. Acad. Sci. U.S.A.. 105, (13 ), 5213 –5218 (2008). 0027-8424 CrossRef
Bhooshan  N.  et al., “Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers,” Radiology. 254, (3 ), 680 –690 (2010). 0033-8419 CrossRef
Gevaert  O.  et al., “Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results,” Radiology. 264, (2 ), 387 –396 (2012). 0033-8419 CrossRef
Aerts  H. J.  et al., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nat. Commun.. 5, , 4006  (2014).CrossRef
Colen  R.  et al., “NCI workshop report: clinical and computational requirements for correlating imaging phenotypes with genomics signatures,” Transl. Oncol.. 7, (5 ), 556 –569 (2014).CrossRef
Itakura  H.  et al., “Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities,” Sci. Transl. Med.. 7, (303 ) 303ra138  (2015).CrossRef
Grove  O.  et al., “Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma,” PLoS One. 10, (3 ), e0118261  (2015). 1932-6203 CrossRef
Li  H.  et al., “Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers,” Med. Phys.. 41, (3 ), 031917  (2014). 0094-2405 CrossRef
Jaffe  C. C., “Imaging and genomics: is there a synergy?,” Radiology. 264, (2 ), 329 –331 (2012). 0033-8419 CrossRef
Lambin  P.  et al., “Extracting more information from medical images using advanced feature analysis,” Eur. J. Cancer. 48, (4 ), 441 –446 (2012). 0959-8049 CrossRef
Kumar  V.  et al., “Radiomics: the process and the challenges,” Magn. Reson. Imaging. 30, (9 ), 1234 –1248 (2012). 0730-725X CrossRef
Burnet  N. G.  et al., “Radiosensitivity, radiogenomics and RAPPER,” Clin. Oncol.. 18, (7 ), 525 –528 (2006).CrossRef
Rutman  A. M., and Kuo  M. D., “Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging,” Eur. J. Radiol.. 70, (2 ), 232 –241 (2009). 0720-048X CrossRef
Clarke  L. P.  et al., “The quantitative imaging network: NCI’s historical perspective and planned goals,” Transl. Oncol.. 7, , 1 –4 (2014).CrossRef
Buckler  A. J.  et al., “Quantitative imaging test approval and biomarker qualification: interrelated but distinct activities,” Radiology. 259, (3 ), 875 –884 (2011). 0033-8419 CrossRef
AAPM FOREM on Imaging Genomics, Conference Agenda, 30 September–1 October 2014, Houston, Texas http://www.aapm.org/meetings/documents/revfinalAgendaforFOREM09242014.pdf (Accessed 13  November  2015).
Clark  K.  et al., “The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository,” J. Digit. Imaging. 26, (6 ), 1045 –1057 (2013).CrossRef
Tomczak  K., , Czerwinska  P., and Wiznerowicz  M., “The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge,” Contemp Oncol.. 19, (1A ), A68 –77 (2015).CrossRef
Kuo  M. D., and Jamshidi  N., “Behind the numbers: decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations,” Radiology. 270, (2 ), 320 –325 (2014). 0033-8419 CrossRef

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