Advertisement

Promoting Collaborations Between Radiologists and Scientists

Published:August 24, 2017DOI:https://doi.org/10.1016/j.acra.2017.05.020
      Radiology as a discipline thrives on the dynamic interplay between technological and clinical advances. Progress in almost all facets of the imaging sciences is highly dependent on complex tools sourced from physics, engineering, biology, and the clinical sciences to obtain, process, and view imaging studies. The application of these tools, however, requires broad and deep medical knowledge about disease pathophysiology and its relationship with medical imaging. This relationship between clinical medicine and imaging technology, nurtured and fostered over the past 75 years, has cultivated extraordinarily rich collaborative opportunities between basic scientists, engineers, and physicians. In this review, we attempt to provide a framework to identify both currently successful collaborative ventures and future opportunities for scientific partnership. This invited review is a product of a special working group within the Association of University Radiologists-Radiology Research Alliance.

      Key Words

      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 access
      One-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 Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Hillman B.J.
        The past 25 years in medical imaging research: a memoir.
        Radiology. 2000; 214: 11-14
        • Gore J.C.
        Biomedical imaging: current and future trends.
        (Available at:)
        • Johnson D.R.
        • O'Neill B.P.
        Glioblastoma survival in the United States before and during the temozolomide era.
        J Neurooncol. 2012; 107: 359-364
        • Stupp R.
        • Mason W.P.
        • van den Bent M.J.
        • et al.
        Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma.
        NEJM. 2005; 352: 987-996
        • Korfiatis P.
        • Kline T.L.
        • Coufalova L.
        • et al.
        MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas.
        Med Phys. 2016; 43: 2835-2844
        • Kalender W.A.
        • Kolditz D.
        • Stieding C.
        • et al.
        Technical feasibility proof for high-resolution low-dose photon-counting CT of the breast.
        Eur Radiol. 2016; 27: 1081-1086
        • Foroutan P.
        • Kreahling J.M.
        • Morse D.L.
        • et al.
        Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy.
        PLoS ONE. 2013; 8: 1-10
        • Mackay R.I.
        • Burnet N.G.
        • Green S.
        • et al.
        Radiotherapy physics research in the UK: challenges and proposed solutions.
        British Journal of Radiology. 2012; 85: 1354-1362
        • Lewiss R.E.
        • Chan W.
        • Sheng A.Y.
        • et al.
        Research priorities in the utilization and interpretation of diagnostic imaging: education, assessment, and competency.
        Acad Emerg Med. 2015; 22: 1447-1454
        • Majithia A.
        • Bhat D.L.
        Editorial commentary: bringing precision medicine to intervention: virtually a reality.
        Trends Cardiovasc Med. 2016; 26: 474-476
        • Huang Y.
        • Liu Z.
        • He L.
        • et al.
        Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer.
        Radiology. 2016; 281: 947-957
        • Jaffe C.
        Imaging and genomics: is there a synergy?.
        Radiology. 2012; 264: 329-331
        • National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease
        Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease.
        National Academies Press, Washington (DC)2011
        • Mirnezami R.
        • Nicholson J.
        • Darzi A.
        Preparing for precision medicine.
        N Engl J Med. 2012; 366: 489-491
        • Canonica G.
        • Bachert C.
        • Hellings P.
        • et al.
        Allergen Immunotherapy (AIT): a prototype of precision medicine.
        World Allergy Organ J. 2015; 8: 31
        • Comabella M.
        • Sastre-Garriga J.
        • Montalban X.
        Precision medicine in multiple sclerosis: biomarkers for diagnosis, prognosis, and treatment response.
        Curr Opin Neurol. 2016; 29: 254-262
        • Dugas C.
        • Schussler J.M.
        Advanced technology in interventional cardiology: a roadmap for the future of precision coronary interventions.
        Trends Cardiovasc Med. 2016; 26: 466-473
        • Golan D.
        • Staun-Ram E.
        • Miller A.
        Shifting paradigms in multiple sclerosis: from disease-specific, through population-specific toward patient-specific.
        Curr Opin Neurol. 2016; 29: 354-361
        • Xing X.
        • Liang D.
        • Huang Y.
        • et al.
        The application of proteomics in different aspects of hepatocellular carcinoma research.
        J Proteomics. 2016; 145: 70-80
        • Gillies R.J.
        • Kinahan P.E.
        • Hricak H.
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Gutman D.
        • Cooper L.A.D.
        • Hwang S.N.
        • et al.
        MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.
        Radiology. 2013; 267: 560-569
        • Mazurowski M.
        • Zhang J.
        • Peters K.B.
        • et al.
        Computer-extracted MR imaging features are associated with survival in glioblastoma patients.
        J Neurooncol. 2014; 120: 483-488
        • Mazurowski M.
        • Zhang J.
        • Grimm L.J.
        • et al.
        Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
        Radiology. 2014; 273: 365-372
        • Henzler T.
        • Fink C.
        Functional computed tomography in oncology and cardiovascular imaging: a key player in the era of precision medicine and radiogenomics.
        Eur J Radiol. 2015; 84: 2345-2346
        • Zinn P.
        • Singh S.K.
        • Kotrotsou A.
        • et al.
        Clinically applicable and biologically validated MRI radiomic test method predicts glioblastoma genomic landscape and survival.
        Neurosurgery. 2016; 63: 156-157
        • Nakamura K.
        • Imafuku N.
        • Nishida T.
        • et al.
        Measurement of the minimum apparent diffusion coefficient (ADCmin) of the primary tumor and CA125 are predictive of disease recurrence for patients with endometrial cancer.
        Gynecol Oncol. 2012; 124: 335-339
        • Schlett C.
        • Hendel T.
        • Weckbach S.
        • et al.
        Population-based imaging and radiomics: rationale and perspective of the German national cohort MRI study.
        Rofo. 2016; 188: 652-661
        • Garzón B.
        • Emblem K.E.
        • Mouridsen K.
        • et al.
        Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction.
        Acta Radiol. 2011; 52: 1052-1060
        • Li H.
        • Zhu Y.
        • Burnside E.S.
        • et al.
        MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays.
        Radiology. 2016; : 152110
        • Hawkins S.
        • Wang H.
        • Liu Y.
        • et al.
        Predicting malignant nodules from screening CTs.
        J Thorac Oncol. 2016; 11: 2120-2128
        • Holdsworth C.
        • Badawi R.D.
        • Manola J.B.
        • et al.
        CT and PET: early prognostic indicators of response to imatinib mesylate in patients with gastrointestinal stromal tumor.
        AJR Am J Roentgenol. 2007; 189: W324-W330
        • Zinn P.
        • Mahajan B.
        • Sathyan P.
        • et al.
        Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme.
        PLoS ONE. 2011; 6 (e25451)
        • Gillies R.
        • Kinahan P.E.
        • Hricak H.
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Kuo M.
        • Jamshidi N.
        Behind the numbers: decoding molecular phenotypes with radiogenomics—guiding principles and technical considerations.
        Radiology. 2014; 270: 320-325
        • Mazurowski M.
        Radiogenomics: what it is and why it is important?.
        J Am Coll Radiol. 2015; 12: 862-866
        • Ellingson B.
        Radiogenomics and imaging phenotypes in glioblastoma: novel observations and correlation with molecular characteristics.
        Curr Neurol Neurosci Rep. 2015; 15: 506
        • Herold C.
        • Lewin J.S.
        • Wibmer A.G.
        • et al.
        Imaging in the age of precision medicine: summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology.
        Radiology. 2016; 279: 226-238
        • Rutman A.
        • Kuo M.D.
        Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging.
        Eur J Radiol. 2009; 70: 232-241
        • Panth K.
        • Leijenaar R.T.
        • Carvalho S.
        • et al.
        Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells.
        Radiother Oncol. 2015; 116: 462-466
        • Thrall J.
        Moreton lecture: imaging in the age of precision medicine.
        J Am Coll Radiol. 2015; 12: 1106-1111
        • Collins F.S.
        • Varmus H.
        A new initiative on precision medicine.
        N Engl J Med. 2015; 372: 793-795
        • Kumar V.
        • Gua Y.
        • Basu S.
        • et al.
        Radiomics: the process and the challenges.
        Magn Reson Imaging. 2012; 30: 1234-1248
        • Eisenhauer E.
        • Therasse P.
        • Bogaerts J.
        • et al.
        New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).
        Eur J Cancer. 2009; 45: 228-247
        • Therasse P.
        • Arbuck S.G.
        • Eisenhauer E.A.
        • et al.
        New guidelines to evaluate the response to treatment in solid tumors: European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada.
        J Natl Cancer Inst. 2000; 92: 205-216
        • Yip S.
        • Aerts H.J.
        Applications and limitations of radiomics.
        Phys Med Biol. 2016; 61: R150-R166
        • 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
        • Datta S.
        • Narayana P.A.
        A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis.
        Neuroimage Clin. 2013; 2: 184-196
        • Mansoor A.
        • Bagci U.
        • Foster B.
        • et al.
        Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends.
        Radiographics. 2015; 35: 1056-1076
        • Despotović I.
        • Goossens B.
        • Philips W.
        MRI segmentation of the human brain: challenges, methods, and applications.
        Comput Math Methods Med. 2015; 2015: 450341
        • Wang L.
        • Chitiboi T.
        • Meine H.
        • et al.
        Principles and methods for automatic and semi-automatic tissue segmentation in MRI data.
        MAGMA. 2016; 29: 95-110
        • Sudarshan V.
        • Mookiah M.R.
        • Acharya U.R.
        • et al.
        Application of wavelet techniques for cancer diagnosis using ultrasound images: a review.
        Comput Biol Med. 2016; 69: 97-111
        • Hatt M.
        • Tixier F.
        • Pierce L.
        • et al.
        Characterization of PET/CT images using texture analysis: the past, the present… any future?.
        Eur J Nucl Med Mol Imaging. 2016; 44: 151-165
        • Boone J.
        Radiological interpretation 2020: toward quantitative image assessment.
        Med Phys. 2007; 34: 4173-4179
        • Branstetter 4th, B.F.
        Basics of imaging informatics. Part 1.
        Radiology. 2007; 243: 656-667
        • Schafer J.F.
        • Gatidis S.
        • Schmidt H.
        • et al.
        Simultaneous whole-body PET/MR imaging in comparison to PET/CT in pediatric oncology: initial results.
        Radiology. 2014; 273: 220-231
        • Yu J.P.
        • Kansagra A.P.
        • Thaker A.
        • et al.
        Building for tomorrow today: opportunities and directions in radiology resident research.
        Acad Radiol. 2015; 22: 50-57
        • van Ginneken B.
        • Schaefer-Prokop C.M.
        • Prokop M.
        Computer-aided diagnosis: how to move from the laboratory to the clinic.
        Radiology. 2011; 261: 719-732
        • Giger M.L.
        • Chan H.P.
        • Boone J.
        Anniversary paper: history and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.
        Med Phys. 2008; 35: 5799-5820
        • Wallis M.G.
        • Walsh M.T.
        • Lee J.R.
        A review of false negative mammography in a symptomatic population.
        Clin Radiol. 1991; 44: 13-15
        • Birdwell R.L.
        • Ikeda D.M.
        • O'Shaughnessy K.F.
        • et al.
        Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection.
        Radiology. 2001; 219: 192-202
        • Lai S.M.
        • Li X.
        • Biscof W.F.
        On techniques for detecting circumscribed masses in mammograms.
        IEEE Trans Med Imaging. 1989; 8: 377-386
        • Chan H.P.
        • Doi K.
        • Galhotra S.
        • et al.
        Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography.
        Med Phys. 1987; 14: 538-548
        • Doi K.
        • Huang H.K.
        Computer-aided diagnosis (CAD) and image-guided decision support.
        Comp Med Imag Graph. 2007; 31: 3
        • Chan H.P.
        • Doi K.
        • Vyborny C.J.
        • et al.
        Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.
        Invest Radiol. 1990; 25: 1102-1110
        • Birdwell R.L.
        • Bandodkar P.
        • Ikeda D.M.
        Computer-aided detection with screening mammography in a university hospital setting.
        Radiology. 2005; 236: 451-457
        • Morton M.J.
        • Whaley D.H.
        • Brandt K.R.
        • et al.
        Screening mammograms: interpretation with computer-aided detection—prospective evaluation.
        Radiology. 2006; 239: 375-383
        • Wang S.
        • Summers R.M.
        Machine learning and radiology.
        Med Image Anal. 2012; 16: 933-951
        • Bauce M.
        • Capuani S.
        • Di Domenico G.
        • et al.
        The GAP Project—GPU for Real-time Applications in High Energy Physics and Medical Imaging.
        (2014 19th IEEE-NPSS Real Time Conference (Rt))2014
        • Bryan R.N.
        Look Ahead—Machine Learning in Radiology. RSNA.org: RSNA.
        2016 (Noted radiologist Nick Bryan, M.D., Ph.D. discusses the role of Machine Learning (ML) in the radiology practice of tomorrow)
        • Summers R.M.
        Road maps for advancement of radiologic computer-aided detection in the 21st century.
        Radiology. 2003; 229: 11-13
        • Summers R.M.
        Progress in fully automated abdominal CT interpretation.
        AJR Am J Roentgenol. 2016; 207: 67-79
        • Raghupathi W.
        • Raghupathi V.
        Big data analytics in healthcare: promise and potential.
        Health information science and systems. 2014; 2: 3
        • Poot J.D.
        • Hartman M.S.
        • Daffner R.H.
        Understanding the US medical school requirements and medical students' attitudes about radiology rotations.
        Acad Radiol. 2012; 19: 369-373
        • Branstetter 4th, B.F.
        • Faix L.E.
        • Humphrey A.L.
        • et al.
        Preclinical medical student training in radiology: the effect of early exposure.
        AJR Am J Roentgenol. 2007; 188: W9-W14
        • Heptonstall N.B.
        • Ali T.
        • Mankad K.
        Integrating radiology and anatomy teaching in medical education in the UK—the evidence, current trends, and future scope.
        Acad Radiol. 2016; 23: 521-526
        • McLachlan J.C.
        • Bligh J.
        • Bradley P.
        • et al.
        Teaching anatomy without cadavers.
        Med Educ. 2004; 38: 418-424
        • Miles K.A.
        Diagnostic imaging in undergraduate medical education: an expanding role.
        Clin Radiol. 2005; 60: 742-745
        • Pujol S.
        • Baldwin M.
        • Nassiri J.
        • et al.
        Using 3D modeling techniques to enhance teaching of difficult anatomical concepts.
        Acad Radiol. 2016; 23: 507-516
        • Kong X.
        • Nie L.
        • Zhang H.
        • et al.
        Do three-dimensional visualization and three-dimensional printing improve hepatic segment anatomy teaching? A randomized controlled study.
        Journal of surgical education. 2016; 73: 264-269
        • Lufler R.S.
        • Zumwalt A.C.
        • Romney C.A.
        • et al.
        Incorporating radiology into medical gross anatomy: does the use of cadaver CT scans improve students' academic performance in anatomy?.
        Anat Sci Educ. 2010; 3: 56-63
        • Bell 3rd, F.E.
        • Wilson L.B.
        • Hoppmann R.A.
        Using ultrasound to teach medical students cardiac physiology.
        Adv Physiol Educ. 2015; 39: 392-396
        • Morgan H.
        • Zeller J.
        • Hughes D.T.
        • et al.
        Applied clinical anatomy: the successful integration of anatomy into specialty-specific senior electives.
        Surgical and radiologic anatomy : SRA. 2016; 39: 95-101
        • Torres A.
        • Staskiewicz G.J.
        • Lisiecka J.
        • et al.
        Bridging the gap between basic and clinical sciences: a description of a radiological anatomy course.
        Anat Sci Educ. 2016; 9: 295-303
        • Eisenstein A.
        • Vaisman L.
        • Johnston-Cox H.
        • et al.
        Integration of basic science and clinical medicine: the innovative approach of the cadaver biopsy project at the Boston University School of Medicine.
        Acad Med. 2014; 89: 50-53