Predictive medicine: towards a multi-parametric imaging for a personal risk stratification

  • Eric GuedjEmail author
  • Serge Cammilleri
  • Antoine Verger
Editorial Commentary

Ischemic stroke is a major public health issue with, per 100,000 persons, an incidence rate of 44-176, a disability-adjusted life-year of 80-597, and a mortality rate of 27-42 [1]. The total excess direct and indirect cost would correspond to €1.5 billion, only considering first-ever strokes [2]. Consequently, large financial offsets are expected, both in the healthcare sector and in the social service sector, if the incidence of stroke could be reduced. In this line, a study randomizing 3000 asymptomatic patients showed that successful carotid endarterectomy reduced 10-year stroke risks [3].

Prediction of subsequent ischemic stroke is thus a critical challenge, precisely to identify asymptomatic high-risk patients who would benefit from intervention before rupture of nascent plaques. Beside the well-known risk factors of atherosclerosis, the exact relationship with cancer remains debated [4, 5], the concurrence of the two diseases leading to a more dismal prognosis [5].

Functional imaging, by establishing tools for personal risk stratification, could help to better select asymptomatic high-risk patients requiring an early intervention. Over the past years, public health has indeed greatly benefited from precise and early diagnosis and screening of diseases, which are otherwise incurable or difficult to treat at later stages; as well as from early and precise post-therapeutic evaluation, especially both not to delay another treatment that may be more effective and not to cause unnecessary side effects and/or complications. More recently, medical models have evolved towards a personalized concept, proposing customization of healthcare for each patient with tailored approaches. In this line, imaging PET and SPECT biomarkers are implemented for select individual patients, as well as to guide, predict, and evaluate the most appropriate treatments according to the molecular signature of the disease and of the lesions, in particular in oncology [6] and more recently in neurology [7].

In the present publication of the EJNMMI, Jahae Kim et al. [8] studied 18 F-FDG PET/CT imaging biomarkers that predict ischemic stroke found in 30 of 134 patients initially explored for a cancer, and clinically followed-up during one year. This original study first extends previous findings obtained in a non-tumour population. In this line, Figueroa et al. followed 513 patients without symptomatic cardiovascular disease for a mean of 4.2 years, and found that 18 F-FDG uptake within the wall of the ascending aorta was an independent predictor of future cardiovascular events [9]. The carotid plaque inflammation, as measured by 18 F-FDG PET and correlated with the degree of macrophage infiltration and plaque erosion [10], has been also associated with a high risk of early stroke recurrence, independently of the degree of stenosis [11], and may, thus, be a useful imaging biomarker for high-risk carotid plaque [10, 12], especially in case of imminent vascular event [11, 13], and for yet symptomatic plaques [14].

In tumour populations, previous studies had also shown a specific association between FDG uptake and vascular events, however, with a weak incidence of 0.5-1.6 % [15, 16], by contrast with those of 7.6 % reported here, and specifically for ischemic stroke, by Jahae Kim et al. [8]. This could be due to the fact that these last patients were included from a Department of Neurology, and had all neurological antecedents (but no stroke). It is, of course, thus uncertain whether the findings of Jahae Kim et al. would apply to an asymptomatic cohort in the absence of neurological antecedents.

In addition to its clinical value for this specific population of patients with cancer and neurological antecedents, the multi-parametric and multimodal approach proposed by Jahae Kim et al. [8] appears here as originally, leading to the multivariate validation of a combination of PET and CT biomarkers. In detail, the authors demonstrated the predictive value of arterial multisite FDG uptake (Tissue-to-Background Ratio, TBR, of carotid arteries and abdominal aorta), presence of metabolic PET active tumours, and visceral adipose tissue CT proportions. This model takes advantage of non-specific pathophysiological uptake of FDG for tumour and inflammatory diseases [17]; of whole-body imaging which is particularly adapted for systemic diseases, and here to explore inflammatory processes of atherosclerosis affecting multiple vascular territories; and of hybrid morpho-functional imaging, using CT not only for attenuation correction and anatomical localization but for its own biomarker value.

With a similar approach, dual properties of 18 F-FDG and an opportunity for whole-body imaging have been previously used to concomitantly explore other inflammatory diseases, such as infectious diseases, and cancer, for example for infective endocarditis, to detect both extra-cardiac embolisms [18], and associated tumours [19]. Moreover, multi-parametric PET imaging has been extended to dynamic and dual-phase acquisitions, to distinguish between tumour and inflammatory lesions [20], with a gradually increasing trend of 18 F-FDG uptake over time in malignant cells, and a decreasing or constant trend in inflammatory/infectious processes [21]. On the other hand, several studies had also combined PET and CT biomarkers to improve predictive performance, and especially for further ischemic strokes, using, for example, calcified plaque sum or vessel volume in addition to 18 F-FDG uptake [15, 22]. More recently, Hyafil et al. took advantage of simultaneous acquisition of high-resolution MRI and 18 F-FDG PET imaging to better characterize lesion type and plaque composition [23].

Interestingly, the findings of Jahae Kim et al. [8] point out other morphological parameters, the visceral adipose tissue CT proportions, as a new predictor for ischemic stroke in patients with cancer. As mentioned by the authors, this is of course not surprising given that obesity is a well-known risk factor for atherosclerosis. Pro-inflammatory cytokine secretion associated with obesity could indeed adversely affect endothelial cells, arterial smooth muscle cells, and macrophages in the vessel wall [24].

Beyond aspects developed in this report, and also the possible implication of several radiopharmaceuticals to characterize multiple biological targets [25], multi-parametric imaging nowadays also integrates development and applications of innovative methods of image processing and analysis. While usual metrics such as Standardized Uptake Value (SUV) or Metabolic Tumor Volume (MTV), or even TBR, are known to not fully describe the entire properties of pathological lesions, other properties such as shape and uptake heterogeneity may reflect different biological profiles associated with aggressiveness, or degree of response to a specific treatment, and, consequently, with the prognosis as recently proposed in oncology [26]. Such multi-parametric quantifications could provide complementary indices with higher clinical value in stratifying patients, probably also in non-oncologic diseases.

On the whole, combined PET/CT multi-parametric imaging has now exceeded the initial concept of “anatometabolic” imaging [27], and the only quantification of SUV, by currently proposing new innovative protocols and biomarkers to individually impact healthcare management, and especially for improving personal risk stratification in oncologic and non-oncologic diseases. The study of Jahae Kim et al. [8] highlights this evolution with the identification of original 18 F-FDG PET/CT biomarkers to predict ischemic stroke in patients with cancer.


Compliance with Ethical Standards


This work has been carried out thanks to the support of the A*MIDEX project (n° ANR-11-IDEX-0001-02) funded by the « Investissements d’Avenir » French Government program, managed by the French National Research Agency (ANR).

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.


  1. 1.
    Krishnamurthi RV, Feigin VL, Forouzanfar MH, Mensah GA, Connor M, Bennett DA, et al. Global and regional burden of first-ever ischaemic and haemorrhagic stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. Lancet Glob Health. 2013;1(5):e259–281.CrossRefGoogle Scholar
  2. 2.
    Ghatnekar O, Persson U, Glader E-L, Terént A. Cost of stroke in Sweden: an incidence estimate. Int J Technol Assess Health Care. 2004;20(3):375–80.CrossRefGoogle Scholar
  3. 3.
    Halliday A, Harrison M, Hayter E, Kong X, Mansfield A, Marro J, et al. 10-year stroke prevention after successful carotid endarterectomy for asymptomatic stenosis (ACST-1): a multicentre randomised trial. Lancet Lond Engl. 2010;376(9746):1074–84.CrossRefGoogle Scholar
  4. 4.
    Bang OY, Seok JM, Kim SG, Hong JM, Kim HY, Lee J, et al. Ischemic stroke and cancer: stroke severely impacts cancer patients, while cancer increases the number of strokes. J Clin Neurol Seoul Korea. 2011;7(2):53–9.CrossRefGoogle Scholar
  5. 5.
    Grisold W, Oberndorfer S, Struhal W. Stroke and cancer: a review. Acta Neurol Scand. 2009;119(1):1–16.CrossRefGoogle Scholar
  6. 6.
    Johnson P, Federico M, Kirkwood A, Fosså A, Berkahn L, Carella A, et al. Adapted Treatment Guided by Interim PET-CT Scan in Advanced Hodgkin’s Lymphoma. N Engl J Med. 2016;374(25):2419–29.CrossRefGoogle Scholar
  7. 7.
    Brody M, Liu E, Di J, Lu M, Margolin RA, Werth JL, et al. A Phase II, Randomized, Double-Blind, Placebo-Controlled Study of Safety, Pharmacokinetics, and Biomarker Results of Subcutaneous Bapineuzumab in Patients with mild to moderate Alzheimer’s disease. J Alzheimers Dis JAD. 2016.Google Scholar
  8. 8.
    Jahae Kim, Kang-Ho Choi, Ho-Chun Song, Joon-Tae Kim; Man-Seok Park, Ki-Hyun Choi. 18F-FDG PET/CT imaging factors that predict ischemic stroke in cancer patients. Eur J Nucl Med. 2016.Google Scholar
  9. 9.
    Figueroa AL, Abdelbaky A, Truong QA, Corsini E, MacNabb MH, Lavender ZR, et al. Measurement of arterial activity on routine FDG PET/CT images improves prediction of risk of future CV events. JACC Cardiovasc Imaging. 2013;6(12):1250–9.CrossRefGoogle Scholar
  10. 10.
    Tawakol A, Migrino RQ, Bashian GG, Bedri S, Vermylen D, Cury RC, et al. In vivo 18F-fluorodeoxyglucose positron emission tomography imaging provides a noninvasive measure of carotid plaque inflammation in patients. J Am Coll Cardiol. 2006;48(9):1818–24.CrossRefGoogle Scholar
  11. 11.
    Marnane M, Merwick A, Sheehan OC, Hannon N, Foran P, Grant T, et al. Carotid plaque inflammation on 18F-fluorodeoxyglucose positron emission tomography predicts early stroke recurrence. Ann Neurol. 2012;71(5):709–18.CrossRefGoogle Scholar
  12. 12.
    Kim H-J, Oh M, Moon DH, Yu K-H, Kwon SU, Kim JS, et al. Carotid inflammation on 18F-fluorodeoxyglucose positron emission tomography associates with recurrent ischemic lesions. J Neurol Sci. 2014;347(1–2):242–5.CrossRefGoogle Scholar
  13. 13.
    Skagen K, Johnsrud K, Evensen K, Scott H, Krohg-Sørensen K, Reier-Nilsen F, et al. Carotid plaque inflammation assessed with (18)F-FDG PET/CT is higher in symptomatic compared with asymptomatic patients. Int J Stroke Off J Int Stroke Soc. 2015;10(5):730–6.CrossRefGoogle Scholar
  14. 14.
    Rudd JHF, Warburton EA, Fryer TD, Jones HA, Clark JC, Antoun N, et al. Imaging atherosclerotic plaque inflammation with [18F]-fluorodeoxyglucose positron emission tomography. Circulation. 2002;105(23):2708–11.CrossRefGoogle Scholar
  15. 15.
    Rominger A, Saam T, Wolpers S, Cyran CC, Schmidt M, Foerster S, et al. 18F-FDG PET/CT identifies patients at risk for future vascular events in an otherwise asymptomatic cohort with neoplastic disease. J Nucl Med Off Publ Soc Nucl Med. 2009;50(10):1611–20.Google Scholar
  16. 16.
    Grandpierre S, Desandes E, Meneroux B, Djaballah W, Mandry D, Netter F, et al. Arterial foci of F-18 fluorodeoxyglucose are associated with an enhanced risk of subsequent ischemic stroke in cancer patients: a case-control pilot study. Clin Nucl Med. 2011;36(2):85–90.CrossRefGoogle Scholar
  17. 17.
    Ishimori T, Saga T, Mamede M, Kobayashi H, Higashi T, Nakamoto Y, et al. Increased (18)F-FDG uptake in a model of inflammation: concanavalin A-mediated lymphocyte activation. J Nucl Med Off Publ Soc Nucl Med. 2002;43(5):658–63.Google Scholar
  18. 18.
    Bonfiglioli R, Nanni C, Morigi JJ, Graziosi M, Trapani F, Bartoletti M, et al. 18F-FDG PET/CT diagnosis of unexpected extracardiac septic embolisms in patients with suspected cardiac endocarditis. Eur J Nucl Med Mol Imaging. 2013;40(8):1190–6.CrossRefGoogle Scholar
  19. 19.
    Granados U, Fuster D, Pericas JM, Llopis J, Ninot S, Quintana E, et al. diagnostic accuracy of 18f-FDG PET/CT in infective endocarditis and implantable cardiac electronic device infection: a cross-sectional study. J Nucl Med Off Publ Soc Nucl Med. 2016.Google Scholar
  20. 20.
    Anderson CM, Chang T, Graham MM, Marquardt MD, Button A, Smith BJ, et al. Change of maximum standardized uptake value slope in dynamic triphasic [18F]-fluorodeoxyglucose positron emission tomography/computed tomography distinguishes malignancy from postradiation inflammation in head-and-neck squamous cell carcinoma: a prospective trial. Int J Radiat Oncol Biol Phys. 2015;91(3):472–9.CrossRefGoogle Scholar
  21. 21.
    Houshmand S, Salavati A, Segtnan EA, Grupe P, Høilund-Carlsen PF, Alavi A. Dual-time-point Imaging and Delayed-time-point Fluorodeoxyglucose-PET/Computed Tomography Imaging in Various Clinical Settings. PET Clin. 2016;11(1):65–84.CrossRefGoogle Scholar
  22. 22.
    Kwee RM, Truijman MTB, Mess WH, Teule GJJ, ter Berg JWM, Franke CL, et al. Potential of integrated [18F] fluorodeoxyglucose positron-emission tomography/CT in identifying vulnerable carotid plaques. AJNR Am J Neuroradiol. 2011;32(5):950–4.CrossRefGoogle Scholar
  23. 23.
    Hyafil F, Schindler A, Sepp D, Obenhuber T, Bayer-Karpinska A, Boeckh-Behrens T, et al. High-risk plaque features can be detected in non-stenotic carotid plaques of patients with ischaemic stroke classified as cryptogenic using combined (18)F-FDG PET/MR imaging. Eur J Nucl Med Mol Imaging. 2016;43(2):270–9.CrossRefGoogle Scholar
  24. 24.
    Fantuzzi G, Mazzone T. Adipose tissue and atherosclerosis: exploring the connection. Arterioscler Thromb Vasc Biol. 2007;27(5):996–1003.CrossRefGoogle Scholar
  25. 25.
    Fu L, Liu L, Zhang J, Xu B, Fan Y, Tian J. Comparison of dual-biomarker PIB-PET and dual-tracer PET in AD diagnosis. Eur Radiol. 2014;24(11):2800–9.CrossRefGoogle Scholar
  26. 26.
    Visvikis D, Hatt M, Tixier F, Cheze Le Rest C. The age of reason for FDG PET image-derived indices. Eur J Nucl Med Mol Imaging. 2012;39(11):1670–2.CrossRefGoogle Scholar
  27. 27.
    Fischer BM, Siegel BA, Weber WA, von Bremen K, Beyer T, Kalemis A. PET/CT is a cost-effective tool against cancer: synergy supersedes singularity. Eur J Nucl Med Mol Imaging. 2016.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Nuclear MedicineTimone Hospital, APHMMarseilleFrance
  2. 2.Aix-Marseille University, INT, CNRS UMR 7289MarseilleFrance
  3. 3.Aix-Marseille University, CERIMEDMarseilleFrance
  4. 4.Department of Nuclear Medicine & Nancyclotep Imaging PlatformNancyFrance
  5. 5.IADI, INSERM U947NancyFrance

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