Tumours (neoplasm) are groups of abnormally growing cells, which can be benign (non-cancerous) or malignant (cancerous). Benign tumours grow slowly and do not spread elsewhere. Pre-malignant cells (hyperplasia, atypia, metaplasia, dysplasia) may develop into cancer if not treated. Malignant neoplasm (cancer) grows in an uncontrolled way into nearby tissues and can spread to distant locations (metastasise). Bone marrow, lungs, and liver are the most common sites of metastases.

Fibroblasts synthesize and deposit extracellular matrix (ECM) proteins, which can constitute a major fraction of tumour mass. Vascular network and microcirculation in tumour is disorganized, with nonvascularized areas, arteriovenous anastomoses (shunts), discontinuous capillaries (sinusoids), blood “vessels” without endothelium, and dysfunctional regulation of blood flow. Blood in tumour vasculature may be more viscous than normally because of released ECM proteins. Capillary diameter may be two times larger than in normal tissue. Inadequate blood flow leads to low availability of oxygen and prevalence of hypoxic ([pO2] ≤ 2.5 mmHg) volumes inside the tumour. The efficacy of radiotherapy and most chemotherapies is reduced in hypoxic regions. Acute hypoxia (< 2 h) is caused by temporary reduction of red blood cell flux in tumour microvessels (hypoxemic hypoxia) or blood flow (ischemic hypoxia). Chronic hypoxia (> 2 h) is caused by increased distances between vessels. In hypoxemic hypoxia only the oxygen supply is reduced while the supply of other nutrients and waste removal is preserved. The wound healing and angiogenesis machineries of endothelial cells, tumour-associated macrophages (TAMs), and platelets support the survival of cancer cells and metastasis.

The glucose metabolism in tumours is directed toward glycolysis leading to formation of lactic acid, not only because the availability of oxygen may be reduced, but also in normoxic tumour regions (aerobic glycolysis, Warburg effect). Glucose uptake is upregulated also to produce ribose for nucleotide synthesis via pentose phosphate pathway (Icard and Lincet, 2012). Citrate is directed from TCA cycle to cytosolic anabolic processes by upregulation of citrate carrier (CIC) which transports citrate from mitochondria into cytosol. Glutamine is the second most abundant nutrient in the blood after glucose, and oncogenic mutations and epigenetic adaptations allow tumour cells to increase glutaminolysis to sustain their growth (Zhu et al., 2017).

Interstitial pressure tends to be elevated towards the centre of tumour. This affects the kinetics of large molecules such as monoclonal antibodies, used in radioimmunotherapy and imaging, since convection is a major driving force for tissue distribution of macromolecules (Baxter & Jain, 1989 and 1990; Jain, 1990).

The heterogeneity of the microenvironment of tumour cells affects the response to cancer therapy. While biopsy samples represent only a fraction of the tumour, PET/CT and PET/MR imaging allow to measure the metabolic heterogeneity of the whole tumour, although with less detail and with certain methodological challenges (Zhao et al., 2005; van Baardwijk et al., 2008; Asselin et al., 2012; Orlhac et al., 2014; Orlhac et al., 2017).

Functional imaging

Increased glucose uptake in tumours is commonly quantified or semi-quantified using [18F]FDG. If arterial or arterialized venous plasma input function is available, then either Ki or related FUR can be calculated. Glucose concentration in plasma should then be measured, too, to convert Ki or FUR to metabolic rate of glucose; however, that is not a precise estimate, since lumped constant for tumours is variable, probably even inside one tumour. Without plasma data only semi-quantitative assessments can be made, including SUV, dual-time point imaging, and tumour-to-normal tissue ratio (T/N). Liver or muscle are often used as the reference tissue, although muscle [18F]FDG uptake is known to be highly variable, and results are dependent on the definition of the liver or muscle ROI. [18F]FDG is not well suitable for imaging all tumours: neuroendocrine and prostate tumours often have low [18F]FDG uptake, while in the brain the [18F]FDG uptake is high also in the normal tissue.

Many cancer have upregulated amino acid transport and protein synthesis rate. Amino-acid uptake is quantified using labelled amino-acids and amino-acid derivatives, including [11]methionine, [18F]fluoroethyltyrosine (FET), glutamine analogues, [18F]FDOPA, and leucine derivative [18F]FACBC (fluciclovine) (Elschot et al., 2017; Jambor et al., 2017). Amino acids can be used as boron-10 carriers for BNCT.

Sarcomas and lymphomas tend to have higher perfusion than carcinomas, and larger tumours lower perfusion than smaller tumours. Perfusion in tumours can be measured using [15O]H2O, or using the initial uptake phase of several PET radiopharmaceuticals. Wadsworth et al. (2017) used 2-[18F]-fluoroethanol to measure perfusion in mouse tumour models. Tumours with increased perfusion and oxidative metabolism can be detected using [11C]acetate (Schiepers et al., 2008; Seppälä et al., 2009; Jambor et al., 2010; Strandberg et al., 2014).

[11C]choline and [18F]choline and its derivatives can be used as markers for cellular proliferation, because choline is a precursor for the biosynthesis of membrane phospholipids (Hara et al., 1997; Sutinen et al., 2004; Evangelista et al., 2015).

Cell proliferation requires DNA synthesis, and therefore radiolabelled nucleosides, such as thymidine analogue [18F]FLT can be used for tumour imaging.

Copper transporter 1 (CTR1) is overexpressed in many cancer types, and [64Cu]CuCl2 can have a role in detecting tumours and for example in staging of prostate cancer (Capasso et al., 2015).

Gene c-MYC, involved in oncogenesis, encodes transcription factor c-Myc, which activates numerous target genes, including gene for transferrin receptor 1. Transferrin receptors are often overexpressed in tumour cells because of increased demand for iron, and for example 68Ga3+-labelled transferrin can be used for detecting MYC-positive and other cancers.

Cell-based treatment strategies utilize for example T cells, and the homing of transplanted cells can be studied by in vitro labelling of the cells. [18F]FDG has been traditionally used for cell labelling, but labelling with hexadecyl-4-[18F]fluorobenzoate ([18F]HFB) may provide more accurate results of the distribution of the transplanted cells (Ma et al., 2005; Zhang et al., 2012).


Tumours are usually accompanied with inflammation, and the uptake of many PET radiopharmaceuticals is increased in inflammation. TSPO tracers may be useful in detecting inflammation in tumours, since uptake of TSPO ligands is generally not increased in tumours (Wu et al., 2014). However, TSPO is upregulated in some cancer cell lines and tumour specimens, and TSPO tracer [18F]GE-180 has shown remarkably high tumour-to-background contrast in glioblastoma (Albert et al., 2017).

Neuroendocrine tumours

Neuroendocrine tumours (NETs) are a heterogeneous group of tumours that originate from neuroendocrine cells, and are often small and can be situated almost throughout the body. Somatostatin receptor scintigraphy (SNS) is a standard procedure for the detection of NETs.

SSTR targeting

NETs typically overexpress somatostatin receptors (SSTRs). These receptors are used as targets for radiopharmaceuticals for both NET detection and therapeutics. 111In-labelled SSTR2 ligand DTPA-octreotide ([111In]pentetreotide) is routinely used SPECT radiopharmaceutical for NET detection. Several 68Ga-labelled SSTR tracers have been introduced for PET imaging (Fani et al., 2017). However, SSTR imaging is not specific to NETs; for example some lymphomas express SSTRs (Ruuska et al., 2018). Peptide receptor radionuclide therapy (PRRT) with 177Lu-carrying somatostatin analogues, such as [177Lu]dotatate (Strosberg et al., 2017), is routinely used in treatment of inoperable NETs. Kidneys and bone marrow are the dose-limiting organs for PRRT.

Dopamine system

Because of their neuroendocrine origin, NETs take up amino acids and convert them into biogenic amines (dopamine and serotonin) by decarboxylation and store the amines in vesicles. The alternative term APUDoma refers to this concept of ‘Amino Precursor Uptake and Decarboxylation’. L-DOPA is a precursor of catecholamines (dopamine, noradrenalin, adrenalin). Decarboxylation of L-DOPA into dopamine is catalyzed by the aromatic amino acid decarboxylase (AADC).

Active uptake and decarboxylation of L-DOPA leads to avid uptake of L-DOPA analogue [18F]FDOPA, which is commonly used in PET imaging of NETs (Becherer et al., 2004). Islet cells in pancreas take up L-DOPA (Borelli et al., 1997), and Ahlström et al (1995) utilized this to visualize pancreatic NETs using [11C]L-DOPA. Bergström et al (1996) demonstrated that the increased uptake of [11C]L-DOPA was due to decarboxylation. Whole body [18F]FDOPA PET was found to detect gastrointestinal carcinoid tumours, and localized the primary tumours; serotonin expressing tumours were found to be especially avid for [18F]FDOPA uptake (Hoegerle et al., 2001a). [18F]FDOPA was found to detect medullary thyroid carcinomas better than SNS or [18F]FDG (Hoegerle et al., 2001b). [18F]FDOPA PET may not be optimal for imaging of small cell lung carcinoma (SCLC), although these carcinomas express neuroendocrine markers (Jacob et al. 2003). [18F]FDOPA whole-body PET is highly sensitive and specific for detection of pheochromocytomas (Hoegerle et al., 2002). [18F]FDOPA PET of pancreas has also been shown to distinguish between focal and diffuse forms of hyperinsulinism (HI) in infancy (Ribeiro et al. 2007).

[11C]-5-hydroxytryptophan ([11C]-5-HTP) is specifically taken up by carcinoid serotonin-producing tumours, decarboxylated by AADC, and stored in vesicles as [11C]serotonin. [11C]-5-HTP provided in some cases higher SUV than [18F]FDOPA (Ahlström et al. 1995), and was shown to be more sensitive in imaging small NET lesions (Örlefors et al. 2005). Contrast of the [11C]-5-HTP uptake images could be further enhanced by concomitant administration of AADC inhibitor carbidopa. 6-[18F]Dopamine ([18F]DA) has been shown to be highly sensitive and superior to SNS (Pacak et al., 2001; Ilias et al., 2003).


Embolization is a cancer treatment procedure where small particles are injected into the artery that feeds the tumour, blocking the microvasculature and blood flow to the tumour. In (trans-arterial) radioembolization (RE, TARE), or selective internal radiation therapy (SIRT), the injected microspheres contain radioactive 90Y. Yttrium-90 undergoes β- decay, which travel only short distances in the tissue, providing localized radiotherapy, saving the surrounding tissue. Radioembolization is especially used for treatment of liver cancers; tumour in the liver usually gets most of their blood supply from the arteries, while normal liver tissue get most of the blood supply from the portal vein (Gulec, 2016). Tumour vasculature is immature, devoid of well-developed smooth muscle and innervation, and therefore vasoconstrictors, such as angiotensin-II adrenaline, and noradrenaline, can be used to further reduce the blood flow and effect of RE on the healthy tissue (van den Hoven et al., 2014).

Small fraction of 90Y decays produces high-energy photons and positron-electron pairs, which can be detected with bremsstrahlung SPECT and PET, respectively. PET provides better resolution, and can be used to analyse the absorbed dose to the tumour in detail. Usual PET imaging agents, such as [18F]FDG and hypoxia tracers can also be used after embolization to assess the treatment effect.

TNM classification

Union Internationale Contre le Cancer (UICC) and American Joint Committee on Cancer have published and updated TNM classification of malignant tumours, which is widely used standard for cancer staging. UICC TNM classification is an anatomically-based system with three categories: T category describes the primary tumour site, N category describes regional lymph node involvement, and M category describes the metastatic spread.

Eiber et al (2018) have proposed a molecular imaging TNM system (miTNM) as a standardized reporting framework for PSMA-ligand PET/CT and PET/MRI in prostate cancer.

Werner et al (2018) have proposed a molecular imaging reporting and data systems (MI-RADS) framework for targeted radiotracers with theranostic implications. Their work included RADS classifications for PSMA- and SSTR-targeted PET imaging.

See also:


Alam IS, Arshad MA, Nguyen Q-D, Aboagye EO. Radiopharmaceuticals as probes to characterize tumour tissue. Eur J Nucl Med Mol Imaging 2015; 42: 537-561. doi: 10.1007/s00259-014-2984-3.

Barwick T, Rockall A (eds.): PET/CT in Gynecological Cancers. Springer, 2016. doi: 10.1007/978-3-319-29249-6.

Bernsen MR, Kooiman K, Segbers M, van Leeuwen FWB, de Jong M. Biomarkers in preclinical cancer imaging. Eur J Nucl Med Mol Imaging 2015; 42: 579-596. doi: 10.1007/s00259-014-2980-7.

Burvenich IJG, Parakh S, Parslow AC, Lee ST, Gan HK, Scott AM. Receptor occupancy imaging studies in oncology drug development. AAPS J. 2018; 20: 43. doi: 10.1208/s12248-018-0203-z.

Chua S (ed.): PET/CT in Radiotherapy Planning. Springer, 2017. doi: 10.1007/978-3-319-54744-2.

Cook G (ed.): PET/CT in Prostate Cancer. Springer, 2017. doi: 10.1007/978-3-319-57624-4.

Farwell MD, Pryma DA, Mankoff DA. PET/CT imaging in cancer: current applications and future directions. Cancer 2014; 120: 3433-3445.

Gewirtz DA, Holt SE, Grant S (eds.): Apoptosis, Senescence, and Cancer. Human Press, 2007.

Hofman MS, Hicks RJ (eds.): PET/CT in Melanoma. Springer, 2017. doi: 10.1007/978-3-319-54741-1.

Icard P, Lincet H. A global view of the biochemical pathways involved in the regulation of the metabolism of cancer cells. Biochim Biophys Acta 2012; 1826: 423-433.

Jain RK. Determinants of tumor blood flow: a review. Cancer Res. 1988; 48: 2641-2658.

Jennings M, Marcu LG, Bezak E. PET-specific parameters and radiotracers in theoretical tumour modelling. Comput Math Methods Med. 2015; 415923.

Kanyani I (ed.): PET/CT in Hodgkin’s Lymphoma. Springer, 2017. doi: 10.1007/978-3-319-57225-3.

Keenan MM, Chi J-T. Alternative fuels for cancer cells. Cancer J. 2015; 21: 49-55.

Knap WH, Vyska K (eds.): Current Topics in Tumor Cell Physiology and Positron-Emission Tomography. Springer, 1984. doi: 10.1007/978-3-662-02393-8.

Komar G. Imaging of tumour microenvironment for the planning of oncological therapies using positron emission tomography. Annales Universitatis Turkuensis, D1051, 2013.

Koumenis C, Hammond E, Giaccia A (eds.): Tumor Microenvironment and Cellular Stress - Signaling, Metabolism, Imaging, and Therapeutic Targets. Springer, 2014. doi: 10.1007/978-1-4614-5915-6.

Kubota K. From tumor biology to clinical PET: a review of positron emission tomography (PET) in oncology. Ann Nucl Med. 2001; 15(6): 471-486.

Kötz B, West C, Saleem A, Jones T, Price P. Blood flow and Vd (water): both biomarkers required for interpreting the effects of vascular targeting agents on tumor and normal tissue. Mol Cancer Ther. 2009; 8(2): 303-309.

Le A (ed.): The Heterogeneity of Cancer Metabolism. Springer, 2018. ISBN 978-3-319-77736-8.

Lewis DY, Soloviev D, Brindle KM. Imaging tumor metabolism using positron emission tomography. Cancer J. 2015; 21(2): 129-136.

Lindholm P, Sutinen E, Oikonen V, Mattila K, Tarkkanen M, Kallajoki M, Aro H, Böhling T, Kivioja A, Elomaa I, Minn H. PET imaging of blood flow and glucose metabolism in localized musculoskeletal tumors of the extremities. Nucl Med Biol. 2011; 38: 295-300.

Mankoff DA, Bellon JR. Positron-emission tomographic imaging of cancer: glucose metabolism and beyond. Semin Radiat Oncol. 2001; 11(1): 16-27.

Mann A, Semenenko I, Meir M, Eyal S. Molecular imaging of membrane transporters’ activity in cancer: a picture is worth a thousand tubes. AAPS J. 2015; 17(4): 788-801.

Mayers JR, Vander Heiden MG. Famine versus feast: understanding the metabolism of tumors in vivo. Trends Biochem Sci. 2015; 40(3): 130-140.

Mazurek S, Shoshan M (eds.): Tumor Cell Metabolism - Pathways, Regulation and Biology. Springer, 2015.

Mor G, Alvero AB (eds.): Apoptosis and Cancer, 2nd ed. Humana Press, 2015. doi: 10.1007/978-1-4939-1661-0.

Muzi M, O’Sullivan F, Mankoff DA, Doot RK, Pierce LA, Kurland BF, Linden HM, Kinahan PE. Quantitative assessment of dynamic PET imaging data in cancer imaging. Magn Reson Imaging 2012; 30(9): 1203-1215.

Ribatti D: Morphofunctional Aspects of Tumor Microcirculation. Springer, 2012. doi: 10.1007/978-94-007-4936-8.

Rieger H, Fredrich T, Welter M. Physics of the tumor vasculature: theory and experiment. Eur Phys J Plus 2016; 31.

Silverman P (ed): Oncologic Imaging: A Multidisciplinary Approach. Elsevier, 2012.

Tee S-S, Keshari KR. Novel approaches to imaging tumor metabolism. Cancer J. 2015; 21: 165-173.

Vaupel P, Kallinowski F, Okunieff P. Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review. Cancer Res. 1989; 49: 6449-6465.

Wong WL (ed.): PET/CT in Head and neck Cancer. Springer, 2018. ISBN 978-3-319-61440-3. doi: 10.1007/978-3-319-61440-3.

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Updated at: 2019-04-17
Created at: 2016-03-18
Written by: Vesa Oikonen