- Original research
- Open Access
Impact of tissue transport on PET hypoxia quantification in pancreatic tumours
- Edward Taylor^{1, 2}Email authorView ORCID ID profile,
- Jennifer Gottwald^{1, 3},
- Ivan Yeung^{1, 4},
- Harald Keller^{1, 4},
- Michael Milosevic^{1, 4},
- Neesha C. Dhani^{1, 5},
- Iram Siddiqui^{6},
- David W. Hedley^{1, 3, 5} and
- David A. Jaffray^{1, 2, 3, 4, 7}
- Received: 4 October 2017
- Accepted: 28 November 2017
- Published: 22 December 2017
Abstract
Background
The clinical impact of hypoxia in solid tumours is indisputable and yet questions about the sensitivity of hypoxia-PET imaging have impeded its uptake into routine clinical practice. Notably, the binding rate of hypoxia-sensitive PET tracers is slow, comparable to the rate of diffusive equilibration in some tissue types, including mucinous and necrotic tissue. This means that tracer uptake on the scale of a PET imaging voxel—large enough to include such tissue and hypoxic cells—can be as much determined by tissue transport properties as it is by hypoxia. Dynamic PET imaging of 20 patients with pancreatic ductal adenocarcinoma was used to assess the impact of transport on surrogate metrics of hypoxia: the tumour-to-blood ratio [TBR(t)] at time t post-tracer injection and the trapping rate k _{3} inferred from a two-tissue compartment model. Transport quantities obtained from this model included the vascular influx and efflux rate coefficients, k _{1} and k _{2}, and the distribution volume v _{ d }≡k _{1}/(k _{2}+k _{3}).
Results
Correlations between voxel- and whole tumour-scale k _{3} and TBR values were weak to modest: the population average of the Pearson correlation coefficients (r) between voxel-scale k _{3} and TBR (1 h) [TBR(2 h)] values was 0.10 [0.01] in the 20 patients, while the correlation between tumour-scale k _{3} and TBR(2 h) values was 0.58. Using Patlak’s formula to correct uptake for the distribution volume, correlations became strong (r=0.80[0.52] and r=0.93, respectively). The distribution volume was substantially below unity for a large fraction of tumours studied, with v _{ d } ranging from 0.68 to 1 (population average, 0.85). Surprisingly, k _{3} values were strongly correlated with v _{ d } in all patients. A model was proposed to explain this in which k _{3} is a combination of the hypoxia-sensitive tracer binding rate k _{b} and the rate k _{eq} of equilibration in slow-equilibrating regions occupying a volume fraction 1−v _{ d } of the imaged tissue. This model was used to calculate the proposed hypoxia surrogate marker k _{b}.
Conclusions
Hypoxia-sensitive PET tracers are slow to reach diffusive equilibrium in a substantial fraction of pancreatic tumours, confounding quantification of hypoxia using both static (TBR) and dynamic (k _{3}) PET imaging. TBR is reduced by distribution volume effects and k _{3} is enhanced by slow equilibration. We proposed a novel model to quantify tissue transport properties and hypoxia-sensitive tracer binding in order to improve the sensitivity of hypoxia-PET imaging.
Keywords
- Positron emission tomography
- Hypoxia imaging
- PET tracer kinetic modelling
Background
Positron emission tomography imaging of hypoxia is a promising way to detect hypoxia non-invasively in solid tumours [1, 2]. A major challenge to this approach is that the binding rate of hypoxia-sensitive PET tracers such as fluoromisonidazole (FMISO) and fluoroazomycinarabinoside (FAZA) is slow as compared to, e.g., flurodeoxyglucose (FDG), and can be comparable to diffusive equilibration rates in tumour tissues.
where D is the diffusivity of the tracer. For FAZA and similarly sized molecules (on the order of several hundred Daltons), D∼10 μm^{2}/s in most tissue [6, 7]. Hence, taking l∼100 μm to be the distance between capillaries, the equilibration rate k _{eq}∼20 h^{−1} for tracer is typically much faster than the binding rate, and comparable to the rate of extravasation, k _{1}.
On the other hand, for tissue with substantial mucous deposits (common in carcinomas [8] such as pancreatic ductal adenocarcinoma [9]), where diffusivity can be slowed by two or more orders of magnitude [10, 11], the rate of equilibration slows drastically, becoming comparable to the binding rate. This can also happen in tissue with necrotic regions (\(l\gtrsim 500\;\mu \mathrm {m}\)) interspersed with hypoxic cells.
Slow diffusive equilibration has two important consequences for quantifying tumour hypoxia based on tracer uptake. First, if an imaging voxel contains both hypoxic cells and either mucous or small necroses, the voxel-scale TBR value will be reduced by the fact that tracer does not reach diffusive equilibrium at the standard imaging time, between 2 and 3 h post-injection. Hence, the sensitivity of static PET imaging to hypoxia is diminished. Second, as tracer slowly equilibrates in mucinous and necrotic tissue, its concentration increases at a rate comparable to that due to hypoxia-induced binding and a compartment model [12–15] may not be able to distinguish the two processes. In this case, we hypothesize that the trapping rate k _{3} represents a sum of the binding rate k _{b} and the rate of equilibration. Quantifying hypoxia based on k _{3} will thus overestimate its extent since k _{3}≥k _{b}.
In this paper, we seek to test these hypotheses by modeling the pharmamcokinetics of FAZA in 20 patients with pancreatic ductal adenocarcinoma (PDAC), applying basic principles of diffusive equilibration to interpret transport data calculated from a standard two-tissue compartment model.
Methods
Patient population and PET/CT scans
Data was taken from 20 patients with biopsy-confirmed pancreatic ductal adenocarcinoma and FAZA-PET scans. Dynamic PET imaging scans were acquired over 1 h following injection of FAZA. The 1-h time-activity curves (TAC _{1}) were each binned into 34 frames: 12 10-s frames, followed by 8 32-s frames, followed by 7 2-min frames, followed by 7 5-min frames. Patients returned for a static PET scan at 2 h. CT scans used for co-registration were taken at the beginning of the dynamic and static PET scans. Further details of this patient cohort and the PET/CT scans have been described previously [16].
Region of interest contours
PET activity data was obtained for regions of interest (ROIs) contoured using co-registered CT images. Tumour ROIs were contoured by a radiologist using the CT scan at 2 h. This was co-registered manually to the initial CT scan and the two CT ROI sets were co-registered to the dynamic and static PET scans. In order to minimize effects resulting from high liver uptake of FAZA, aorta ROIs were contoured from the same range of PET/CT slices (along the cranial-caudal axis) as the tumour ROIs. At the level of the pancreas, the aorta is between 1.5 and 2 cm in diameter; to minimize partial volume effects, ROIs in the aorta were restricted to 0.75 cm in diameter and combined so that at least 25 PET voxels (3.9 ×3.9×3.3 mm ^{3} each) were imaged.
Compartment model analysis
where v _{ b } is the volume fraction occupied by blood in the region of interest.
where C _{model}(t _{ i }) are the model activity values [Eqs. (3)–(5)] and C _{data}(t _{ i }) are the measured values acquired during the N discrete time frames; N=34 for TAC _{1} and N=35 for TAC _{2}. To avoid over-weighting short-duration early time frames, we used the weighting function w _{ i }=δ t _{ i } in Eq. 6, where δ t _{ i } was the duration of the ith time frame (because the t=2 h time-point in TAC _{2} did not represent a true 1-h time bin beyond the TAC _{1} data set, we used δ t _{35}=δ t _{34}=5 min). Equation 6 was minimized in Wolfram Mathematica 11.1 using its built-in numerical minimization routine (NMinimize) with C _{model}(t _{ i }) calculated using trapezoidal integration.
In Eq. (8), K _{ i }≡k _{3} v _{ d } is sometimes referred to as the “net trapping rate”. TBR _{corrected} represents the theoretical tumour-to-blood ratio that would have arisen had the distribution volume been unity.
at both t=1 and 2 h. Pearson correlation coefficients were calculated to quantify correlations between voxel- and tumour-scale values of these quantities. Voxel-scale coefficients were calculated by fitting the above model to the individual TACs for each voxel, while tumour-scale values were obtained using the average TAC in each tumour. Correlations were reported as the population average (over twenty tumours) of the intra-tumour voxel-scale r values (“voxel-scale”) and as correlations between tumour-scale values (“tumour-scale”).
Results
Correlations between TBR and k _{3}
Top: Correlation matrix of Pearson correlation coefficients between the mean voxel-scale parameters across the twenty tumours studied using the 2-h data sets. Bottom: Population average values of the corresponding voxel-scale coefficients. Standard deviations of mean values across patients are indicated in parentheses
k _{3}[h^{−1}] | v _{ d } | TBR | TBR _{corrected} | |
---|---|---|---|---|
k _{3} | − | −0.59 | 0.01 | 0.52 |
v _{ d } | −0.59 | − | 0.35 | −0.58 |
TBR | 0.01 | 0.35 | − | 0.50 |
TBR _{corrected} | 0.52 | −0.58 | 0.50 | − |
k _{3} [h ^{−1}] | v _{ d } | TBR | TBR_{corrected} | |
0.30 (0.20) | 0.85 (0.10) | 1.06 (0.13) | 1.25 (0.20) |
Correlation matrix of Pearson correlation coefficients between the tumour-scale parameters across the twenty tumours studied using the 2-h data sets
k _{3} | v _{ d } | TBR | TBR _{corrected} | |
---|---|---|---|---|
k _{3} | − | −0.34 | 0.58 | 0.93 |
v _{ d } | −0.34 | − | 0.30 | −0.26 |
TBR | 0.58 | −0.26 | − | 0.66 |
TBR _{corrected} | 0.93 | −0.26 | 0.66 | − |
Relationship between v _{ d } and k _{3}
Here, v _{ s } represents the voxel volume fraction in which tracer is slow to equilibrate. As noted in the Introduction, tracer will equilibrate slowly in mucinous and necrotic tissue owing to the slow diffusivity and long diffusive distances, respectively.
The factors of 1−v _{ s } and v _{ s } here ensure detailed balance amongst the compartments. k _{b} is the binding rate due to hypoxia and k _{eq} represents the equilibration rate in the regions of slow-equilibration. Recall from the Introduction that we expect it to be on the order of (0.1→1) h ^{−1} when equilibration is driven by diffusion; see Eq. (2). In writing Eq. (14), it has been assumed that tracer does not bind inside regions of slow-equilibration since, e.g., necrotic cells and extracellular mucous deposits do not bind hypoxia-PET nitroimidazole tracers [12].
In arriving at this result, we have neglected back-flux from the slow-diffusion region, dropping the contribution arising from \(C^{(s)}_{d}\) in Eq. (13). This is valid as long as \(t\lesssim k_{\text {eq}}^{-1}\).
Equations (16) and (17) are our main theoretical results. They show that the distribution volume v _{ d } defined in Eq. (7) is the volume fraction of tissue in which tracer rapidly equilibrates and that the standard two-tissue compartment model trapping rate in general represents the sum of the rate of binding due to hypoxia and the equilibration rate. In turn, this means that it is not possible to distinguish binding from equilibration from just the shape of the time-activity curves.
where N _{ b } is the total number of values within each bin, σ _{ X } and X denote the standard deviation and mean values of X=k _{b} or k _{eq}. Assuming that the relative variance \(\left (\left.\sigma _{k_{\mathrm {b}}}\right /k_{\mathrm {b}}\right)\) is equal to that for the oxygen partial pressure \(P_{O_{2}}\) (the case, e.g., when the two are related by a Michaelis-Menten-type relation [12]), the variance in k _{b} is expected to be large, based on the broad distribution of \(P_{O_{2}}\) levels in tumours: \(\left (\left.\sigma _{P_{O_{2}}}\right /P_{O_{2}}\right)\gtrsim 1\) [23]. In contrast, the relative variance in k _{eq}—reflecting that of the size l of the regions in which tracer is slow to equilibrate—is small. This was estimated by calculating the variance in the minimum k _{3} value in each bin with respect to a v _{ d }-dependent average (see, e.g., the curve fits in Fig. 2). Across our twenty patients, we found an average value \(\left (\left.\sigma _{k_{\text {eq}}}\right /k_{\text {eq}}\right)\sim 0.4\). As a compromise to having a sufficient number of voxels to ensure the validity of statistics and few enough to have sufficient resolution in v _{ d }-space to carry out these curve fits, bins were chosen to contain ten voxels. Hence, we chose M=0.4×10=4. A sensitivity analysis of the predicted equilibration rates and the choice of M is presented in Online Resource 2 (Additional file 2).
Top: Correlation matrix of Pearson correlation coefficients between the mean voxel-scale parameters across the twenty tumours studied using the 2-h data sets. Bottom: Population-averages of the corresponding voxel-scale rate coefficients; values are shown in units of h ^{−1}. Standard deviations of mean values across patients are indicated in parentheses. Also shown is the population average k _{eq} value, which was calculated from fits to data from all voxels in each tumour, as described in the text
k _{3} | K _{eq} | k _{b} | v _{ d } | |
---|---|---|---|---|
k _{3} | − | 0.57 | 0.86 | −0.59 |
K _{eq} | 0.57 | − | 0.18 | −0.73 |
k _{b} | 0.86 | 0.18 | − | −0.27 |
v _{ d } | −0.59 | −0.73 | −0.27 | − |
k _{3} | K _{eq} | k _{b} | k _{eq} | |
0.30 (0.20) | 0.17 (0.15) | 0.14 (0.08) | 0.44 (0.29) |
The v _{ d }-dependence of k _{3} in our model is a consequence only of mass conservation and the assumption that there exists a compartment in which tracer is slow to reach diffusive equilibrium. It does not depend on a specific microscopic model for equilibration. We tested the prediction given by Eq. (17) by fitting the binned K _{eq} values to a function of the form K _{eq}(v _{ d },γ)=k _{eq}[(1−v _{ d })/v _{ d }]^{ γ } to determine how close γ was to its predicted value of unity. Averaging over all tumours, we found γ=(0.9±0.4), with the error given by the standard deviation of values across all tumours. This confirms that our model in which tracer equilibrates slowly in a fraction 1−v _{ d } of tissue is consistent with our data. The mean equilibration rate derived from these fits was k _{eq}=0.44 h ^{−1} (standard deviation of 0.29 h ^{−1} across all patients), corresponding to an equilibration time of 1/k _{eq}∼2.3 h.
Discussion
It is well-appreciated that the uptake of hypoxia-sensitive PET tracers is dependent on tissue transport properties as well as hypoxia [13, 14, 17, 18, 25]. In principle, dynamic PET modeling corrects for transport properties such as slow tissue diffusivity that can impede the uptake of tracer and reduce sensitivity to hypoxia when such features are co-localized with hypoxia in PET voxels. This is especially problematic since PET voxels are typically large enough [ ∼(4 mm)^{3}] to include diverse cell populations, with widely varying pathology [26]. The quantity of primary interest in a compartment model analysis of dynamic PET imaging is the trapping rate k _{3}, commonly believed to be sensitive to hypoxia via the underlying binding kinetics [12–14]. Static PET imaging is more feasible clinically, however, and it is often assumed that one can adopt static imaging in place of kinetic imaging when some appropriate uptake metric–SUV for FDG-PET or TBR for hypoxia-PET–is well-correlated with k _{3} [27, 28].
In this paper, we have investigated dynamic and static PET in 20 patients with pancreatic adenocarcinoma (PDAC) and found k _{3} values to be only modestly correlated with TBR. Using Patlak’s formula to analyze these correlations, we found that a highly variable distribution volume across patients was primarily responsible for the reduced correlations, consistent with recent findings of FMISO kinetics in head and neck tumours [25].
At first glance, this would suggest that these tumours would benefit from dynamic PET imaging. The trapping rate was found to exhibit a strong dependence on the distribution volume, however, implying that k _{3} describes both the binding rate due to hypoxia as well as the rate of equilibration. A model was developed to explain this in which the extravascular tissue space was divided into two regions, one in which tracer rapidly achieved diffusive equilibration and one in which it equilibrated slowly. The population-averaged equilibration rate k _{eq}≃(0.44±0.29) h ^{−1} in the latter region is consistent with our estimate in the Introduction of having either mucinous regions (on the order of tens to hundreds of microns in extent) where diffusivity is greatly slowed or micronecroses, smaller than a PET imaging voxel but larger than ∼ 500 μm across.
The long equilibration time [1/k _{eq}∼2.3 h] implied by this result means that unbound tracer will not equilibrate until well-after tracer injection, at times t≫1/k _{eq}. At this time, the concentration of tracer in both the slow- and fast-equilibrating regions will approach that in blood and the effect of the distribution volume on TBR will vanish. Ideally, static hypoxia-PET imaging would be carried out when t≫1/k _{eq} in order to remove this sensitivity to transport. Unfortunately, the half-life of ^{18}F is short and imaging times are typically restricted to be 3 h or less. (In our study, it was felt that accrual would be challenged by imaging patients past 2 h.)
Our conclusion that equilibration is slow in parts of pancreatic tumours is not inconsistent with claims by us [21] and others [25] that tumour-scale equilibration rates are rapid. The characteristic equilibration rate in the fast-equilibrating regions can be approximated by k _{1} which, even for the hypo-perfused PDAC tumours studied in this work, was fast compared to k _{b} and k _{eq}. The population average of the tumour-scale k _{1} values was ∼ 0.3 min ^{−1} [16]. Regions of slow-equilibration occupy a relatively small fraction of the tumours and hence, the tumour-scale equilibration rate is not strongly affected by these.
Although we have proposed a scheme to differentiate binding from equilibration, and hence, to quantify hypoxic status via the surrogate binding rate k _{b}, the accuracy of this approach relies on the assumption that the variance in the equilibration rate is much smaller than the variance in the binding rate: \(\left (\left.\sigma _{k_{\text {eq}}}\right /k_{\text {eq}}\right)\ll \left (\left.\sigma _{k_{\mathrm {b}}}\right /k_{\mathrm {b}}\right)\). Only then can we attribute the lowest few k _{3} values in each v _{ d } bin to K _{eq} and not k _{b}.
The fact that the estimated \(\left (\left.\sigma _{k_{\text {eq}}}\right /k_{\text {eq}}\right)\) was only marginally smaller than \(\left (\left.\sigma _{k_{\mathrm {b}}}\right /k_{\mathrm {b}}\right)\) means that our analysis did not completely distinguish equilibration and binding. In effectively assuming that the variance in the equilibration rate was zero, our analysis erred on the side of underestimating the equilibration rate and hence, overestimated the binding rate k _{b}. At the same time, our scheme still represents an improvement over hypoxia quantification using k _{3} since k _{3} will always be larger than our estimated k _{b}, which in turn is likely larger than the true k _{b}. Full validation of our approach will rely on comparing our estimates of k _{b} and oxygen levels using other methods such as immunohistochemical staining of resected tumours. We plan on doing this in the future.
Beyond hypoxia quantification, dynamic PET imaging reveals additional information about tumour physiology that may prove to be clinically important [13, 14, 25, 29]. In our case, we have found that the distribution volume of FAZA (and likely all freely-diffusible PET tracers) quantifies the amount of mucous present in pancreatic tumours. Over-expression of the mucous gel-forming mucin MUC5AC in PDAC is prognostic for shorter survival time [30], greater metastatic potential [9, 31], and immune system avoidance [32]. We hypothesize that the distribution volume in other tumour sites will likewise provide complementary physiological information beyond hypoxic status.
A key question raised by this work is whether or not the tissue transport effects identified here confound hypoxia quantification using other hypoxia-PET tracers such as FMISO and in other tumour sites. The primary impediment to tracer equilibration is slow diffusivity. FAZA has been estimated to diffuse marginally faster than FMISO [7], and so the issues identified here should impact FMISO to a comparable degree. Indeed, similar effects as the ones reported here have arisen in FMISO imaging of pre-clinical tumour models [33], as well as clinical pharmacokinetic studies of head and neck tumours [17, 25]. In all cases, a variable distribution volume diminished correlations between TBR and k _{3}. [The fact that K _{ i }=v _{ d } k _{3} but not k _{3} was found to be well-correlated with TBR in Ref. [33] can be understood from Eq. (8): K _{ i } removes the variance in TBR arising from v _{ d } in the trapping term, but not the first two terms on the right-hand side of this equation.] In recent work, Grkovski et al. discuss the important role of the distribution volume in static PET hypoxia quantification and also report significant negative correlations between k _{3} and v _{ d } [25]. The present work builds on these analyses by proposing a model in which k _{3} is sensitive both to hypoxia-induced binding as well as diffusive equilibration of un-bound tracer.
Conclusions
The uptake of hypoxia-sensitive PET tracers in pancreatic tumours depends in a significant way on both tissue transport properties as well as the presence of hypoxia. Both dynamic- and static-PET based hypoxia surrogates— k _{3} and TBR—are affected by regions where diffusive equilibrium is achieved very slowly, over several hours. We have proposed a scheme to extract the hypoxia-sensitive tracer binding rate as well as the from dynamic PET data and proposed this as a novel hypoxia biomarker. Our results are of relevance for all hypoxia-PET tracers and any tumour site where transport of small-molecular weight agents is challenged.
Declarations
Acknowledgements
The authors thank Caryn Geady for assistance with some of the figures and Douglass Vines, Brandon Driscoll, and Tina Shek for useful discussions.
Funding
This work was funded by a Terry Fox New Frontiers Program Grant, the Quantitative Imaging Network, Canadian Institutes for Health Research, and the Orey and Mary Fidani family chair in radiation physics.
Authors’ contributions
ET and JG carried out the compartment model analysis. ET, IY, HK, and MM developed the model used to analyze data. NCD, DWH, and DAJ participated in the design of the study. IS carried out the histology analysis. All authors read and approved the final manuscript.
Ethics approval and consent to participate
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study protocol was approved by the University Health Network Research Ethics Board and a signed written informed consent was obtained from all individual participants included in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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References
- Fleming IN, Manavaki R, Blower PJ, West C, Williams KJ, Harris AL, Domarkas J, Lord S, Baldry C, Gilbert FJ. Imaging tumour hypoxia with positron emission tomography. Brit J Cancer. 2015; 112:238–50.View ArticlePubMedGoogle Scholar
- Rajendran JG, Krohn KA. F-18 fluoromisonidazole for imaging tumor hypoxia: imaging the microenvironment for personalized cancer therapy. Semin Nucl Med. 2015; 45(2):151–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Koh WJ, Rasey JS, Evans ML, Grierson JR, Lewellen TK, Graham MM, Krohn KA, Griffin TW. Imaging of hypoxia in human tumors with [F-18] fluoromisonidazole. Int J Radiat Oncol Biol Phys. 1992; 22(2):199–212.View ArticlePubMedGoogle Scholar
- Rajendran JG, Schwartz DL, O’Sullivan J, Peterson LM, Ng P, Scharnhorst J, Grierson JR, Krohn KA. Tumor hypoxia imaging with [F-18] fluoromisonidazole positron emission tomography in head and neck cancer. Clin Cancer Res. 2006; 12(18):5435–41.View ArticlePubMedPubMed CentralGoogle Scholar
- Muzi M, Peterson LM, O’Sullivan JN, Fink JR, Rajendran JG, McLaughlin LJ, Muzi JP, Mankoff DA, Krohn KA. 18F-Fluoromisonidazole Quantification of Hypoxia in Human Cancer Patients Using Image-Derived Blood Surrogate Tissue Reference Regions. J Nucl Med. 2015; 56(8):1223–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Pruijn FB, Patel K, Hay MP, Wilson WR, Hicks KO. Prediction of tumour tissue diffusion coefficients of hypoxia-activated prodrugs from physicochemical parameters. Aust J Chem. 2008; 61:687–93.View ArticleGoogle Scholar
- Wack LJ, Mönnich D, van Elmpt W, Zegers CML, Troost EGC, Zips D, Thorwath D. Comparison of [18F]-FMISO, [18F]-FAZA, and [18F]-HX4 for PET imaging of hypoxia—a simulation study. Acta Oncologica. 2015; 54:1370–7.View ArticlePubMedGoogle Scholar
- Lau SK, Weiss LM, Chu PG. Differential expression of MUC1, MUC2, and MUC5AC in carcinomas of various sites: an immunohistochemical study. Am J Clin Pathol. 2004; 122(1):61–9.View ArticlePubMedGoogle Scholar
- Kaur S, Kumar S, Momi N, Sasson AR, Batra SK. Mucins in pancreatic cancer and its microenvironment. Nat Rev Gastroenterol Hepatol. 2013; 10(10):607–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Georgiades P, Pudney PD, Thornton DJ, Waigh TA. Particle tracking microrheology of purified gastrointestinal mucins. Biopolymers. 2014; 101(4):366–77.View ArticlePubMedGoogle Scholar
- Runnsjö A, Dabkowska AP, Sparr E, Kocherbitov V, Arnebrant T, Engblom J. Diffusion through Pig Gastric Mucin: Effect of Relative Humidity. PLoS ONE. 2016; 11(6):e0157596.View ArticlePubMedPubMed CentralGoogle Scholar
- Casciari JJ, Graham MM, Rasey JS. A modeling approach for quantifying tumor hypoxia with [F-18]fluoromisonidazole PET time-activity data. Med Phys. 1995; 22:1127–39.View ArticlePubMedGoogle Scholar
- Thorwarth D, Eschmann SM, Paulsen F, Alber M. A kinetic model for dynamic [ ^{18}F]-Fmiso PET data to analyse tumour hypoxia. Phys Med Biol. 2005; 50:2209–24.View ArticlePubMedGoogle Scholar
- Thorwarth D, Eschmann SM, Scheiderbauer J, Paulsen F, Alber M. Kinetic analysis of dynamic ^{18}F-fluoromisonidazole PET correlates with radiation treatment outcome in head-and-neck cancer. BMC Cancer. 2005; 5:152.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang W, Georgi J-C, Nehmeh SA, Narayanan M, Paulus T, Bal M, O’Donoghue J, Zanzonico PB, Schmidtlein CR, Lee NY, Humm JL. Evaluation of a compartmental model for estimating tumor hypoxia via FMISO dynamic PET imaging. Phys Med Biol. 2009; 54:3083–99.View ArticlePubMedPubMed CentralGoogle Scholar
- Metran-Nascente C, Yeung I, Vines DC, Metser U, Dhani DC, Green D, Milosevic M, Jaffray D, Hedley DW. Measurement of tumor hypoxia in patients with advanced pancreatic cancer based on ^{18}F-fluoroazomyin arabinoside uptake. J Nucl Med. 2016; 57(3):361–6.View ArticlePubMedGoogle Scholar
- Wang W, Lee NY, Georgi J-C, Narayanan M, Guillem J, Schöder H, Humm JL. Pharmacokinetic Analysis of Hypoxia ^{18}F-Fluoromisonidazole Dynamic PET in Head and Neck Cancer. J Nucl Med. 2010; 51(1):37–45.View ArticlePubMedGoogle Scholar
- Bartlett RM, Beattie BJ, Naryanan M, Georgi J-C, Chen Q, Carlin SD, Roble G, Zanzonico PB, Gonen M, O’Donoghue J, Fischer A, Humm JL. Image-Guided PO2 Probe Measurements Correlated with Parametric Images Derived from ^{18}F-Fluoromisonidazole Small-Animal PET Data in Rats. J Nucl Med. 2012; 53(10):1608–15.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang K, Georgi J-C, Zanzonico P, Narayanan M, Paulus T, Bal M, Wang W, Cai A, O’ Donoghue J, Ling CC, Humm JL. Hypoxia Imaging of Rodent Xenografts with ^{18}F-Fluoromisonidazole: Comparison of Dynamic and Static PET Imaging. Int J Med Physics Clin Eng Radiat Oncol. 2012; 1(3):95–104.View ArticleGoogle Scholar
- Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983; 3(1):1–7.View ArticlePubMedGoogle Scholar
- Taylor E, Yeung I, Keller H, Wouters BG, Milosevic M, Hedley DW, Jaffray DW. Quantifying hypoxia in human cancers using static PET imaging. Phys Med Biol. 2016; 61:7957.View ArticlePubMedGoogle Scholar
- Larson KB, Markham J, Raichle ME. Tracer-kinetic models for measuring cerebral blood flow using externally detected radiotracers. J Cereb Blood Flow Metab. 1987; 7(4):443–63.View ArticlePubMedGoogle Scholar
- Nordsmark M, Bentzen SM, Overgaard J. Measurement of human tumour oxygenation status by a polarographic needle electrode. An analysis of inter- and intratumour heterogeneity. Acta Oncol. 1994; 33(4):383–9.View ArticlePubMedGoogle Scholar
- CD Lorenz, RM Ziff. Precise determination of the critical percolation threshold for the three-dimensional “Swiss cheese” model using a growth algorithm. J Chem Phys. 2011; 114(8):3659–61.View ArticleGoogle Scholar
- Grkovski M, Schöder H, Lee NY, Carlin SD, Beattie BT, Riaz N, Leeman JE, O’Donoghue JA, Humm JL. Multiparametric Imaging of Tumor Hypoxia and Perfusion with ^{18}F-Fluoromisonidazole Dynamic PET in Head and Neck Cancer. J Nucl Med. 2017; 58:1072–80.View ArticlePubMedGoogle Scholar
- Busk M, Horsman MR, Overgaard J. Resolution in PET hypoxia imaging: Voxel size matters. Acta Oncologica. 2008; 47(7):1201–10.View ArticlePubMedGoogle Scholar
- Freedman NM, Sundaram SK, Kurdziel K, Carrasquillo JA, Whatley M, Carson JM, Sellers D, Libutti SK, Yang JC, Bacharach SL. Comparison of SUV and Patlak slope for monitoring of cancer therapy using serial PET scans. Eur J Nucl Med Mol Imaging. 2003; 30(1):46–53.View ArticlePubMedGoogle Scholar
- Doot RK, Dunnwald LK, Schubert EK, Muzi M, Peterson LM, Kinahan PE, Kurland BF, Mankoff DA. Dynamic and static approaches to quantifying ^{18}F-FDG uptake for measuring cancer response to therapy, including the effect of granulocyte CSF. J Nucl Med. 2007; 48(6):920–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Grkovski M, Lee NY, Schöder H, Carlin SD, Beattie BT, Riaz N, Leeman JE, O’Donoghue JA, Humm JL. Monitoring early response to chemoradiotherapy with ^{18}F-FMISO dynamic PET in head and neck cancer. Eur J Nucl Med Mol Imaging. 2017; 44(10):1682–91.View ArticlePubMedGoogle Scholar
- Takikita M, et al. Associations between Selected Biomarkers and Prognosis in a Population-Based Pancreatic Cancer Tissue Microarray. Cancer Res. 2009; 69(7):2950–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Yamazoe S, Tanaka H, Sawada T, Amano R, Yamada N, Ohira M, Hirakawa K. RNA interference suppression of mucin 5AC (MUC5AC) reduces the adhesive and invasive capacity of human pancreatic cancer cells. J Exp Clin Cancer Res. 2010; 29:53.View ArticlePubMedPubMed CentralGoogle Scholar
- Hoshi H, Sawada T, Uchida M, Saito H, Iijima H, Toda-Agetsuma M, Wada T, Yamazoe S, Tanaka H, Kimura K, Kakehashi A, Wei M, Hirakawa K, Wanibuchi H. Tumor-associated MUC5AC stimulates in vivo tumorigenicity of human pancreatic cancer. Int J Oncol. 2011; 38(3):619–27.PubMedGoogle Scholar
- Busk M, Munk OL, Jakobsen S, Wang T, Skals M, Steiniche T, Horsman MR, Overgaard J. Assessing hypoxia in animal tumor models based on pharmocokinetic analysis of dynamic FAZA PET. Acta Oncol. 2010; 49(7):922–33.View ArticlePubMedGoogle Scholar