Is there a need for a CT scan of the pancreatic phase? A perfusion and simulation study of the pancreas, an HCC, and the kidney cortex

Abstract

Purpose

To explore the peak enhancement time of a hepatocellular carcinoma, the pancreas, and the kidney cortex and its determinants.

Materials and Methods

We obtained a time enhancement curve from the perfusion CT scans of 11 advanced HCC patients (40 volumes at 1.25 s time interval, slab slice 90 mm, bolus of 50 ml of iodinated contrast agent, 350 g iodine/ml, flow 5 ml/s). Small regions of interest were drawn on the abdominal aorta, the HCC, the cortex of the right kidney, and on the pancreas. The behavior of the contrast agent in the capillary and in the surrounding tissue was further explored with a finite element model.

Results

The peak enhancement time of the pancreas did not differ from that of the HCC (10±3 vs. 11±4 s, p=0.9), while the peak enhancement time of the kidney tended to be a few seconds earlier (8±1 s, p=0.082 vs. pancreas and p=0.069 vs. kidney). Simulation showed that the time span in which the tissue enhancement remained within 10% of its peak value was similar across all capillary densities and ranged between 26−38 s for a capillary density of 0.00125 per mm to 30–60 s for a capillary density of 0.01.

Conclusion

The plateau tissue enhancement clinically acquired in the “late arterial phase” should be adequate both for the detection of hypervascular liver lesions such as HCCs and for obtaining peak pancreatic enhancement to detect hypovascular lesions.

Key Points

• The peak tissue enhancement time of an HCC, the pancreas, and the kidney cortex is similar

• The tissue peak enhancement time in the arterial phase is at the end of bolus transit

• Simulation shows that tissue enhancement peak time is a function of capillary density

Citation Format

• Cressoni M, Cadringher P, Colarieti A et al. Is there a need for a CT scan of the pancreatic phase? A perfusion and simulation study of the pancreas, an HCC, and the kidney cortex. Rofo 2025; DOI 10.1055/a-2516-3176

Zusammenfassung

Zweck

Untersuchung der Spitzenkontrastverstärkungszeit von hepatozellulärem Karzinom, Pankreas und Nierenrinde sowie deren Einflussfaktoren.

Materialien und Methoden

Wir haben Zeit-Verstärkungskurven aus Perfusions-CT-Scans von 11 Patienten mit fortgeschrittenem HCC erhalten (40 Volumina in 1,25 s Intervallen, Schichtdicke 90 mm, Bolus von 50 ml jodhaltigem Kontrastmittel, 350 g Jod/ml, Flussrate 5 ml/s). Kleine Interessensregionen wurden auf der Bauchaorta, dem HCC, der Nierenrinde und dem Pankreas markiert. Das Verhalten des Kontrastmittels in den Kapillaren und im umliegenden Gewebe wurde weiter mit einem Finite-Elemente-Modell untersucht.

Ergebnisse

Die Spitzenkontrastverstärkungszeit des Pankreas unterschied sich nicht von der des HCC (10±3 vs 11±4 s, p=0.9), während die der Niere einige Sekunden früher zu liegen schien (8±1 s, p=0.082 vs. Pankreas und p=0.069 vs. Niere). Simulationen zeigten, dass der Zeitraum, in dem die Gewebekontrastverstärkung innerhalb von 10% ihres Spitzenwertes blieb, bei allen Kapillardichten ähnlich war: Er reichte von 26−38 s bei einer Kapillardichte von 0.00125/mm bis 30–60 s bei einer Kapillardichte von 0.01.

Schlussfolgerungen

Die klinisch in der „späten arteriellen Phase“ erworbene Gewebekontrastverstärkung sollte sowohl für die Erkennung hypervaskulärer Leberläsionen wie HCC als auch für die Erzielung der Spitzenkontrastverstärkung des Pankreas zur Erkennung hypovaskulärer Läsionen geeignet sein.

Kernaussagen

• Die maximale Gewebeanreicherungszeit von HCC, Pankreas und Nierenrinde ist ähnlich

• Die Gewebespitzenverstärkungszeit in der arteriellen Phase liegt am Ende des Bolustransports

• Simulationen zeigen, dass die Spitzenzeit der Gewebeverstärkung von der Kapillardichte abhängt

Keywords

contrast agents - pancreatic adenocarcinoma - computed tomography - arterial phase - hepatocarcinoma - perfusion imaging

Introduction

CT scans of the arterial phase are usually subdivided into an early arterial phase corresponding to contrast arrival in the aorta where only vessels are opacified (15−20 s post-injection or immediately after bolus tracking) and the late arterial phase where highly perfused tissues show their maximal contrast enhancement (35−40 s post-injection or 15−20 s after bolus tracking) [1] [2] [3]. This late arterial phase is commonly scanned to detect hypervascular lesions in the liver. However, the optimal timing for the best pancreatic enhancement and detection of pancreatic adenocarcinoma is unknown. A series of manuscripts that compared early CT scan acquisition at 20 s with late arterial acquisition at approximately 40–70 s suggested the existence of a separate “pancreatic phase” defined as a contrastographic phase obtained after the late arterial phase, 40–70 seconds after infusion of intravenous contrast material [4] [5]. Consequently, some institutions consider later acquisition in the clinical routine, and the term “pancreatic phase” is frequently used [5]. In contrast, other authors [6] have reported time enhancement curves of the pancreatic parenchyma suggesting that pancreatic peak enhancement occurs at 40 s, which is consistent with the standard timing of the late arterial phase.

The core of this topic is the mechanism of abdominal contrast enhancement in the arterial phase and its determinants. It has been claimed that the behavior of contrast agents is exclusively intravascular during the arterial phase [7] [8]. The precise location of contrast agent is relevant for determining what the radiologist sees and interprets while evaluating an arterial phase CT scan. We hypothesized that a perfusion study exploring the possible abdominal arterial phases by scanning the same volume every 1.0−1.5 s could allow extrapolation of the “rules” behind contrast enhancement in the arterial phase.

Methods

Patients

This study is a retrospective analysis of patients from a previously published study regarding the impact of antiangiogenic agents on the vascularity of HCC [9] [10]. The study was approved by the ethics committee San Gerardo Hospital, Via Pergolesi 33, 20900, Monza MB, Italy and all patients provided written informed consent prior to enrollment in accordance with institutional guidelines. Only eleven of the 40 CT examinations met the enrollment criteria, which required an abdominal perfusion study pre-treatment with the pancreas and right kidney included in the imaged volume. Patients with known pancreatic or kidney disease were excluded.

Perfusion CT protocol

CT examinations were performed on a multi-detector 256-slice CT scanner (Brilliance, iCT, Philips Medical Systems, Eindhoven, The Netherlands). A multiphase CT scan (i.e., no contrast, arterial, portal venous, and delayed phases) was acquired before and after intravenous bolus injection of a non-ionic iodinated contrast material (Xenetix 350; Guerbet, Aulnay, France). These data were not used for the analysis since it included only one time point in the arterial phase.

The perfusion CT examination was performed about 45 min after the standard dynamic MDCT examination to avoid the effect of previously administered contrast.

Perfusion examinations were performed as follows: a 50-mL bolus of iodinated contrast agent (with a 350 mgI/mL concentration (Xenetix 350, Guerbet, Aulnay, France) was injected in an antecubital vein at a 5 mL/s flow rate, acquiring 40 CT scan volumes on a 256-slice multi-detector-row scanner (Brilliance iCT, Philips Medical Systems, Eindhoven, Netherlands) at time intervals of 1.25 s (slice thickness 90 mm). The acquisition parameters were 100 kV, 100 mAs. CT data acquisition began after a 5 s delay from intravenous bolus injection.

To avoid motion artifacts, a strap compressing the abdomen was employed to limit respiratory excursion.

Image analysis

Image analysis was performed with custom software (www.softefilm.eu). A trained radiologist (M.C., with 6 years of experience) preprocessed perfusion images by drawing small regions of interest (ROIs) of approximately 80 mm² (100−200 pixels of 0.74 × 0.74 mm) on the abdominal aorta, the HCC, the right renal cortex, and the region of the pancreas best visualized within the acquired volume, which was the pancreatic tail for 8 patients and the pancreatic body for 3 patients. The pancreatic tail is typically positioned superior to the head and is more likely to be encompassed within the study volume. No efforts were undertaken to spatially register the images. ROIs were then propagated across all frames of the perfusion study. Median density values in HU of each ROI were calculated. We used median values rather than mean values to more effectively remove outlier voxels from noisy images.

These data were used to compute time enhancement curves using a spline model and gamma variate fitting to compare peak enhancement times and curve shapes in the HCC, kidney, and pancreas. We used the Madsen’s formulation of the gamma variate function [11] which describes the bolus as a function of its enhancement peak time (t max), height (y max), and shape (alpha).

Simulations

A simulation of the behavior of contrast agent within a capillary and surrounding extravascular tissue with a finite element model was implemented as a Visual Basic application within an Excel spreadsheet (Microsoft, Redmond, WA, USA). The capillary was modeled as a cylinder and the interstitial space as 20 multiple coaxial cylinders concentric to the capillary. The capillary and interstitial space were further divided into 40 sections along their longitudinal axes. Diffusion between the capillary and the interstitial space and between the interstitial space layers was modeled with cylindrical coordinates. Equilibrium between all regions is computed at each time step. The fully functional simulator together with source code is available at 10.6084/m9.figshare.28250687. The selection of 20 coaxial cylinders and 40 sections is arbitrary and is a compromise between simulation accuracy and computation time.

The contrast bolus was modeled as gamma variate function settings y max, t max, and alpha. The number of capillaries, and, consequently, the volume of interstitial space was modeled by defining the capillary density as the number of capillaries/mm assuming that the capillaries are parallel to one another like in Krog’s model [12]. The simulation parameters are summarized in ([Table 1]).

Table 1 Parameters used for capillary model.

Parameter	Values
The table summarizes the parameters used for the simulations. Diffusivity was set to 0 or 5·10–3 mm2/s or zero, assuming that the capillary walls are freely permeable to small molecules like iodinate contrast agent, which have a diffusivity near the contrast agent diffusivity in water. The number of capillaries per mm was simulated using four growing values, with each one being double the previous (0.00125, 0.0025, 0.05, 0.1) and ranging from values similar to a highly vascularized tissue like the kidney to a poorly vascularized, fibrous one with only 10 capillaries/mm.
Diffusivity	0 or 5·10–3 mm2/s
Capillary diameter	0.005 mm
Capillary length	1 mm
Capillary speed	0.23 mm/s
Capillary density (number of capillaries per mm)	0.00125/0.0025/0.05/0.1

Simulations were performed with increasing capillary densities: 0.00125, 0.0025, 0.005, and 0.1 capillaries/mm, with both a permeable capillary and a non-permeable capillary. Our aim was not to simulate any particular tissue but to explore the whole possible range of capillary densities using double the value each time. A capillary density of 0.00125 capillaries/mm had been roughly extrapolated for the pancreas from the study published by Fowler et al. [13]. We were unable to estimate the capillary density of the renal cortex, which alternates glomeruli composed of capillary bundles to a less vascularized space between glomeruli, and arbitrarily established an upper limit of capillary density at 0.00125 capillaries/mm, which is double that of the pancreas.

It is important to note that the diffusivity was set to 5·10^–3 mm²/s or zero assuming that the capillary wall is freely permeable to small molecules like those of iodinated contrast agent, which have a diffusivity comparable to that of contrast diffusivity in water. [Fig. 1] illustrates the correlation between simulated perfusion (mL/s/mL) and capillary density (distance between capillaries/mm). As shown, the relationship between the two quantities is exponential rather than linear.

Fig. 1 shows the relationship between perfusion and capillary density. Perfusion is defined as ml/s/ml while capillary density is the number of capillaries/mm. Assuming that (1) flow in the capillary is constant and (2) all capillaries are the same size, we can compute: Perfusion = capillary volume * capillary number; Capillary volume = 2*pi*r² * capillary length (capillary length set to 1 mm); Perfusion = 2*pi*r² * 1/capillary density.

Capillary and tissue time enhancement curves shapes – effect of extravascular contrast media

We fitted the simulated curves to the Madsen formulation of the gamma variate equation and evaluated the peak time and the alpha parameter (indicator of curve steepness) between the simulated input boluses and the curves obtained at different capillary densities. Because contrast enhancement reaches a plateau, we also determined the time range during which contrast enhancement remained within 10% of its maximum value. It should be noted that statistical analysis was not performed as simulated data shows no variability.

Power analysis

We were able to find only 11 perfusion studies that met the enrollment criteria. Abdominal perfusion studies are not used in clinical practice because they result in considerable radiation exposure. Therefore, we had to limit ourselves to the available data. The number of participants required for a study and hence the power of a statistical test depend on the variability of the data, which was minimal in our patient population. The primary aim was to compare the peak enhancement in an HCC and the pancreas. We arbitrarily defined the least clinically significant difference in peak time normalized by aortic peak time as 5 seconds. The measured standard deviation was 4 seconds. Seven patients were required to identify a mean difference of 5 seconds in peak time between an HCC and the pancreas with a standard deviation of 4 seconds (power of 80% and a level of significance of 5%, two sided).

Eleven patients are typically inadequate for a diagnostic investigation but adequate for a physiological study intended to elucidate fundamental physiological mechanisms. Multiple readers are essential for the evaluation of diagnostic performance. Conversely our objective was to derive time enhancement curves from a noisy dataset. Simulation studies were incorporated to demonstrate our investigation of a basic physiological mechanism.

Statistical analysis

Enhancement peak times are given as mean ± standard deviation. A longitudinal data analysis of the average estimate of the time enhancement curves for the aorta, pancreas, HCC, and kidney ROIs was performed. A random intercept model was used. Time was modelled using a natural cubic spline with two degrees of freedom. An interaction between time and organ was used to account for differences in time enhancement curves.

For each patient we calculated the peak enhancement time and alpha value (bolus shape) for the aorta, pancreas, HCC, and kidney ROIs from the gamma-fitting of the time enhancement curves. The peak enhancement times were analyzed with ANOVA for repeated measures and multiple comparisons were performed with the Bonferroni’s correction. Statistical analysis was conducted using R software [14].

Results

Enhancement peak time of pancreas, kidney, and HCC

The fitting of the longitudinal regression models is depicted in [Fig. 2]. The kidney enhancement curve peaked before the pancreatic and HCC enhancement curves (p < 0.001), while the peak times for the pancreas and HCC were similar (p=0.988).

Fig. 2 The observed time enhancement curves for the aorta, pancreas, hepatocellular carcinoma, and the kidney for the 11 patients. The average model estimate is reported by the thick black line. The peaks are at mean times of 23, 38, 32 and 34 for the aorta, HCC, kidney, and pancreas, respectively.

The gamma-fitting methodology indicated that the enhancement peak times for the pancreas, kidney, and HCC, which occurred at the end of the bolus transit in the aorta, were 27 ± 7 s (mean ± standard deviation), 29 ± 6 s, and 25 ± 6 s (p = 0.026, ANOVA for repeated measures), respectively. There was a trend toward an earlier peak time for the kidney compared to the pancreas (p = 0.066) and an HCC (p = 0.084) while the peak times for an HCC and the pancreas were similar (p = 0.932). The enhancement peak time of the aorta was 18 ± 6 s. [Fig. 3] illustrates peak times calculated as the difference between the peak time of the tissue and the aortic peak time. The peak enhancement times were 10 ± 3, 8 ± 1, and 11 ± 4 s for the pancreas, kidney, and HCC, respectively (p = 0.021 overall). There was a trend indicating a significant difference between the kidney peak time and both the pancreas peak time (p = 0.082) and HCC peak time (p = 0.069), while the HCC and pancreas peak times were comparable (p = 0.90).

Fig. 3 This graph presents time enhancement curves of the aorta (red), kidney (brown), pancreas (orange), and hepatocellular carcinoma (yellow) presented as mean and standard error. Individual patient curves had been fitted with gamma variate function to remove recirculation (enhancement (HU)=ymax × t^α × e^α(1–t) where ymax represents the maximum enhancement, t is defined as time/tmax (tmax being the time at which the function reaches its maximum) and α describes the bolus shape. Time enhancement curves are created for each patient using gamma variate fitting with the peak time subtracted from the peak aortic time. Computed enhancement is averaged across patients and standard errors are presented in 1 s intervals. The x-axis of the aorta is computed for each patient by subtracting the aortic peak time from the time scale and averaging the resulting times.

The bolus curve of the parenchymas showed a lower curve upslope defined as the alpha parameter of the Madsen gamma variate (4 ± 2, 5 ± 3, and 3 ± 2 s for pancreas, kidney and HCC, respectively) compared to that of the aorta (10 ± 7 s) (p < 0.001).

Simulations of capillary time enhancement curves

[Fig. 4], [Fig. 5], and [Fig. 6] present the simulated time enhancement curves for the input contrast bolus and volume at various capillary densities as well as capillary walls that are permeable or non-permeable to iodine contrast agent. The simulated curves of inlet contrast (aorta) and tissues with capillary densities ranging from 0.00125 to 0.0025 ([Fig. 4]) are similar to those obtained in patients in terms of relative height to the aorta, peak time, and upslope of the ascending part of the curve.

Fig. 4 shows simulated time enhancement curves of the aorta and tissue with 4 different capillary densities, doubling each time (0.00125, 0.0025, 0.005, 0.01 capillaries/mm). The capillary wall is considered permeable to iodine contrast media.

Fig. 5 shows simulated time-enhancement curves of aorta and tissue with 4 different capillary densities, doubling each time (0.00125, 0.0025, 0.005, 0.01 capillaries/mm). The capillary wall is considered permeable to iodine contrast media.

Fig. 6 shows the effect of capillary permeability on time enhancement curve shape at a capillary density of 0.01 capillaries/mm.

Determinants of enhancement peak time from simulations

The simulated enhancement peak time increased with increasing capillary density, reaching 32 s after contrast arrival with a capillary density of 0.00125 per mm and 46 s for poorly perfused tissue with a capillary density of 0.1 per mm ([Table 1]).

Upon examining the time range during which tissue enhancement stayed within 10% of its maximum value, significant overlap was observed across all capillary densities, spanning from 26 to 38 seconds for a capillary density of 0.0001 per mm, and from 30 to 60 seconds for a capillary density of 0.01 capillaries per mm.

Capillary and tissue time enhancement curve shapes – effect of extravascular contrast media

The effect of capillary permeability on the time enhancement curve is shown in [Fig. 5] (capillary density 0.00125 capillaries/mm) and [Fig. 6] (capillary density 0.1 capillaries/mm). [Fig. 7] and [Fig. 8] show that most of the contrast agent in the system is located in the extravascular volume regardless of the capillary density.

Fig. 7 Total amount of contrast and extravascular contrast agent in the system with a capillary density of 0.00125.

Fig. 8 Total amount of contrast and extravascular contrast agent in the system with a capillary density of 0.01. As shown, almost all contrast agent is located in the extracellular tissue.

The transfer of iodine contrast into the extravascular space makes the shape of the tissue time enhancement curve different from the capillary time enhancement curve. The alpha parameter (indicator of curve steepness was reduced from 10 of the simulated inlet boluses to 7, 3, 3, and 2 for capillary densities of 0.00125, 0.0025, 0.005, and 0.1 per mm, respectively.

Discussion

The primary findings of the current investigation are as follows:

1. On average, the enhancement peak time is similar for the studied abdominal parenchymas (pancreas, renal cortex, and HCC) with a tendency of the kidney to exhibit a slightly earlier peak enhancement, suggesting that there is no clinical need to differentiate arterial acquisition times according to the diagnostic inquiry;

2. Peak tissue enhancement occurs at the end of the contrast bolus transit, which represents the optimal time for arterial phase scanning;

3. Iodine contrast agent was predominantly extravascular during the arterial phase.

The definition of time enhancement curves makes it possible to understand the principles governing system behavior, which must apply consistently.

The optimal acquisition timing for a particular diagnosis may be determined by obtaining contrast-enhanced CT images at various intervals and doing a comparative analysis. This approach treats the organ of interest as a “black box” capable of exhibiting any conceivable behavior, with its functionality being ascertainable solely through experimental testing. Furthermore, the results are strictly dependent on the specific CT scan setting. We concluded that a perfusion study acquiring a large number of timepoints during the whole arterial phase is the best approach to investigate this topic. The definition of the time enhancement curves, in turn, makes it possible to understand the rules underlying the system behavior, which must always be valid.

The definition of the optimal CT scan timing for the arterial phase is likewise based on an understanding of the mechanism of enhancement. If the capillary was impermeable to the contrast agent, the tissue bolus shape would be nearly identical to the arterial one (as occurs in the brain with the blood-brain barrier) and enhancement would be driven solely by intravascular iodine. On the contrary, if the endothelium is permeable, contrast will move from the intravascular to the extravascular space, delaying the peak tissue enhancement until the end of bolus transit, as shown by the time enhancement curves depicted in this manuscript where tissue enhancement peaks at the end of aortic bolus transit.

To the best of our knowledge, methods for assessing the behavior of contrast media at the capillary level are not available. Therefore, we opted to investigate this phenomenon in silico. Contrary to the existing literature [7] [8] [15], we demonstrated that contrast permeates the extracellular space immediately upon its entry into the capillary network. This phenomenon solely can explain the alteration in shape between the arterial contrast bolus (measured in the aorta) and the tissue time enhancement curve.

Tissue permeability and extravascular contrast behavior have consistently been expressed using Toft’s model [16], which describes contrast behavior through the permeability-surface product, integrating the capillary surface accessible for exchange with its permeability. The Brix model [17] delineates a pharmacokinetic model featuring a core compartment for contrast inflow and elimination, indicative of perfusion, which exists in dynamic equilibrium with a peripheral compartment symbolizing tissue.

In our simulations, we decoupled these two parameters, presuming that parenchymas exhibit unrestricted permeability to contrast agent. This assumption regarding free and constant permeability for iodinated contrast agent across all parenchymas is somehow counterintuitive since tumors exhibit aberrant capillaries with endothelial gaps, suggesting that they possess increased endothelial permeability compared to normal tissue.

Normal capillaries exhibit near-complete permeability to small molecules such as glucose (molecular weight 180.16 g/mol) and sucrose (molecular weight 342.30 g/mol). Iobitridol (the contrast molecule used in this study) has a molar mass of 835.2 g/mol, almost twice that of sucrose. The endothelium of parenchymas contains pores with a size of 5−10 nm [18]. Since we may estimate an upper limit of about 1 nm for the iodinated contrast molecular radius, which is 0.45 nm for sucrose, the iodine contrast agent should be able to freely cross endothelial pores. In fact, increased permeability of the tumoral endothelium compared to the normal endothelium has been consistently demonstrated for macromolecules such as ferritin, with a molecular weight of 474,000 (about 7,000 times that of iobitridol) and albumin, with a molecular weight of 66,500 (about 82 times that of iobitridol). This increased permeability is attributed to the activation of the vesiculo-vacuolar organ by vascular-endothelial growth factor with macromolecule transport. The increased tissue permeability in inflammatory conditions relies on the same mechanism [19].

Thus, we emphasize that iodinated contrast agents behave as tiny molecules, so that peak tissue enhancement in our model is merely a function of capillary density and its timing is consistent across all tissues at the end of bolus transit. Because contrast agents in the arterial phase are mainly extravascular, the parenchymal time enhancement curve approaches a plateau around 30 s, as demonstrated by both patient time enhancement curves and simulations.

The simulation data revealed a theoretically earlier peak with a higher vessel density, but when the range of 10% peak enhancement was considered, the different plateaus were similar. We believe that this difference cannot be clinically detected, or possibly only with extremely high levels of standardization.

The model we present and the accompanying simulations make a series of assumptions, such as the number of capillaries, the volume of interstitial space, Krog’s model, and free permeability. It is evident that no model can fully capture the biological complexity. However, we expect the actual differences will be too small to be of clinical interest and smaller than inter-patient differences.

The delayed tissue peak enhancement near the bolus end is explained by the interplay between capillary and tissue concentration. Initially, tissue concentration is zero, while capillary concentration is extremely high, thus fostering passage of contrast agent from capillary to tissue. Absolute rise in tissue concentration depends on capillary density/extracellular volume: if the capillary density is high, tissue concentration will rise more rapidly; if capillary density is low, contrast agent will be diluted and its tissue concentration will rise more slowly. The effect of capillary density is more pronounced on the washout phase of the curve. Contrast will re-enter the capillary only if the capillary concentration is lower than that of the tissue. If the capillary density is high with high tissue concentration, it will occur early. Conversely, if the capillary density is low, tissue concentration will remain low, as contrast from the capillary will be diluted in a huge volume, and contrast will re-enter the capillary only when the capillary concentration approaches zero. This likely represents the mechanism of early enhancement in prostatic and breast cancer, characterized by elevated capillary density within the tumor. The variations in peak time generated by capillary density ([Fig. 3], [Fig. 4], [Fig. 5] and [Table 2]) may explain the necessity of perfusion studies to identify the early enhancement of prostate cancer [20] [21] [22].

Table 2 Tissue enhancement peak time (EPT) and EPT range in which tissue enhancement is within ±10% of its maximal value.

Capillaries per mm	EPTmax	EPTmax minus 10%	EPTmax plus 10%
At the four different capillary densities, we simulated the peak time of the capillary densities and the time range in which tissue enhancement is within 10% of its peak value. As shown, the simulated tissue enhancements have a long plateau and considerable overlap.
None (non-permeable vascularization)	22	18	27
0.00125	31.8	26.7	38.4
0.0025	38.7	29.1	59.8
0.005	43.8	30	>60
0.01	46.1	30	>60

The “late enhancement” phenomenon [23] of fibrous tissue can be attributed to low capillary density, which allows for possible contrast reuptake from an enlarged extravascular space only after capillary concentration is significantly diminished.

The present study has some obvious limitations. First, the lack of direct measurement of pancreatic adenocarcinoma time enhancement curves so that CT perfusion studies including cases of pancreatic adenocarcinoma are needed for the clinical application of our hypothesis. However, because the tumor is hypovascular, what matters is the peak enhancement of the healthy pancreas, which is one of the two drivers of pancreatic adenocarcinoma detection.

We did not perform an analysis of the liver parenchyma because it has a double vascular input with most blood coming from the portal vein. Consequently, its enhancement would have been modeled differently when taking in account the time shift between the hepatic artery and the portal vein contrast input. We focused on highly vascularized tissues because of the higher signal-to-noise ratio and did not include muscle tissue, which is usually used as the reference tissue, since it is poorly perfused at rest.

The value of our findings indicating that the peak enhancement of an HCC, the pancreas, and the kidney is not significantly different may be limited by our small sample size and by the implementation of a specific protocol utilizing a reduced contrast volume (50 mL). This protocol facilitated a comprehensive elucidation of the contrast enhancement process, rendering alternative behaviors improbable.

The study aims to investigate and revise principles of physiology. First, we believe that this study will have an impact on the optimization of radiographic protocols. The peak enhancement time of highly vascularized tissues occurs at the end of bolus transit. It is the timing of the “perfect” arterial phase. Since tissue enhancement is a plateau rather than a sudden peak, this time point roughly correlates to the late arterial phase, albeit it may be difficult to determine the ideal time point. The test bolus gives only the bolus arrival time while the bolus shape is a function of contrast volume, injection rate, cardiac output, and blood volume and cannot be readily determined.

There was no significant difference in the peak enhancement time between an HCC and pancreatic tissue. A pancreatic adenocarcinoma is hypovascular and hypodense, gradually increasing its density until the portal venous phase [24] [25] and, as a result, the optimal time for detection is at peak pancreatic arterial enhancement.

That peak enhancement time occurring at the end of bolus transit indicated that the late arterial phase is optimal for HCC detection without the need for a double arterial scan. The double arterial phase was initially proposed for HCC detection [26] [27] but was ultimately discarded due to radioprotection issues without definitive physiopathological evidence [26] [28]. Multiple arterial scans for HCC had been recently proposed in the MRI setting [29]. The MRI bolus has a different shape and timing since the amount of contrast is lower (10–20 ml), consequently generating a shorter bolus. It may complicate the targeting of CT scan acquisition at the end of the bolus.

The fact that the peak enhancement time for HCCs occurs at the end of bolus transit raises doubts regarding the necessity of abdominal perfusion studies [30]. Because the majority of the iodine contrast is extravascular, tissue enhancement should be linearly proportional to perfusion and response to anti-angiogenic drugs can be estimated solely from the late arterial phase [10] .

In conclusion, our CT perfusion study of abdominal parenchymas showed that the presence of contrast agent in the extravascular space is the main determinant of contrast enhancement in the arterial phase. The movement of contrast agent from the capillary space to the interstitial space in parenchymatous organs lasts up to the inversion of the concentration gradient between the capillary and tissue determining tissue peak enhancement time at the end of bolus transit. Differences in peak enhancement time between tissues with different capillary densities are in the order of seconds. Consequently, patients with an HCC and patients with a suspected pancreatic adenocarcinoma do not need to be examined with different delays and a CT scan performed at the end of bolus transit would guarantee optimal enhancement of the HCC and the best contrast between a hypovascular pancreatic adenocarcinoma and a hypervascular pancreatic parenchyma. At this time point, vessels are well opacified to allow the definition of vessel tumor contact to evaluate respectability.

Clinical relevance

• Computed tomography peak enhancement time in the arterial phase is at the end of bolus transit, suggesting that the arterial phase acquisition time should not be differentiated according to the diagnostic question.

• HCC and pancreatic parenchyma have the same peak enhancement time.

• There is no need for a double arterial phase for HCC detection.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Publication History

Received: 16 November 2024

Accepted after revision: 09 January 2025

Article published online:
06 February 2025