Time density curve of dynamic contrast‑enhanced computed tomography correlates with histological characteristics of pancreatic cancer

Goto, Shintaro; Seino, Hiroko; Yoshizawa, Tadashi; Morohashi, Satoko; Ishido, Keinosuke; Hakamada, Kenichi; Kijima, Hiroshi

doi:10.3892/ol.2021.12537

Cited by 2 publications

(7 citation statements)

References 34 publications

(46 reference statements)

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“…Comparison of TDCs before and after NAC revealed that δ1′ was significantly higher than δ1 in low-responders. In our previous study, it was revealed that the first slope of TDCs is associated with microvessel density ( 16 ). Even though there were no significant differences in microvessel density between low-responders and high-responders, tumor angiogenesis may have involved the ascent of the first slope of TDC in low-responders.…”

Section: Discussionmentioning

confidence: 99%

“…The histological characteristics of the NAC and non-NAC groups were investigated. The densities of cancer cells, CAFs, microvessels and stromal collagen fibers in the whole areas of the tumor were measured in a way similar to that described in our previous study ( 16 ). In the non-NAC group, the entire maximum cross-section of the tumor was examined.…”

Section: Methodsmentioning

confidence: 99%

“…All of the patients in the NAC group underwent dynamic CECT before and after NAC using the fixed protocol at Hirosaki University Hospital. The protocol was the same as that described in our previous study ( 16 ). Dynamic CECT was conducted using a 64-detector row CT scanner (Discovery CT750 HD; GE Healthcare) with the following parameters: Detector configuration of 64×0.625, tube voltage of 120 kV, automatic tube current modulation, collimation of 40 mm, tube rotation time of 0.5 sec, pitch of 0.8, field of view of 35×35 cm, image matrix of 512×512, and slice thickness of 5 mm.…”

Section: Methodsmentioning

confidence: 99%

“…TDCs were drawn using Digital Imaging and Communications in Medicine data [EV insite R version 3.4.0.0 (PSP Corporation); or ShadeQuest/ViewR V1.24 (Yokogawa Medical Solutions)]. The method used to draw regions of interest (ROIs) in our previous study was used in the present study ( 16 ). The ROIs were drawn on the central areas of tumors, avoiding tumor margins, contrasted vessels and artificial materials, such as stents.…”

Section: Methodsmentioning

confidence: 99%

“…Our previous study on PDAC not treated with NAC showed that the time-density curve (TDC) of dynamic contrast-enhanced CT (CECT) was associated with the histological characteristics of PDAC, such as densities of cancer cells, CAFs and microvessels ( 16 ). Dynamic CECT consists of four time phases: Non-contrast, arterial, portal and equilibrium phases.…”

Section: Introductionmentioning

confidence: 99%

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Use of time‑density curves of dynamic contrast‑enhanced computed tomography for determination of the histological therapeutic effects of neoadjuvant chemotherapy for pancreatic ductal adenocarcinoma

et al. 2023

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The present study aimed to investigate the histological changes caused by neoadjuvant chemotherapy (NAC) for pancreatic ductal adenocarcinoma (PDAC), and to demonstrate the use of time-density curves (TDCs) of dynamic contrast-enhanced computed tomography (CECT) for determination of the histological therapeutic effects of NAC for PDAC. A total of 96 patients with PDAC were examined; 46 underwent NAC (NAC group) and 50 did not undergo NAC (non-NAC group). Based on histological therapeutic effect and using the area of residual tumor (ART) grading system, the NAC group was divided into low-responders and high-responders. Histological analysis was used to evaluate the densities of cancer cells, cancer-associated fibroblasts (CAFs), microvessels and stromal collagen fibers in the NAC and non-NAC groups. Radiological analysis was used to evaluate the TDCs of three slopes of the NAC group, namely slopes between the non-contrast and arterial phases (δ1 and δ1'), between the arterial and portal phases (δ2 and δ2'), and between the portal and equilibrium phases (δ3 and δ3'). δ1-δ3 were before NAC, whereas δ1'-δ3' were after NAC. Changes in δ1, δ2 and δ3 before and after NAC were denoted as δδ1 (=δ1'-δ1), δδ2 (=δ2'-δ2) and δδ3 (=δ3'-δ3). ART grading system, histological examination and radiological examination data were also statistically analyzed. Histological examination revealed a significant decrease in cancer cells and CAFs, and a significant increase in stromal collagen fibers due to NAC (P<0.01). Radiological examination revealed that δ1' was significantly higher than δ1 in low-responders (P<0.05), whereas δ2' was significantly lower than δ2 in high-responders (P<0.01). δδ2 was significantly lower and δδ3 was significantly higher in high-responders than in low-responders (P<0.01 and P<0.05, respectively). Receiver operating characteristic curve showed that δδ2 and δδ3 were effective indicators of the histological therapeutic effect of NAC. In conclusion, the TDC of dynamic CECT may be useful for determining the histological therapeutic effect of NAC for PDAC.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Use of time‑density curves of dynamic contrast‑enhanced computed tomography for determination of the histological therapeutic effects of neoadjuvant chemotherapy for pancreatic ductal adenocarcinoma

et al. 2023

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In-silico analysis of curve fitting in angiographic parametric imaging in intracranial aneurysms to reduce patient and interventionalist-induced errors

Mondal,

Shields,

Bhurwani

et al. 2024

Medical Imaging 2024: Physics of Medical Imaging

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In Angiographic Parametric Imaging (API), accurate estimation of parameters from Time Density Curves (TDC) is crucial. However, these estimations are often marred by errors arising from factors such as patient motion, image noise, and injection variability. While fitting methods like gamma-variate fitting offer a solution to recover incomplete or corrupted TDC data, they might also introduce unforeseen biases. This study investigates the trade-offs and benefits of employing gamma-variate fitting on virtual angiograms to enhance the precision of API biomarkers. Utilizing Computational Fluid Dynamics (CFD) in patient specific 3D geometries, we generated a series of high-definition virtual angiograms at distinct inlet velocities: 0.25m/s, 0.35m/s, and 0.45m/s. These velocities were investigated across injection durations ranging from 0.5s to 2.0s. From these angiograms, TDCs for aneurysms and their corresponding inlets were constructed. We generated a perfect injection function and convolved with it with the Impulse Residue Function (IRF), to get a TDC of the aneurysm dome. To emulate typical clinical challenges, we introduced noise, simulated patient motion, and generated temporally incomplete data sets. These modified TDCs underwent gamma-variate fitting. We quantified both the ideal, non-gamma fitted and fitted TDC curves using standard angiography metrics such as Cross-Correlation (Cor), Time to Peak (TTP), Mean Transit Time (MTT), Peak Height (PH), Area Under the Curve (AUC), and Maximum Gradient (Max-Gr) for a comprehensive comparison. TDCs enhanced by gamma-variate fitting exhibited a robust correlation with vascular flow dynamics. Our results affirm that gamma-variate fitting can adeptly restore TDCs from fragmentary sequences, elevating the precision of derived API parameters. Incorporating gamma-variate fitting into TDCs analysis augments the precision and robustness of API parameters, bolstering the credibility of neurovascular diagnostic procedures.

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Time density curve of dynamic contrast‑enhanced computed tomography correlates with histological characteristics of pancreatic cancer

Cited by 2 publications

References 34 publications

Use of time‑density curves of dynamic contrast‑enhanced computed tomography for determination of the histological therapeutic effects of neoadjuvant chemotherapy for pancreatic ductal adenocarcinoma

Use of time‑density curves of dynamic contrast‑enhanced computed tomography for determination of the histological therapeutic effects of neoadjuvant chemotherapy for pancreatic ductal adenocarcinoma

In-silico analysis of curve fitting in angiographic parametric imaging in intracranial aneurysms to reduce patient and interventionalist-induced errors

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