Assessment of tissue perfusion of pancreatic cancer as potential imaging biomarker by means of Intravoxel incoherent motion MRI and CT perfusion: correlation with histological microvessel density as ground truth

Mayer, Philipp; Fritz, Franziska; Koell, Marco; Skornitzke, Stephan; Bergmann, Frank; Gaida, Matthias M.; Hackert, Thilo; Maier‐Hein, Klaus H.; Laun, Frederik Bernd; Kauczor, Hans‐Ulrich; Grenacher, Lars; Klauß, Miriam; Stiller, Wolfram

doi:10.1186/s40644-021-00382-x

Cited by 20 publications

(22 citation statements)

References 39 publications

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“…This study is a retrospective evaluation of data that were acquired in the course of a prospective study performed between August 2014 and July 2015 and was approved by the local ethics committee [17]. 23 patients underwent perfusion CT imaging.…”

Section: Study Design and Study Populationmentioning

confidence: 99%

Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma using Deconvolution, Maximum Slope, and Patlak Methods – Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values

Koell

Klauß

Skornitzke

et al. 2021

Rofo

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Purpose The goal of this study was to evaluate the diagnostic accuracy of perfusion computed tomography (CT) parameters obtained by different mathematical-kinetic methods for distinguishing pancreatic adenocarcinoma from normal tissue. To determine cut-off values and to assess the interchangeability of cut-off values, which were determined by different methods. Materials and Methods Perfusion CT imaging of the pancreas was prospectively performed in 23 patients. 19 patients with histopathologically confirmed pancreatic adenocarcinoma were included in the study. Blood flow (BF), blood volume (BV) and permeability-surface area product (PS) were measured in pancreatic adenocarcinoma and normal tissue with the deconvolution (BF, BV, PS), maximum slope (BF), and Patlak methods (BV, PS). The interchangeability of cut-off values was examined by assessing agreement between BF, BV, and PS measured with different mathematical-kinetic methods. Results Bland-Altman analysis demonstrated poor agreement between perfusion parameters, measured with different mathematical-kinetic methods. According to receiver operating characteristic (ROC) analysis, PS measured with the Patlak method had the significantly lowest diagnostic accuracy (area under ROC curve = 0.748). All other parameters were of high diagnostic accuracy (area under ROC curve = 0.940–0.997), although differences in diagnostic accuracy were not statistically different. Cut-off values for BF of ≤ 91.83 ml/100 ml/min and for BV of ≤ 5.36 ml/100 ml, both measured with the deconvolution method, appear to be the most appropriate cut-off values to distinguish pancreatic adenocarcinoma from normal tissue. Conclusion Perfusion parameters obtained by different methods are not interchangeable. Therefore, cut-off values, which were determined using different methods, are not interchangeable either. Perfusion parameters can help to distinguish pancreatic adenocarcinoma from normal tissue with high diagnostic accuracy, except for PS measured with the Patlak method. Key Points: Citation Format

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Section: Study Design and Study Populationmentioning

confidence: 99%

Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma using Deconvolution, Maximum Slope, and Patlak Methods – Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values

Koell

Klauß

Skornitzke

et al. 2021

Rofo

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“…Die mit dem IVIM-DW-MRT-Modell berechnete Perfusionsfraktion f vermag ähnlich gut wie die CT-Perfusion die Durchblutung und histopathologisch quantifizierte Mikrogefäßdichte des PDAC abzuschätzen [ 14 , 20 ]. Für die klinische Praxis ist relevant, dass die Perfusionsfraktion f aus dem biexponentiellen IVIM-Modell eine höhere diagnostische Güte für die Differenzierung zwischen PDACs und chronischen Pankreatitiden aufweist als der ADC aus dem monoexponentiellen Diffusionsmodell [ 13 ].…”

Section: Korrelation Von Histologie Und Radiologieunclassified

Die Mikroarchitektur des Pankreaskarzinoms aus Sicht des Pathologen und des Radiologen

Mayer

Gaida

2021

Pathologe

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ZusammenfassungDie diagnostische Radiologie ist gemeinsam mit der diagnostischen Pathologie eines der klinisch-morphologischen Fächer, welche in unterschiedlicher makroskopischer bzw. mikroskopischer Auflösung zur Detektion, Charakterisierung sowie zum Ausbreitungsmuster eines Tumors führen. Die klinischen Disziplinen sind oft voneinander getrennt, wenngleich es vor allem in klinischen Tumorboards immer stärkere Verzahnungen gibt. Am Beispiel des Pankreaskarzinoms sind die Korrelationen radiologischer und pathologischer Diagnostik dargestellt.

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“…Perfusion-related parameters, including the pseudo-diffusion coefficient ( D *) and the perfusion fraction ( f ), may predict tumor microvasculature. Regarding tumor angiogenesis, a correlation between microvessel density (investigated by CD31 staining) and IVIM parameters has been observed in various types of cancers 19 – 21 .…”

Section: Introductionmentioning

confidence: 98%

Investigation of breast cancer microstructure and microvasculature from time-dependent DWI and CEST in correlation with histological biomarkers

Someya

Iima

Imai

et al. 2022

Sci Rep

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We investigated the associations of time-dependent DWI, non-Gaussian DWI, and CEST parameters with histological biomarkers in a breast cancer xenograft model. 22 xenograft mice (7 MCF-7 and 15 MDA-MB-231) were scanned at 4 diffusion times [Td = 2.5/5 ms with 11 b-values (0–600 s/mm2) and Td = 9/27.6 ms with 17 b-values (0–3000 s/mm2), respectively]. The apparent diffusion coefficient (ADC) was estimated using 2 b-values in different combinations (ADC0–600 using b = 0 and 600 s/mm2 and shifted ADC [sADC200–1500] using b = 200 and 1500 s/mm2) at each of those diffusion times. Then the change (Δ) in ADC/sADC between diffusion times was evaluated. Non-Gaussian diffusion and intravoxel incoherent motion (IVIM) parameters (ADC0, the virtual ADC at b = 0; K, Kurtosis from non-Gaussian diffusion; f, the IVIM perfusion fraction) were estimated. CEST images were acquired and the amide proton transfer signal intensity (APT SI) were measured. The ΔsADC9–27.6 (between $${\text{sADC}}_{{9\,{\text{ms}}}}^{200{-}1500}$$ sADC 9 ms 200 - 1500 and $${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$ sADC 27.6 ms 200 - 1500 and ΔADC2.5_sADC27.6 (between $${\text{ADC}}_{{2.5\, {\text{ms}}}}^{0{-}600}$$ ADC 2.5 ms 0 - 600 and $${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$ sADC 27.6 ms 200 - 1500 ) was significantly larger for MCF-7 groups, and ΔADC2.5_sADC27.6 was positively correlated with Ki67max and APT SI. ADC0 decreased significantly in MDA-MB-231 group and K increased significantly with Td in MCF-7 group. APT SI and cellular area had a moderately strong positive correlation in MDA-MB-231 and MCF-7 tumors combined, and there was a positive correlation in MDA-MB-231 tumors. There was a significant negative correlation between APT SI and the Ki-67-positive ratio in MDA-MB-231 tumors and when combined with MCF-7 tumors. The associations of ΔADC2.5_sADC27.6 and API SI with Ki-67 parameters indicate that the Td-dependent DW and CEST parameters are useful to predict the histological markers of breast cancers.

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Assessment of tissue perfusion of pancreatic cancer as potential imaging biomarker by means of Intravoxel incoherent motion MRI and CT perfusion: correlation with histological microvessel density as ground truth

Cited by 20 publications

References 39 publications

Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma using Deconvolution, Maximum Slope, and Patlak Methods – Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values

Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma using Deconvolution, Maximum Slope, and Patlak Methods – Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values

Die Mikroarchitektur des Pankreaskarzinoms aus Sicht des Pathologen und des Radiologen

Investigation of breast cancer microstructure and microvasculature from time-dependent DWI and CEST in correlation with histological biomarkers

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