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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. 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. 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. 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. · Perfusion CT parameters showed high diagnostic accuracy in differentiating between pancreatic adenocarcinoma and normal tissue.. · Only PS measured with the Patlak method showed a significantly lower diagnostic accuracy.. · Perfusion parameters measured with different mathematical-kinetic methods are not interchangeable.. · A specific cut-off value must be determined for each method and each perfusion parameter..· Koell M, Klauss M, Skornitzke S et al. Computed Tomography Perfusion Analysis of Pancreatic Adenocarcinoma with the Deconvolution, Maximum Slope, and Patlak Methods - Evaluation of Diagnostic Accuracy and Interchangeability of Cut-Off Values. Fortschr Röntgenstr 2021; 193: 1062 - 1073.ZIEL: Ziel dieser Studie war es, die diagnostische Güte von Perfusions-Computertomografie (CT) -Parametern zu evaluieren, die mit verschiedenen mathematisch-kinetischen Methoden gemessen wurden, für die Differenzierung zwischen Pankreaskarzinom und Normalgewebe. Es sollten zudem Cut-off-Werte bestimmt und die Austauschbarkeit von Cut-off-Werten, die mit verschiedenen Methoden ermittelt wurden, evaluiert werden. Bei 23 Patienten wurde prospektiv eine Perfusions-CT-Bildgebung des Pankreas durchgeführt. 19 Patienten mit histopathologisch bestätigtem Pankreaskarzinom wurden in die Studie eingeschlossen. Der Blutfluss (BF), das Blutvolumen (BV) und das Permeabilitätsoberflächenprodukt (PS) wurden im Pankreaskarzinom und Normalgewebe mit der Deconvolution- (BF, BV, PS), Maximum-Slope- (BF) und Patlak-Methode (BV, PS) gemessen. Die Austauschbarkeit der Cut-off-Werte wurde evaluiert, indem die Übereinstimmung zwischen BF, BV und PS untersucht wurde, die mit verschiedenen mathematisch-kinetischen Methoden gemessen wurden. Die Bland-Altman-Analyse zeigte eine schlechte Übereinstimmung zwischen den Perfusionsparametern, die mit verschiedenen mathematisch-kinetischen Methoden gemessen wurden. Gemäß ROC-Analyse (Receiver Operating Characteristic) hatte das mit der Patlak-Methode gemessene PS die signifikant niedrigste diagnostische Güte (Fläche unter der ROC-Kurve = 0,748). Alle anderen Parameter hatten eine hohe diagnostische Güte (Fläche unter der ROC-Kurve = 0,940–0,997); deren diagnostische Güte unterschied sich jedoch nicht statistisch signifikant. Cut-off-Werte für BF von ≤ 91,83 ml/100 ml/min und für BV von ≤ 5,36 ml/100 ml, beide mit der Deconvolution-Methode gemessen, scheinen die geeignetsten Cut-off-Werte zu sein, mit denen das Pankreaskarzinom von Normalgewebe abgegrenzt werden kann. Perfusionsparameter, die mit verschiedenen Methoden gemessen wurden, sind nicht austauschbar. Daraus folgt, dass Cut-off-Werte, die mit unterschiedlichen Methoden ermittelt wurden, ebenfalls nicht austauschbar sind. Perfusionsparameter können dazu beitragen, das Pankreaskarzinom mit hoher diagnostischer Güte vom Normalgewebe abzugrenzen, mit Ausnahme von PS, das mit der Patlak-Methode gemessen wurde. · Perfusions-CT-Parameter zeigten eine hohe diagnostische Güte bei der Differenzierung zwischen Pankreaskarzinom und Normalgewebe.. · Nur PS, gemessen mit der Patlak-Methode, zeigte eine signifikant niedrigere diagnostische Güte.. · Perfusionsparameter, die mit unterschiedlichen mathematisch-kinetischen Methoden gemessen wurden, sind nicht austauschbar.. · Für jede Methode und jeden Perfusionsparameter muss ein spezifischer Cut-off-Wert bestimmt werden..

Background/objectives The aim of this study was to compare intravoxel incoherent motion (IVIM) diffusion weighted (DW) MRI and CT perfusion to assess tumor perfusion of pancreatic ductal adenocarcinoma (PDAC). Methods In this prospective study, DW-MRI and CT perfusion were conducted in nineteen patients with PDAC on the day before surgery. IVIM analysis of DW-MRI was performed and the parameters perfusion fraction f, pseudodiffusion coefficient D*, and diffusion coefficient D were extracted for tumors, upstream, and downstream parenchyma. With a deconvolution-based analysis, the CT perfusion parameters blood flow (BF) and blood volume (BV) were estimated for tumors, upstream, and downstream parenchyma. In ten patients, intratumoral microvessel density (MVDtumor) and microvessel area (MVAtumor) were analyzed microscopically in resection specimens. Correlation coefficients between IVIM parameters, CT perfusion parameters, and histological microvessel parameters in tumors were calculated. Receiver operating characteristic (ROC) analysis was performed for differentiation of tumors and upstream parenchyma. Results ftumor significantly positively correlated with BFtumor (r = 0.668, p = 0.002) and BVtumor (r = 0.672, p = 0.002). There were significant positive correlations between ftumor and MVDtumor/ MVAtumor (r ≥ 0.770, p ≤ 0.009) as well as between BFtumor and MVDtumor/ MVAtumor (r ≥ 0.697, p ≤ 0.025). Correlation coefficients between ftumor and MVDtumor/ MVAtumor were not significantly different from correlation coefficients between BFtumor and MVDtumor/ MVAtumor (p ≥ 0.400). Moreover, f, BF, BV, and permeability values (PEM) showed excellent performance in distinguishing tumors from upstream parenchyma (area under the ROC curve ≥0.874). Conclusions The study shows that IVIM derived ftumor and CT perfusion derived BFtumor similarly reflect vascularity of PDAC and seem to be comparably applicable for the evaluation of tumor perfusion for tumor characterization and as potential quantitative imaging biomarker. Trial registration DRKS, DRKS00022227, Registered 26 June 2020, retrospectively registered. https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00022227.

OBJECTIVE: Quantitative evaluation of different bolus tracking trigger delays for acquisition of dual energy (DE) CT iodine maps as an alternative to CT perfusion. METHODS: Prior to this retrospective analysis of prospectively acquired data, DECT perfusion sequences were dynamically acquired in 22 patients with pancreatic carcinoma using dual source CT at 80/140 kV(p) with tin filtration. After deformable motion-correction, perfusion maps of blood flow (BF) were calculated from 80 kV(p) image series of DECT, and iodine maps were calculated for each of the 34 DECT acquisitions per patient. BF and iodine concentrations were measured in healthy pancreatic tissue and carcinoma. To evaluate potential DECT acquisition triggered by bolus tracking, measured iodine concentrations from the 34 DECT acquisitions per patient corresponding to different trigger delays were assessed for correlation to BF and intergroup differences between tissue types depending on acquisition time. RESULTS: Average BF measured in healthy pancreatic tissue and carcinoma was 87.6 ± 28.4 and 38.6 ± 22.2 ml/100 ml min(–1), respectively. Correlation between iodine concentrations and BF was statistically significant for bolus tracking with trigger delay greater than 0 s (r(max) = 0.89; p < 0.05). Differences in iodine concentrations between healthy pancreatic tissue and carcinoma were statistically significant for DECT acquisitions corresponding to trigger delays of 15–21 s (p < 0.05). CONCLUSION: An acquisition window between 15 and 21 s after exceeding bolus tracking threshold shows promising results for acquisition of DECT iodine maps as an alternative to CT perfusion measurements of BF. ADVANCES IN KNOWLEDGE: After clinical validation, DECT iodine maps of pancreas acquired using bolus tracking with appropriate trigger delay as determined in this study could offer an alternative quantitative imaging biomarker providing functional information for tumor assessment at reduced patient radiation exposure compared to CT perfusion measurements of BF.

Perfusion CT is well-suited for diagnosis of pancreatic tumors but tends to be associated with a high radiation exposure. Dual-energy CT (DECT) might be an alternative to perfusion CT, offering correlating contrasts while being acquired at lower radiation doses. While previous studies compared intensities of Dual Energy iodine maps and CT-perfusion maps, no study has assessed the combined discriminative power of all information that can be generated from an acquisition of both functional imaging methods. We therefore propose the use of a machine learning algorithm for assessing the amount of information that becomes available by the combination of multiple images. For this, we train a classifier on both imaging methods, using a new approach that allows us to train only from small regions of interests (ROIs). This makes our study comparable to other - ROI-based analysis - and still allows comparing the ability of both classifiers to discriminate between healthy and tumorous tissue. We were able to train classifiers that yield DICE scores over 80% with both imaging methods. This indicates that Dual Energy Iodine maps might be used for diagnosis of pancreatic tumors instead of Perfusion CT, although the detection rate is lower. We also present tumor risk maps that visualize possible tumorous areas in an intuitive way and can be used during diagnosis as an additional information source.