Perfusion CT to Assess Response to Neoadjuvant Chemotherapy and Radiation Therapy in Pancreatic Ductal Adenocarcinoma: Initial Experience

Automated Quantitative Analysis of CT Perfusion to Classify Vascular Phenotypes of Pancreatic Ductal Adenocarcinoma

CT perfusion (CTP) analysis is difficult to implement in clinical practice. Therefore, we investigated a novel semi-automated CTP AI biomarker and applied it to identify vascular phenotypes of pancreatic ductal adenocarcinoma (PDAC) and evaluate their association with overall survival (OS).

METHODS

From January 2018 to November 2022, 107 PDAC patients were prospectively included, who needed to undergo CTP and a diagnostic contrast-enhanced CT (CECT). We developed a semi-automated CTP AI biomarker, through a process that involved deformable image registration, a deep learning segmentation model of tumor and pancreas parenchyma volume, and a trilinear non-parametric CTP curve model to extract the enhancement slope and peak enhancement in segmented tumors and pancreas. The biomarker was validated in terms of its use to predict vascular phenotypes and their association with OS. A receiver operating characteristic (ROC) analysis with five-fold cross-validation was performed. OS was assessed with Kaplan-Meier curves. Differences between phenotypes were tested using the Mann-Whitney U test.

RESULTS

The final analysis included 92 patients, in whom 20 tumors (21%) were visually isovascular. The AI biomarker effectively discriminated tumor types, and isovascular tumors showed higher enhancement slopes (2.9 Hounsfield unit HU/s vs. 2.0 HU/s, p < 0.001) and peak enhancement (70 HU vs. 47 HU, p < 0.001); the AUC was 0.86. The AI biomarker's vascular phenotype significantly differed in OS (p < 0.01).

CONCLUSIONS

The AI biomarker offers a promising tool for robust CTP analysis. In PDAC, it can distinguish vascular phenotypes with significant OS prognostication.

Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma

OBJECTIVE

To develop and evaluate task-based radiomic features extracted from the mesenteric-portal axis for prediction of survival and response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Consecutive patients with PDAC who underwent surgery after neoadjuvant therapy from two academic hospitals between December 2012 and June 2018 were retrospectively included. Two radiologists performed a volumetric segmentation of PDAC and mesenteric-portal axis (MPA) using a segmentation software on CT scans before (CTtp0) and after (CTtp1) neoadjuvant therapy. Segmentation masks were resampled into uniform 0.625-mm voxels to develop task-based morphologic features (n = 57). These features aimed to assess MPA shape, MPA narrowing, changes in shape and diameter between CTtp0 and CTtp1, and length of MPA segment affected by the tumor. A Kaplan-Meier curve was generated to estimate the survival function. To identify reliable radiomic features associated with survival, a Cox proportional hazards model was used. Features with an ICC  ≥ 0.80 were used as candidate variables, with clinical features included a priori.

RESULTS

In total, 107 patients (60 men) were included. The median survival time was 895 days (95% CI: 717, 1061). Three task-based shape radiomic features (Eccentricity mean tp0, Area minimum value tp1, and Ratio 2 minor tp1) were selected. The model showed an integrated AUC of 0.72 for prediction of survival. The hazard ratio for the Area minimum value tp1 feature was 1.78 (p = 0.02) and 0.48 for the Ratio 2 minor tp1 feature (p = 0.002).

CONCLUSION

Preliminary results suggest that task-based shape radiomic features can predict survival in PDAC patients.

KEY POINTS

• In a retrospective study of 107 patients who underwent neoadjuvant therapy followed by surgery for PDAC, task-based shape radiomic features were extracted and analyzed from the mesenteric-portal axis. • A Cox proportional hazards model that included three selected radiomic features plus clinical information showed an integrated AUC of 0.72 for prediction of survival, and a better fit compared to the model with only clinical information.

Pancreatic CT perfusion: quantitative meta-analysis of disease discrimination, protocol development, and effect of CT parameters

BACKGROUND

This study provides a quantitative meta-analysis of pancreatic CT perfusion studies, investigating choice of study parameters, ability for quantitative discrimination of pancreatic diseases, and influence of acquisition and reconstruction parameters on reported results.

METHODS

Based on a PubMed search with key terms 'pancreas' or 'pancreatic,' 'dynamic' or 'perfusion,' and 'computed tomography' or 'CT,' 491 articles published between 1982 and 2020 were screened for inclusion in the study. Inclusion criteria were: reported original data, human subjects, five or more datasets, measurements of pancreas or pancreatic pathologies, and reported quantitative perfusion parameters. Study parameters and reported quantitative measurements were extracted, and heterogeneity of study parameters and trends over time are analyzed. Pooled data were tested with weighted ANOVA and ANCOVA models for differences in perfusion results between normal pancreas, pancreatitis, PDAC (pancreatic ductal adenocarcinoma), and non-PDAC (e.g., neuroendocrine tumors, insulinomas) and based on study parameters.

RESULTS

Reported acquisition parameters were heterogeneous, except for contrast agent amount and injection rate. Tube potential and slice thickness decreased, whereas tube current time product and scan coverage increased over time. Blood flow and blood volume showed significant differences between pathologies (both p < 0.001), unlike permeability (p = 0.11). Study parameters showed a significant effect on reported quantitative measurements (p < 0.05).

CONCLUSIONS

Significant differences in perfusion measurements between pathologies could be shown for pooled data despite observed heterogeneity in study parameters. Statistical analysis indicates most influential parameters for future optimization and standardization of acquisition protocols.

CRITICAL RELEVANCE STATEMENT

Quantitative CT perfusion enables differentiation of pancreatic pathologies despite the heterogeneity of study parameters in current clinical practice.

Dynamic Contrast-Enhanced Ultrasound Modeling of an Analog to Pseudo-Diffusivity in Intravoxel Incoherent Motion Magnetic Resonance Imaging

Tumor perfusion and vascular properties are important determinants of cancer response to therapy and thus various approaches for imaging perfusion are being explored. In particular, Intravoxel Incoherent Motion (IVIM) MRI has been actively researched as an alternative to Dynamic-Contrast-Enhanced (DCE) CT and DCE-MRI as it offers non-ionizing, non-contrast-based perfusion imaging. However, for repetitive treatment assessment in a short time period, high cost, limited access, and inability to scan at the bedside remain disadvantages of IVIM MRI. We propose an analysis framework that may enable 3D DCE Ultrasound (DCE-US) - low cost, bedside imaging with excellent safety record - as an alternative modality to IVIM MRI for the generation of DCE-US based pseudo-diffusivity maps in acoustically accessible anatomy and tumors. Modelling intravascular contrast propagation as a convective-diffusive process, we reconstruct parametric maps of pseudo-diffusivity by solving a large-scale fully coupled inverse problem without any assumptions regarding local constancy of the reconstructed parameters. In a mouse tumor model, we demonstrate that the 3D DCE-US pseudo-diffusivity is repeatable, sensitive to treatment with an antiangiogenic agent, and moderately correlated to histological measures of perfusion and angiogenesis.

CT perfusion as a potential biomarker for pancreatic ductal adenocarcinoma during routine staging and restaging

PURPOSE

To evaluate the significance of CT perfusion parameters predicting response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Seventy patients with PDAC prospectively had CT perfusion acquisition incorporated into baseline multiphase staging CT. Twenty-eight who were naïve to therapy were retained for further investigation. Perfusion was performed 5-42.5 s after contrast, followed by parenchymal and portal venous phases. Blood flow (BF), blood volume (BV), and permeability surface area product (PS) were calculated using deconvolution algorithms. Patients were categorized as responders or non-responders per RECIST 1.1. Perfusion variables with AUC ≥ 0.70 in differentiating responders from non-responders were retained. Logistic regression was used to assess associations between baseline perfusion variables and response.

RESULTS

18 of 28 patients showed favorable response to therapy. Baseline heterogeneity variables in tumor max ROI were higher in non-responders than responders [median BF coefficient of variation (CV) 0.91 vs. 0.51 respectively, odds ratio (OR) 6.8 per one standard deviation (1-SD) increase, P = 0.047; median PS CV 1.6 vs. 0.68, OR 3.9 per 1-SD increase, P = 0.047; and median BV CV 0.75 vs. 0.54, OR = 4.0 per 1-SD increase, P = 0.047]. Baseline BV mean in tumor center was lower in non-responders than responders (median BV mean: 0.74 vs. 2.9 ml/100 g respectively, OR 0.28 per 1-SD increase, P = 0.047).

CONCLUSION

For patients with PDAC receiving neoadjuvant therapy, lower and more heterogeneous perfusion parameters correlated with an unfavorable response to therapy. Such quantitative information can be acquired utilizing a comprehensive protocol interleaving perfusion CT acquisition with standard of care multiphase CT scans using a single contrast injection, which could be used to identify surgical candidates and predict outcome.

Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine-induced hypo-vascularization

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis.

METHODS AND MATERIALS

Regional CA accumulation in PDAC was correlated with tumor vascularization, stroma content, and tumor cellularity in murine and human subjects. Changes in CA distribution in response to gemcitabine (GEM) were monitored longitudinally with computed tomography (CT) Hounsfield Units ratio (HUr) of tumor to the aorta or with magnetic resonance imaging (MRI) ΔR₁ area under the curve at 60 s tumor-to-muscle ratio (AUC60r). Tissue analyses were performed on co-registered samples, including endothelial cell proliferation and cisplatin tissue deposition as a surrogate of chemotherapy delivery.

RESULTS

Tumor cell poor, stroma-rich regions exhibited high CA accumulation both in human (meanHUr 0.64 vs. 0.34, p < 0.001) and mouse PDAC (meanAUC60r 2.0 vs. 1.1, p < 0.001). Compared to the baseline, in vivo CA accumulation decreased specifically in response to GEM treatment in a subset of human (HUr -18%) and mouse (AUC60r -36%) tumors. Ex vivo analyses of mPDAC showed reduced cisplatin delivery (GEM: 0.92 ± 0.5 mg/g, vs. vehicle: 3.1 ± 1.5 mg/g, p = 0.004) and diminished endothelial cell proliferation (GEM: 22.3% vs. vehicle: 30.9%, p = 0.002) upon GEM administration.

CONCLUSION

In PDAC, CA accumulation, which is related to tumor vascularization and perfusion, inversely correlates with tumor cellularity. The standard of care GEM treatment results in decreased CA accumulation, which impedes drug delivery. Further investigation is warranted into potentially detrimental effects of GEM in combinatorial therapy regimens.

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death with a 5-year survival rate of 10%. Quantitative CT perfusion (CTP) can provide additional diagnostic information compared to the limited accuracy of the current standard, contrast-enhanced CT (CECT). This systematic review evaluates CTP for diagnosis, grading, and treatment assessment of PDAC. The secondary goal is to provide an overview of scan protocols and perfusion models used for CTP in PDAC. The search strategy combined synonyms for 'CTP' and 'PDAC.' Pubmed, Embase, and Web of Science were systematically searched from January 2000 to December 2020 for studies using CTP to evaluate PDAC. The risk of bias was assessed using QUADAS-2. 607 abstracts were screened, of which 29 were selected for full-text eligibility. 21 studies were included in the final analysis with a total of 760 patients. All studies comparing PDAC with non-tumorous parenchyma found significant CTP-based differences in blood flow (BF) and blood volume (BV). Two studies found significant differences between pathological grades. Two other studies showed that BF could predict neoadjuvant treatment response. A wide variety in kinetic models and acquisition protocol was found among included studies. Quantitative CTP shows a potential benefit in PDAC diagnosis and can serve as a tool for pathological grading and treatment assessment; however, clinical evidence is still limited. To improve clinical use, standardized acquisition and reconstruction parameters are necessary for interchangeability of the perfusion parameters.

Computation of contrast-enhanced perfusion using only two CT scan phases: a proof-of-concept study on abdominal organs

Background

Computed tomography perfusion imaging (CTPI) by repeated scanning has clinical relevance but implies relatively high radiation exposure. We present a method to measure perfusion from two CT scan phases only, considering tissue enhancement, feeding vessel (aortic) peak enhancement, and bolus shape.

Methods

CTPI scans (each with 40 frames acquired every 1.5 s) of 11 patients with advanced hepatocellular carcinoma (HCC) enrolled between 2012 and 2016 were retrospectively analysed (aged 69 ± 9 years, 8/11 males). Perfusion was defined as the maximal slope of the time-enhancement curve divided by the peak enhancement of the feeding vessel (aorta). Perfusion was computed two times, first using the maximum slope derived from all data points and then using the peak tissue enhancement and the bolus shape obtained from the aortic curve.

Results

Perfusion values from the two methods were linearly related (r2 = 0.92, p < 0.001; Bland–Altman analysis bias -0.12). The mathematical model showed that the perfusion ratio of two ROIs with the same feeding vessel (aorta) corresponds to their peak enhancement ratio (r2 = 0.55, p < 0.001; Bland–Altman analysis bias -0.68). The relationship between perfusion and tissue enhancement is predicted to be linear in the clinical range of interest, being only function of perfusion, peak feeding vessel enhancement, and bolus shape.

Conclusions

This proof-of-concept study showed that perfusion values of HCC, kidney, and pancreas could be computed using enhancement measured only with two CT scan phases, if aortic peak enhancement and bolus shape are known.

Comparison of multiphase data from CT perfusion vs clinical 4-phase CT scans with respect to image quality, lesion detection, and LI-RADS classification in HCC patients

Purpose

The aim of this study was to assess the image quality and diagnostic performance of reconstructed arterial (A) and portal venous (PV) phases in computed tomography perfusion (CTP) scans compared to the corresponding phases in standard 4-phase CT and to assess the utility for LI-RADS classification using CTP and 4-phase 4CT.

Methods

A total of 26 scans with each method (CTP and 4-phase CT) from 19 hepatocellular carcinoma patients were analyzed and compared. Arterial and PV phases reconstructed by advanced modeled iterative reconstruction at strength 4 (ADMIRE 4) from raw CTP data were compared with image sets from arterial and PV phases of 4-phase CT (ADMIRE 3) in the same patient with respect to image quality.

Results

Quantitative image analysis showed that reconstructed CTP datasets were equivalent to 4-phase CT image sets. Qualitative image analysis revealed similar lesion detection rates with the 2 methods for patients with an abdominal diameter ≤36 cm and body weight <90 kg, but lower detection rates with CTP for patients with an abdominal diameter >37 cm. There was no difference in Liver Imaging Reporting and Data System (LI-RADS) classifications between the 2 methods.

Conclusion

Reconstructed CTP images can potentially replace 4-phase CT images in patients weighing <90 kg and with a body diameter <37 cm, as the 2 methods are comparable in terms of quantitative image quality and ability to detect and classify lesions based on LI-RADS criteria.

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Reconstructed A- and PV-CTP images have comparable image quality to 4-phase CT.

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Reconstructed A- and PV-CTP images can be used for LI-RADS classification of HCC.

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A-/PV-CTP has the potential to reliably detect lesions in patients weighing <90 kg with body diameter ≤36 cm.

A Systematic Review on the Role of the Perfusion Computed Tomography in Abdominal Cancer

Background and purpose

Perfusion Computed Tomography (CTp) is an imaging technique which allows quantitative and qualitative evaluation of tissue perfusion through dynamic CT acquisitions. Since CTp is still considered a research tool in the field of abdominal imaging, the aim of this work is to provide a systematic summary of the current literature on CTp in the abdominal region to clarify the role of this technique for abdominal cancer applications.

Materials and Methods

A systematic literature search of PubMed, Web of Science, and Scopus was performed to identify original articles involving the use of CTp for clinical applications in abdominal cancer since 2011. Studies were included if they reported original data on CTp and investigated the clinical applications of CTp in abdominal cancer.

Results

Fifty-seven studies were finally included in the study. Most of the included articles (33/57) dealt with CTp at the level of the liver, while a low number of studies investigated CTp for oncologic diseases involving UGI tract (8/57), pancreas (8/57), kidneys (3/57), and colon–rectum (5/57).

Conclusions

Our study revealed that CTp could be a valuable functional imaging tool in the field of abdominal oncology, particularly as a biomarker for monitoring the response to anti-tumoral treatment.

Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma: Role of the Radiologist and Oncologist in the Era of Precision Medicine

The incidence and mortality of pancreatic ductal adenocarcinoma are growing over time. The management of patients with pancreatic ductal adenocarcinoma involves a multidisciplinary team, ideally involving experts from surgery, diagnostic imaging, interventional endoscopy, medical oncology, radiation oncology, pathology, geriatric medicine, and palliative care. An adequate staging of pancreatic ductal adenocarcinoma and re-assessment of the tumor after neoadjuvant therapy allows the multidisciplinary team to choose the most appropriate treatment for the patient. This review article discusses advancement in the molecular basis of pancreatic ductal adenocarcinoma, diagnostic tools available for staging and tumor response assessment, and management of resectable or borderline resectable pancreatic cancer.

Estimated tumor blood flow as a predictive imaging indicator of therapeutic response in pancreatic ductal adenocarcinoma: use of three-phase contrast-enhanced computed tomography

BACKGROUND

Preoperative chemotherapy or chemoradiotherapy is a common strategy for treating pancreatic ductal adenocarcinoma (PDAC). This study aimed to assess the association between the therapeutic response in PDAC and tumor blood circulation.

METHODS

The medical records of patients who underwent chemotherapy or chemoradiotherapy prior to pancreatectomy for PDAC were reviewed. Of these, patient data that included three-phase contrast-enhanced computed tomography (CECT) findings before treatments were used in this study. We evaluated the estimated tumor blood flow (eTBF) using CECT. According to the therapeutic histopathological response defined by the Evans classification, patients were divided into poor (grade I/IIa) and good (grade IIb/III/IV) responder groups. The variables, including eTBF, were compared between the two groups.

RESULTS

Thirty patients were enrolled in this study. Of these, 13 (43.3%) (grade IIB/III/IV: 8/4/1 patients) were categorized into the good responder group and 17 patients (56.7%) (grade I/IIA: 4/13 patients) were categorized into the poor responder group. eTBF was significantly higher in the good responder group (0.39 s^-1 vs. 0.32 s^-1, p = 0.007). An eTBF ≥ 0.36 s^-1 was found to be an independent predictive factor for the destruction of over 50% of tumor cells (p = 0.036; odds ratio, 9.71; 95% confidence interval, 1.16-81.30).

CONCLUSIONS

eTBF can be used to predict the therapeutic histopathological response in PDAC prior to treatment.

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

PURPOSE

Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients.

METHOD

In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed.

RESULTS

In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations.

CONCLUSION

IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Role of imaging in evaluating the response after neoadjuvant treatment for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Despite the development of multimodality treatments, including surgical resection, radiotherapy, and chemotherapy, the long-term prognosis of patients with PDAC remains poor. Recently, the introduction of neoadjuvant treatment (NAT) has made more patients amenable to surgery, increasing the possibility of R0 resection, treatment of occult micro-metastasis, and prolongation of overall survival. Imaging plays a vital role in tumor response evaluation after NAT. However, conventional imaging modalities such as multidetector computed tomography have limited roles in the assessment of tumor resectability after NAT for PDAC because of the similar appearance of tissue fibrosis and tumor infiltration. Perfusion computed tomography, using blood perfusion as a biomarker, provides added value in predicting the histopathologic response of PDAC to NAT by reflecting the changes in tumor matrix and fibrosis content. Other imaging technologies, including diffusion-weighted imaging of magnetic resonance imaging and positron emission tomography, can reveal the tumor response by monitoring the structural changes in tumor cells and functional metabolic changes in tumors after NAT. In addition, with the renewed interest in data acquisition and analysis, texture analysis and radiomics have shown potential for the early evaluation of the response to NAT, thus improving patient stratification to achieve accurate and intensive treatment. In this review, we briefly introduce the application and value of NAT in resectable and unresectable PDAC. We also summarize the role of imaging in evaluating the response to NAT for PDAC, as well as the advantages, limitations, and future development directions of current imaging techniques.

Feasibility of wide detector CT perfusion imaging performed during routine staging and restaging of pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the feasibility of CT perfusion performed during routine multiphase contrast-enhanced CT on a 160 mm wide-coverage 256-slice scanner in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Fifty-seven patients had a CT perfusion acquisition during their routine multiphase CT. Perfusion was performed 5 to 42.5 s (15 passes at 2.5 s intervals) after intravenous contrast administration (4.2-5 ml/s), followed by pancreatic parenchymal and portal venous phases for clinical interpretation. Perfusion maps were generated and blood flow (BF), blood volume (BV), and permeability surface area product (PS) for tumor and uninvolved pancreas were calculated using deconvolution algorithms and compared to existing similar publications. Radiation dose information was recorded and size-specific dose estimate (SSDE) was calculated using body dimensions.

RESULTS

Diagnostic quality of standard images was unaffected by performing the perfusion acquisition. Average tumor center BF was 20.8 ± 12.1 ml/100 g/min, BV 2.5 ± 2.1 ml/100 g and PS 15.5 ± 39.4 ml/100 g/min. Average pancreas BF was 90.8 ± 50.2 ml/100 g/min, BV 11.9 ± 4.3 ml/100 g and PS 33.6 ± 27.7 ml/100 g/min. For the perfusion acquisition, mean SSDE was 57 ± 11 mGy, CTDI_vol 43 ± 6 mGy and DLP 685 ± 100 mGy-cm.

CONCLUSION

Adding a perfusion CT acquisition to standard pancreatic CT protocol is feasible using a wide-detector 256-slice CT scanner and adds quantitative information while maintaining diagnostic quality of the standard of care examination. This novel protocol adds no time or cost to the examination and yields perfusion parameters that are comparable to existing literature using a separate dedicated perfusion protocol.

Pancreatic perfusion imaging method that reduces radiation dose and maintains image quality by combining volumetric perfusion CT with multiphasic contrast enhanced-CT

OBJECTIVES

The aim of this study is to propose and evaluate a new method of volumetric perfusion computed tomography (PCT) incorporated into pancreatic multiphasic contrast enhanced (CE)-CT in the clinical setting.

METHODS

In this ethically approved study, PCT was incorporated into our existing scanning protocol in 17 patients and effective doses related to PCT were evaluated. CT values and signal-to-noise ratio (SNR) of anatomical structure were compared in diagnostic images that were acquired using 320-detector volumetric scan mode and 64-detector helical scan mode. In addition, focal lesion depiction was qualitatively assessed in the two groups. Perfusion parameters in normal pancreas were measured by two radiologists and the interobserver-reliability was assessed.

RESULTS

The effective dose of PCT was 5.1 ± 0.3 mSv. The actual effective dose (AED) including the dose used in volumetric scans for diagnostic imaging was 22.8 ± 5.3 mSv and the putative effective dose (PED) was 21.9 ± 9.1 mSv on average. There was no significant difference between AED and PED (p = 0.404). Compared with conventional helical scans, volumetric scans did not decrease CT values or SNR, but rather significantly increased those of the aorta in the arterial phase. Both groups had acceptable qualitatively assessed image quality with no significant difference in the depiction of each structure. There was almost perfect interobserver agreement in the measurement of perfusion parameters (mean ICCs > 0.9).

CONCLUSIONS

Our scanning protocol for pancreatic perfusion CT provides high-quality images while requiring lower radiation doses than conventional methods.

Visual enhancement pattern during the delayed phase of enhanced CT as an independent prognostic factor in stage IV pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has substantial heterogeneity in biophysical features and in outcomes of patients. Identifying reliable pretreatment imaging biomarkers for PDAC with distant metastases (stage IV) is a key imperative. Our objective was to determine whether visual tumor enhancement pattern on enhanced computed tomography (CT) can be used as a prognostic factor in stage IV PDAC treated with chemotherapy.

METHODS

This is a retrospective cohort study of 133 patients with stage IV PDAC who underwent multiphasic enhanced CT before systemic chemotherapy. The enhancement pattern of PDAC was qualitatively categorized as hypoattenuation, isoattenuation, or hyperattenuation on each of the pancreatic, portal venous, and delayed phases. The effects of clinical prognostic factors and the visual tumor enhancement pattern on progression-free survival (PFS) and overall survival (OS) were assessed in univariate and multivariate analyses using Cox proportional hazards models.

RESULTS

On univariate analysis, the number of metastatic organs and the visual tumor enhancement pattern during the delayed phase were significantly associated with PFS (p = 0.003 and < 0.001, respectively) and OS (p = 0.005 and < 0.001, respectively). Multivariate analysis identified the number of metastatic organs (PFS, p = 0.021; OS, p = 0.041) and visual tumor enhancement pattern during the delayed phase (PFS, p < 0.001; OS, p < 0.001) as independent predictors of PFS and OS.

CONCLUSION

Visual enhancement pattern of PDAC on delayed phase enhanced CT appears to be associated with outcomes and could be a useful prognostic factor in stage IV PDAC, despite the need to add the delayed phase to CT protocol for pancreatic disease.

Multiparametric MRI for prediction of treatment response to neoadjuvant FOLFIRINOX therapy in borderline resectable or locally advanced pancreatic cancer

OBJECTIVES

To identify multiparametric MRI biomarkers to predict the tumor response to neoadjuvant FOLFIRINOX therapy in patients with borderline resectable (BR) or locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC).

METHODS

From May 2016 to March 2018, adult patients with BR or LA PDAC were prospectively enrolled in this study. They received eight cycles of FOLFIRINOX therapy and underwent multiparametric MRI twice (at baseline and after the second cycle). MRI evaluations included dynamic contrast-enhanced MRI, intravoxel incoherent motion diffusion-weighted imaging, and assessment of T2* relaxivity (R2*) and the change in T1 relaxivity (ΔR1, equilibrium phase R1 minus non-enhanced R1) of the tumors. Factors to predict the responders determined by the best overall response during FOLFIRINOX therapy and those to predict progression-free survival (PFS) and overall survival (OS) were evaluated using multivariable logistic regression and the Cox proportional hazard model.

RESULTS

Forty-one patients (mean age, 60.3 years ± 9.3; 24 men) were included. Among the clinical and MRI factors, the baseline ΔR1 (adjusted odds ratio, 31.07; p = 0.008) was the only independent predictor for tumor response. The baseline ΔR1 was also an independent predictor for PFS (adjusted hazard ratio, 0.40; p = 0.033) along with R0 resection. The use of a cutoff ΔR1 value of ≥ 1.31 s^-1 enabled prognostic stratification (median PFS, 16.0 months vs.10.0 months; p = 0.029; median OS, 34.9 months vs. 16.6 months; p = 0 .023, respectively).

CONCLUSIONS

The baseline tumor ΔR1 value may be useful to predict tumor response and survival in patients with BR or LA PDAC receiving FOLFIRINOX neoadjuvant therapy.

KEY POINTS

• Baseline ΔR1 was an independent predictor for tumor response (adjusted odds ratio, 31.07; p = 0.008) and progression-free survival (adjusted hazard ratio, 0.40; p = 0.033) in patients with borderline resectable or locally advanced pancreatic ductal adenocarcinoma receiving neoadjuvant FOLFIRINOX therapy. • The criterion of baseline ΔR1 value ≥ 1.31 s^-1 allowed for the prediction of favorable tumor response and survival outcome after neoadjuvant FOLFIRINOX therapy.

Iterative reconstruction algorithm improves the image quality without affecting quantitative measurements of computed tomography perfusion in the upper abdomen

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Iterative image-reconstruction algorithm (ADMIRE) did not affect the quantitative measurements in CT perfusion.

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Iterative image-reconstruction algorithm (ADMIRE) did not affect the time attenuation curves in CT perfusion.

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Image noise was lower, but the SNR was higher, for iterative reconstructions in CT perfusion examinations with higher strength of noise reduction.

Objective

To investigate differences between reconstruction algorithms in quantitative perfusion values and time-attenuation curves in computed tomography perfusion (CTP) examinations of the upper abdomen.

Methods

Twenty-six CTP examinations were reconstructed with filtered back projection and an iterative reconstruction algorithm, advanced modeled iterative reconstruction (ADMIRE), with different levels of noise-reduction strength. Using the maximum-slope model, quantitative measurements were obtained: blood flow (mL/min/100 mL), blood volume (mL/100 mL), time to peak (s), arterial liver perfusion (mL/100 mL/min), portal venous liver perfusion (mL/100 mL/min), hepatic perfusion index (%), temporal maximum intensity projection (Hounsfield units (HU)) and temporal average HU. Time-attenuation curves for seven sites (left liver lobe, right liver lobe, hepatocellular carcinoma, spleen, gastric wall, pancreas, portal vein) were obtained. Mixed-model analysis was used for statistical evaluation. Image noise and the signal:noise ratio (SNR) were compared between four reconstructions, and statistical analysis of these reconstructions was made with a related-samples Friedman’s two-way analysis of variance by ranks test.

Results

There were no significant differences for quantitative measurements between the four reconstructions for all tissues. There were no significant differences between the AUC values of the time-attenuation curves between the four reconstructions for all tissues, including three automatic measurements (portal vein, aorta, spleen). There was a significant difference in image noise and SNR between the four reconstructions.

Conclusions

ADMIRE did not affect the quantitative measurements or time-attenuation curves of tissues in the upper abdomen. The image noise was lower, and the SNR higher, for iterative reconstructions with higher noise-reduction strengths.