The isoattenuating pancreatic adenocarcinoma: Review of the literature and critical analysis

Preliminary study on the ability of the machine learning models based on 18F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma

PURPOSE

The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to distinguish mass-forming pancreatic lymphoma from pancreatic carcinoma using machine learning.

METHODS

A total of 88 lesions from 86 patients diagnosed as mass-forming pancreatic lymphoma or pancreatic carcinoma were included and randomly divided into a training set and a validation set at a 4-to-1 ratio. The segmentation of regions of interest was performed using ITK-SNAP software, PET metabolic parameters and radiomics features were extracted using 3Dslicer and PYTHON. Following the selection of optimal metabolic parameters and radiomics features, Logistic regression (LR), support vector machine (SVM), and random forest (RF) models were constructed for PET metabolic parameters, CT radiomics, PET radiomics, and PET/CT radiomics. Model performance was assessed in terms of area under the curve (AUC), accuracy, sensitivity, and specificity in both the training and validation sets.

RESULTS

Strong discriminative ability observed in all models, with AUC values ranging from 0.727 to 0.978. The highest performance exhibited by the combined PET and CT radiomics features. AUC values for PET/CT radiomics models in the training set were LR 0.994, SVM 0.994, RF 0.989. In the validation set, AUC values were LR 0.909, SVM 0.883, RF 0.844.

CONCLUSION

Machine learning models utilizing the metabolic parameters and radiomics of 18F-FDG PET/CT show promise in distinguishing between pancreatic carcinoma and mass-forming pancreatic lymphoma. Further validation on a larger cohort is necessary before practical implementation in clinical settings.

Qualitative flow metabolic phenotype of pancreatic cancer. A new prognostic biomarker?

BACKGROUND

Retrospective analysis to investigate the relationship between the flow-metabolic phenotype and overall survival (OS) of pancreatic ductal adenocarcinoma (PDAC) and its potential clinical utility.

METHODS

Patients with histopathologically proven PDAC between 2005 and 2014 using tumor attenuation on routine pre-operative CECT as a surrogate for the vascularity and [18F]FDG-uptake as a surrogate for metabolic activity on [18F]FDG-PET.

RESULTS

In total, 93 patients (50 male, 43 female, median age 63) were included. Hypoattenuating PDAC with high [18F]FDG-uptake has the poorest prognosis (median OS 7 ± 1 months), compared to hypoattenuating PDAC with low [18F]FDG-uptake (median OS 11 ± 3 months; p = 0.176), iso- or hyperattenuating PDAC with high [18F]FDG-uptake (median OS 15 ± 5 months; p = 0.004) and iso- or hyperattenuating PDAC with low [18F]FDG-uptake (median OS 23 ± 4 months; p = 0.035). In multivariate analysis, surgery combined with tumor differentiation, tumor stage, systemic therapy and flow metabolic phenotype remained independent predictors for overall survival.

DISCUSSION

The novel qualitative flow-metabolic phenotype of PDAC using a combination of CECT and [18F]FDG-PET features, predicted significantly worse survival for hypoattenuating-high uptake pancreatic cancers compared to the other phenotypes.

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.

The role of abdominal ultrasonography in patients with isoattenuating pancreatic carcinoma

AIMS

The main objective of this study was to determine the sensitivity of abdominal ultrasonography (US) in patients with isoattenuating pancreatic carcinoma and to compare the frequency of secondary signs on abdominal US and endoscopic ultrasonography (EUS) in these tumours.

METHODS

Twenty-four patients with histologically or cytologically verified isoattenuating pancreatic carcinoma who underwent abdominal US, contrast-enhanced CT and EUS of the pancreas as part of the diagnostic workup were included in this retrospective study. The sensitivity of abdominal US in detecting the isoattenuating pancreatic carcinoma was investigated and the frequency of secondary signs of isoattenuating pancreatic carcinoma on abdominal US and EUS was compared.

RESULTS

In 5 of 24 patients (21%) with isoattenuating pancreatic carcinoma, a hypoechogenic pancreatic lesion was directly visualised on abdominal US. Secondary signs were present on US in 21 patients (88%). These included dilatation of the common bile duct and/or intrahepatic bile ducts in 19/24 (79%), dilatation of the pancreatic duct in 3/24 (13%), abnormal contour/inhomogeneity of the pancreas in 1/24 (4%), and atrophy of the distal parenchyma in 1/24 (4%). Pancreatic duct dilatation was observed more frequently on EUS than on abdominal US (P=0.002). For other secondary signs, there was no significant difference in their detection on abdominal US and EUS (P=0.61-1.00).

CONCLUSION

Abdominal US is capable of detecting secondary signs of isoattenuating pancreatic carcinoma with high sensitivity and has the potential to directly visualise these tumours.

Evaluation of the diagnostic performance of the EFSUMB CEUS Pancreatic Applications guidelines (2017 version): a retrospective single-center analysis of 455 solid pancreatic masses

OBJECTIVES

To explore the diagnostic performance of EFSUMB CEUS Pancreatic Applications guidelines (version 2017) before and after the addition of iso-enhancement and very fast/fast washout as supplementary diagnostic criteria for PDAC.

METHODS

In this retrospective study, patients diagnosed with solid pancreatic lesions from January 2017 to December 2020 were evaluated. Pancreatic ductal adenocarcinoma (PDAC) is reported to show hypo-enhancement in all phases according to the EFSUMB guidelines. First, based on this definition, all lesions were categorized as PDAC and non-PDAC. Then, iso-enhancement and very fast/fast washout were added as supplementary diagnostic criteria, and all lesions were recategorized. The diagnostic performance was assessed in terms of the accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV). The reference standard consisted of histologic evaluation or composite imaging and clinical follow-up findings.

RESULTS

A total of 455 nodules in 450 patients (median age, 58.37 years; 250 men) were included. The diagnostic performance using the EFSUMB CEUS guidelines for PDAC had an ACC of 69.5%, SEN of 65.4%, SPE of 84%, PPV of 93.5%, NPV of 40.6%, and ROC of 0.747. After recategorization according to the supplementary diagnostic criteria, the diagnostic performance for PDAC had an ACC of 95.8%, SEN of 99.2%, SPE of 84%, PPV of 95.7%, NPV of 96.6%, and ROC of 0.916.

CONCLUSION

The EFSUMB guidelines and recommendations for pancreatic lesions can effectively identify PDAC via hypo-enhancement on CEUS. However, the diagnostic performance may be further improved by the reclassification of PDAC lesions after adding iso-enhancement and very fast/fast washout mode.

KEY POINTS

• In the EFSUMB guidelines, the only diagnostic criterion for PDAC is hypo-enhancement, to which iso-enhancement and very fast/fast washout mode were added in our research. • Using hypo-enhancement/iso-enhancement with very fast/fast washout patterns as the diagnostic criteria for PDAC for solid pancreatic masses on CEUS has high diagnostic accuracy. • The blood supply pattern of PDAC can provide important information, and CEUS has unique advantages in this respect due to its real-time dynamic attenuation ability.

Value of Dual-Energy Computed Tomography for Detecting Small Pancreatic Ductal Adenocarcinoma

OBJECTIVE

The aim of the study is to evaluate the usefulness of virtual monoenergetic imaging (VMI) generated from dual-energy computed tomography (DECT) in detecting small pancreatic ductal adenocarcinomas (PDACs).

METHODS

This study included 82 patients pathologically diagnosed with small PDAC (≤30 mm) and 20 without pancreatic tumors who underwent triple-phase contrast-enhanced DECT. To assess diagnostic performance for small PDAC detection via a receiver operating characteristic analysis, 3 observers reviewed 2 image sets (conventional computed tomography [CT] set and combined image set [conventional CT + 40-keV VMI from DECT]). The tumor-to-pancreas contrast-to-noise ratio was compared between conventional CT and 40-keV VMI from DECT.

RESULTS

The area under the receiver operating characteristic curve of the 3 observers were 0.97, 0.96, and 0.97 in conventional CT set and 0.99, 0.99, and 0.99 in combined image set (P = 0.017-0.028), respectively. The combined image set yielded a better sensitivity than the conventional CT set (P = 0.001-0.023), without a loss of specificity (all P > 0.999). The tumor-to-pancreas contrast-to-noise ratios of 40-keV VMI from DECT were approximately threefold higher than those of conventional CT at all phases.

CONCLUSIONS

The addition of 40-keV VMI from DECT to conventional CT had better sensitivity for detecting small PDACs without compromising specificity.

Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Simple Summary

Early image-based diagnosis is crucial to improve outcomes in pancreatic ductal adenocarcinoma (PDAC) patients, but is challenging even for experienced radiologists. Artificial intelligence has the potential to assist in early diagnosis by leveraging high amounts of data to automatically detect small (<2 cm) lesions. In this study, the state-of-the-art, self-configuring framework for medical segmentation nnUnet was used to develop a fully automatic pipeline for the detection and localization of PDAC lesions on contrast-enhanced computed tomography scans, with a focus on small lesions. Furthermore, the impact of integrating the surrounding anatomy (which is known to be relevant to clinical diagnosis) into deep learning models was assessed. The developed automatic framework was tested in an external, publicly available test set, and the results showed that state-of-the-art deep learning can detect small PDAC lesions and benefits from anatomy information.

Abstract

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC), but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis; however, current models still fail to identify small (<2 cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating the surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), and (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2 cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.

Current trends in the diagnosis of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a dreaded malignancy with a dismal 5-year survival rate despite maximal efforts on optimizing treatment strategies. Currently, early detection is considered to be the most effective way to improve survival as radical resection is the only potential cure. PDAC is often divided into four categories based on the extent of disease: resectable, borderline resectable, locally advanced, and metastatic. Unfortunately, the majority of patients are diagnosed with locally advanced or metastatic disease, which renders them ineligible for curative resection. This is mainly due to the lack of or vague symptoms while the disease is still localized, although appropriate utilization and prompt availability of adequate diagnostic tools is also critical given the aggressive nature of the disease. A cost-effective biomarker with high specificity and sensitivity allowing early detection of PDAC without the need for advanced or invasive methods is still not available. This leaves the diagnosis dependent on radiodiagnostic methods or endoscopic ultrasound. Here we summarize the latest epidemiological data, risk factors, clinical manifestation, and current diagnostic trends and implications of PDAC focusing on serum biomarkers and imaging modalities. Additionally, up-to-date management and therapeutic algorithms are outlined.

Pancreas in Hereditary Syndromes: Cross-sectional Imaging Spectrum

A wide spectrum of hereditary syndromes predispose patients to distinct pancreatic abnormalities, including cystic lesions, recurrent pancreatitis, ductal adenocarcinoma, nonductal neoplasms, and parenchymal iron deposition. While pancreatic exocrine insufficiency and recurrent pancreatitis are common manifestations in cystic fibrosis and hereditary pancreatitis, pancreatic cysts are seen in von Hippel-Lindau disease, cystic fibrosis, autosomal dominant polycystic kidney disease, and McCune-Albright syndrome. Ductal adenocarcinoma can be seen in many syndromes, including Peutz-Jeghers syndrome, familial atypical multiple mole melanoma syndrome, Lynch syndrome, hereditary breast and ovarian cancer syndrome, Li-Fraumeni syndrome, and familial pancreatic cancer syndrome. Neuroendocrine tumors are commonly seen in multiple endocrine neoplasia type 1 syndrome and von Hippel-Lindau disease. Pancreatoblastoma is an essential component of Beckwith-Wiedemann syndrome. Primary hemochromatosis is characterized by pancreatic iron deposition. Pancreatic pathologic conditions associated with genetic syndromes exhibit characteristic imaging findings. Imaging plays a pivotal role in early detection of these conditions and can positively affect the clinical outcomes of those at risk for pancreatic malignancies. Awareness of the characteristic imaging features, imaging-based screening protocols, and surveillance guidelines is crucial for radiologists to guide appropriate patient management. ^©RSNA, 2021.

Improvement of the Diagnosis of Isoattenuating Pancreatic Carcinomas by Defining their Characteristics on Contrast Enhanced Computed Tomography and Endosonography with Fine-Needle Aspiration (EUS-FNA)

(1) Background. The aim was to define typical features of isoattenuating pancreatic carcinomas on computed tomography (CT) and endosonography and determine the yield of fine-needle aspiration endosonography (EUS-FNA) in their diagnosis. (2) Methods. One hundred and seventy-three patients with pancreatic carcinomas underwent multiphase contrast-enhanced CT followed by EUS-FNA at the time of diagnosis. Secondary signs on CT, size and location on EUS, and the yield of EUS-FNA in isoattenuating and hypoattenuating pancreatic cancer, were evaluated. (3) Results. Isoattenuating pancreatic carcinomas occurred in 12.1% of patients. Secondary signs of isoattenuating pancreatic carcinomas on CT were present in 95.2% cases and included dilatation of the pancreatic duct and/or the common bile duct (85.7%), interruption of the pancreatic duct (76.2%), abnormal pancreatic contour (33.3%), and atrophy of the distal parenchyma (9.5%) Compared to hypoattenuating pancreatic carcinomas, isoattenuating carcinomas were more often localized in the pancreatic head (100% vs. 59.2%; p < 0.001). In ROC (receiver operating characteristic) analysis, the optimal cut-off value for the size of isoattenuating carcinomas on EUS was ≤ 25 mm (AUC = 0.898). The sensitivity of EUS-FNA in confirmation of isoattenuating and hypoattenuating pancreatic cancer were 90.5% and 92.8% (p = 0.886). (4) Conclusions. Isoattenuating pancreatic head carcinoma can be revealed by indirect signs on CT and confirmed with high sensitivity by EUS-FNA.

Adding Delayed Phase Images to Dual-Phase Contrast-Enhanced CT Increases Sensitivity for Small Pancreatic Ductal Adenocarcinoma

BACKGROUND. Contrast-enhanced CT performed for pancreatic ductal adeno-carcinoma (PDAC) detection traditionally uses a dual-phase (pancreatic and portal venous) protocol. However, PDAC may exhibit isoattenuation in these phases, hindering detection. OBJECTIVE. The purpose of this study was to assess the impact on diagnostic performance in detection of small PDAC when a delayed phase is added to dual-phase contrast-enhanced CT. METHODS. A database of 571 patients who underwent triple-phase (pancreatic, portal venous, and delayed) contrast-enhanced MDCT between January 2017 and March 2020 for suspected pancreatic tumor was retrospectively reviewed. A total of 97 patients had pathologically confirmed small PDAC (mean size, 22 mm; range, 7-30 mm). Twenty control patients had no pancreatic tumor suspected on CT, on initial MRI and follow-up CT, or on MRI after 12 months or longer. Three radiologists independently reviewed dual-phase and triple-phase images. Two additional radiologists assessed tumors' visual attenuation on each phase, reaching consensus for differences. Performance of dual- and triple-phase images were compared using ROC analysis, McNemar test, and Fisher exact test. RESULTS. AUC was higher (p < .05) for triple-phase than dual-phase images for all observers (observer 1, 0.97 vs 0.94; observer 2, 0.97 vs 0.94; observer 3, 0.97 vs 0.95). Sensitivity was higher (p < .001) for triple-phase than dual-phase images for all observers (observer 1, 74.2% [72/97] vs 59.8% [58/97]; observer 2, 88.7% [86/97] vs 71.1% [69/97]; observer 3, 86.6% [84/97] vs 72.2% [70/97]). Specificity, PPV, and NPV did not differ between image sets for any reader (p ≥ .05). Seventeen tumors showed pancreatic phase visual isoattenuation, of which nine showed isoattenuation and eight hyperattenuation in the delayed phase. Of these 17 tumors, 16 were not detected by any observer on dual-phase images; of these 16, six were detected by at least two observers and five by at least one observer on triple-phase images. Visual attenuation showed excellent interob-server agreement (κ = 0.89-0.96). CONCLUSION. Addition of a delayed phase to pancreatic and portal venous phase CT increases sensitivity for small PDAC without loss of specificity, partly related to delayed phase hyperattenuation of some small PDACs showing pancreatic phase isoattenuation. CLINICAL IMPACT. Addition of a delayed phase may facilitate earlier PDAC detection and thus improved prognosis.

Prognostic factors in patients with locally advanced or borderline resectable pancreatic ductal adenocarcinoma: chemotherapy vs. chemoradiotherapy

PURPOSE

To identify common and unique pre-treatment prognostic factors in patients with borderline resectable (BR) or locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC), treated with chemotherapy (CTx) or concurrent chemoradiotherapy (CRT).

METHODS

We enrolled 215 patients with BR/LA PDAC, who were treated with either CTx (n = 82) or CRT (n = 133) as a first-line treatment between 2013 and 2016. Clinical data and CT imaging findings for predicting overall survival (OS) and progression-free survival (PFS) were analyzed using Cox regression analysis.

RESULTS

Carbohydrate antigen (CA) 19-9 > 1000 U/mL (hazard ratio [HR] 1.91; p = 0.001) and non-homogeneous enhancement (HR 1.95; p < 0.001) were associated with shorter OS in all study populations. There was no significant difference in median OS (15.3 vs 16.8 months, p = 0.297) and PFS (10.0 vs 11.7 months, p = 0.321) between the CTx and CRT groups. Non-homogeneous enhancement (HR 2.04; p = 0.006) and presence of positive lymph node on CT (HR 2.38; p = 0.036) were associated with poor OS in the CTx group, while CA 19-9 > 1000 U/mL (HR 2.38; p = 0.001) and non-homogeneous enhancement (HR 1.73; p = 0.006) were independent predictors for poor OS in the CRT group.

CONCLUSION

Enhancement pattern on CT was a common prognostic factor for patients with PDAC treated with either CTx or CRT. Presence of positive lymph nodes on CT was a poor prognostic factor for the CTx group only, whereas CA 19-9 > 1000 U/mL was a poor prognostic factor for the CRT group only.

Tumor conspicuity significantly correlates with postoperative recurrence in patients with pancreatic cancer: a retrospective observational study

BACKGROUND

There has been scanty data regarding the clinical significance of tumor conspicuity in pancreatic cancer. In this study, we attempted to investigate the prognostic significance of pancreatic tumor conspicuity and determine prognostic factors for postoperative recurrence in patients with surgically resected pancreatic cancer.

METHODS

Between January 2011 and September 2019, 62 patients who underwent preoperative computed tomography (CT) for pancreatic cancer were retrospectively included. Two reviewers evaluated various clinical, imaging, and pathologic variables and reviewed all available medical records to determine patient outcomes after surgery. Tumor conspicuity was defined as the attenuation ratio between normal parenchyma and tumor lesions on dynamic-enhanced CT images and represented the conspicuity score. Recurrence-free survival and overall survival were investigated using Cox regression analysis.

RESULTS

Patient mean age was 65.9 (±11.6) years, and 56.5% were male. The median follow-up period was 11 months (range 2-138). Forty patients (64.5%) experienced postoperative recurrence, and the median time to recurrence was 6 months (range 1-101). Tumor conspicuity scores were positively correlated with both radiologic and pathologic tumor sizes (r = 0.252, 0.321, p < 0.01). Conspicuity score ≥ 2 (HR 3.8, 95% CI 1.73-8.47), elevated preoperative (HR 1.15, 95% CI; 1.02-1.28) and postoperative CA19-9 (HR 1.11, 95% CI 1.01-1.23), pathologic tumor size (HR 1.61, 95% CI 1.06-2.45), and lymphatic invasion (HR 2.76, 95% CI 1.22-6.21) were significant factors for recurrence-free survival in the multivariate analysis.

CONCLUSIONS

Over half of the patients with pancreatic cancer experienced postoperative recurrence (64.5%). Increased tumor conspicuity correlated with larger tumor size and postoperative recurrence.

Pitfalls in the MDCT of pancreatic cancer: strategies for minimizing errors

Multidetector computed tomography (MDCT) is a widely used cross-sectional imaging modality for initial evaluation of patients with suspected pancreatic ductal adenocarcinoma (PDAC). However, diagnosis of PDAC can be challenging due to numerous pitfalls associated with image acquisition and interpretation, including technical factors, imaging features, and cognitive errors. Accurate diagnosis requires familiarity with these pitfalls, as these can be minimized using systematic strategies. Suboptimal acquisition protocols and other technical errors such as motion artifacts and incomplete anatomical coverage increase the risk of misdiagnosis. Interpretation of images can be challenging due to intrinsic tumor features (including small and isoenhancing masses, exophytic masses, subtle pancreatic duct irregularities, and diffuse tumor infiltration), presence of coexisting pathology (including chronic pancreatitis and intraductal papillary mucinous neoplasm), mimickers of PDAC (including focal fatty infiltration and focal pancreatitis), distracting findings, and satisfaction of search. Awareness of pitfalls associated with the diagnosis of PDAC along with the strategies to avoid them will help radiologists to minimize technical and interpretation errors. Cognizance and mitigation of these errors can lead to earlier PDAC diagnosis and ultimately improve patient prognosis.

Usefulness of non-contrast MR imaging in distinguishing pancreatic ductal adenocarcinoma from focal pancreatitis

BACKGROUND

Accurate differentiation between pancreatic adenocarcinoma and focal pancreatitis is challenging.

PURPOSE

To investigate the usefulness of non-contrast MRI by comparing with multidetector row CT (MDCT) and gadoxetic acid-enhanced MRI in the discrimination of pancreatic ductal adenocarcinoma (PDAC) and focal pancreatitis (FP).

MATERIALS AND METHODS

This retrospective study included 187 patients (116 with PDACs and 71 with FP) who underwent gadoxetic acid-MRI and MDCT prior to surgical resection or biopsy. The MRI features of PDAC and FP were compared by two radiologists. Then, two observers independently reviewed the three imaging sets: MDCT, non-contrast MRI (T1-, T2-weighted, and diffusion-weighted images), and MRI with and without gadoxetic acid to determine the diagnostic performances of each imaging modality in the discrimination of PDAC and FP.

RESULTS

The significant features on non-contrast MRI for diagnosis of PDAC included peritumoral cyst, pancreatic duct cut-off, clear hypointensity on T1WI, and bile duct dilatation (P < 0.05). Presence of peritumoural cyst showed the highest odds ratio for predicting PDAC. Non-contrast MRI was superior to MDCT in differentiating PDAC from FP with regard to accuracy (84.5% vs 95.5% for observer 1; 85.8% vs. 96.0% for observer 2), sensitivity (83.6% vs. 98.3%; 84.5% vs 97.8%), and negative predictive value (76.3% vs. 97.0%; 77.6% vs 96.4%) (P < 0.05). We found similar diagnostic values between the non-contrast MRI and MRI with and without contrast (P > 0.05) for both observers.

CONCLUSION

Non-contrast MRI is better than MDCT and comparable to MRI with and without gadoxetic acid in differentiating PDAC from FP.