MRI features of serous oligocystic adenoma of the pancreas: differentiation from mucinous cystic neoplasm of the pancreas

Differentiation of Solid Pseudopapillary Tumor and Non-Functional Neuroendocrine Tumors of the Pancreas Based on CT Delayed Imaging: A Propensity Score Analysis

PURPOSE

To evaluate the diagnostic performance of the delayed-phase difference between tumor and pancreas for differentiating solid pseudopapillary tumors (SPTs) from non-functional neuroendocrine tumors (NF-NETs) of the pancreas.

METHODS

This retrospective review included 148 consecutive patients with SPT and 98 consecutive patients with NF-NET confirmed by pathology. Patients with SPT and NF-NET were matched via propensity score matching (PSM). All patients underwent multidetector computed tomography (MDCT). For each patient, the delayed-phase difference between the tumor and pancreas was measured, and the performance of this variable was assessed based on its discriminative ability and clinical utility.

RESULTS

After PSM, 27 patients with SPT and 27 patients with NF-NET were included in the matched analysis. There were no statistically significant differences in clinical and CT characteristics between the resulting two groups (p > 0.05). The delayed-phase difference values between the tumor and pancreas were significantly lower in patients with SPT (median: -0.45; range: -2.05 to 0.73) than in patients with NF-NET (median: 0.71; range: -1.39 to 2.38). The delayed-phase difference between tumor and pancreas had a high diagnostic accuracy (area under the curve=0.88). The best cutoff point based on maximizing the sum of the sensitivity and specificity was -0.23 (sensitivity = 88.89%; specificity = 88.89%; accuracy = 0.89).

CONCLUSIONS

The delayed-phase difference between tumor and pancreas can accurately and noninvasively differentiate SPT from NF-NET.

Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis

Objective

To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs).

Materials and Methods

Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity.

Results

Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively.

Conclusion

This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm

OBJECTIVES

To develop and validate a nomogram for the preoperative prediction of pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) based on multidetector computed tomography (MDCT).

MATERIALS AND METHODS

In this retrospective study, the data of 227 patients with SCN and MCN were analyzed. Each patient underwent MDCT and surgical resection. A multivariable logistic regression model was developed using a training set consisting of 129 patients with SCN and 38 patients with MCN who were admitted between October 2012 and April 2019. The model was validated in 60 consecutive patients, 44 of whom had SCN and 16 of whom had MCN, admitted between May 2019 and April 2020. The regression model was adopted to establish a nomogram. Nomogram performance was determined by its discriminative ability and clinical utility.

RESULT

The multivariable logistic regression model included sex, size, location, shape, cyst characteristic, and cystic wall thickening. The individualized prediction nomogram showed good discrimination in the training sample (AUC 0.89; 95% CI 0.83-0.95) and in the validation sample (AUC 0.81; 95% CI 0.70-0.94). If the threshold probability is between 0.03 and 0.9, and > 0.93 in the prediction model, using the nomogram to predict SCN and MCN is more beneficial than the treat-all-patients as SCN scheme or the treat-all-patients as MCN scheme. The prediction model showed better discrimination than the radiologists' diagnosis (AUC = 0.68).

CONCLUSION

The nomogram could predict SCN and MCN preoperatively and may aid clinical decision-making.

Incidental Pancreatic Cysts on Cross-Sectional Imaging

Incidental pancreatic cysts are commonly encountered in radiology practice. Although some of these are benign, mucinous varieties have a potential to undergo malignant transformation. Characterization of some incidental pancreatic cysts based on imaging alone is limited, and given that some pancreatic cysts have a malignant potential, various societies have created guidelines for the management and follow-up of incidental pancreatic cysts. This article reviews the imaging findings and work-up of pancreatic cysts and gives an overview of the societal guidelines for the management and follow-up of incidental pancreatic cysts.

The "extracapsular cystic" sign in pancreatic serous cystic neoplasms: A clinicopathologic study of 177 patients with cystic pancreatic lesions

OBJECTIVE

To assess a new imaging feature that we have named the extracapsular cystic sign which can make a constructive contribution towards differentiating serous cystic neoplasms (SCNs) from other pancreatic cystic lesions.

MATERIAL AND METHODS

We retrospectively reviewed 177 CTs/MRIs of patients who underwent pancreatic resection of cystic lesions at two institutions from January 2011/2013, to September 2017. For each patient, demographic information, clinical presentation, especially imaging features were carefully investigated by two experienced abdominal radiologists, retrospectively. All statistical analyses were performed using SPSS V.23.0.

RESULTS

Twenty-one lesions had extracapsular cystic signs which were newly discovered, 17 (28.3%) of 60 SCNs and 4 (3.4%) (mucinous cystic neoplasm = 1, walled-off necrosis = 2, retention cyst = 1) of 117 Non-SCNs were included, from which indicating that the extracapsular cystic sign was more often detected on SCNs. As for 21 lesions, 86% (n = 18) were females, and mean age at diagnosis was 51.2 years. 71% (n = 15) located in the pancreatic body and tail. Average size was 27.2 mm (23.7-53.4), mean (SD) ratio of biggest daughter cyst to mother cyst was 0.51[0.14] (p = 0.99), average (SD) angle between two of them was 105.5° [14.9] (p = 0.84). The average time interval between last imaging examination and surgery was 8.4 days.

CONCLUSIONS

The new sign named the extracapsular cystic sign in SCNs may help differentiate SCNs from other pancreatic cystic lesions. Furthermore, this study supports an original diagnosis for SCNs when the sign of extracapsular cyst appears.

Cystic pancreatic lesions: From increased diagnosis rate to new dilemmas

Cystic pancreatic lesions vary from benign to malignant entities and are increasingly detected on cross-sectional imaging. Knowledge of the imaging appearances of cystic pancreatic lesions may help radiologists in their diagnostic reporting and management. In this review, we discuss the morphologic classification of these lesions based on a diagnostic algorithm as well as the management of these lesions.

Laparoscopic fenestration of pancreatic serous cystadenoma: Minimally invasive approach for symptomatic benign disease

Serous cystadenoma (SC) is a benign pancreatic cystic tumor. Surgical resection is recommended for symptomatic forms, but laparoscopic fenestration of large symptomatic macrocystic SC was not yet described in the literature. In this study, 3 female patients underwent laparoscopic fenestration for macrocystic SC (12-14 cm). Diagnosis was established via magnetic resonance imaging and endoscopic ultrasound, with intra-cystic dosage of tumors markers (ACE and CA19-9) in 2 patients. All patients were symptomatic and operated on 15-60 mo after diagnosis. Radiological evaluation showed constant cyst growth. Patients were informed about this new surgical modality that can avoid pancreatic resection. The mean operative time was 103 min (70-150 min) with one conversion. The post-operative course was marked by a grade A pancreatic fistula in one patient and was uneventful in the other two. The hospital stay was 3, 10, and 18 d, respectively. The diagnosis of macrocystic SC was histologically-confirmed in all cases. At the last follow-up (13-26 mo), all patients were symptom-free, and radiological evaluation showed complete disappearance of the cyst. Laparoscopic fenestration, as opposed to resection, should be considered for large symptomatic macrocystic SC, thereby avoiding pancreatic resection morbidity and mortality.

Computer-Aided Diagnosis for Distinguishing Pancreatic Mucinous Cystic Neoplasms From Serous Oligocystic Adenomas in Spectral CT Images

OBJECTIVE

This preliminary study aims to verify the effectiveness of the additional information provided by spectral computed tomography (CT) with the proposed computer-aided diagnosis (CAD) scheme to differentiate pancreatic serous oligocystic adenomas (SOAs) from mucinous cystic neoplasms of pancreas cystic lesions.

MATERIALS AND METHODS

This study was conducted from January 2010 to October 2013. Twenty-three patients (5 men and 18 women; mean age, 43.96 years old) with SOA and 19 patients (3 men and 16 women; mean age, 41.74 years old) with MCN were included in this retrospective study. Two types of features were collected by dual-energy spectral CT imaging as follows: conventional and additional quantitative spectral CT features. Classification results of the CAD scheme were compared using the conventional features and full feature data set. Important features were selected using support vector machine classification method combined with feature-selection technique. The optimal cutoff values of selected features were determined through receiver-operating characteristic curve analyses.

RESULTS

Combining conventional features with additional spectral CT features improved the overall accuracy from 88.37% to 93.02%. The selected features of the proposed CAD scheme were tumor size, contour, location, and low-energy CT values (43 keV). Iodine-water basis material pair densities in both arterial phase (AP) and portal venous phase (PP) were important factors for differential diagnosis of SOA and MCN. The optimal cutoff values of long axis, short axis, 40 keV monochromatic CT value in AP, iodine (water) density in AP, 43 keV monochromatic CT value in PP, and iodine (water) density in PP were 3.4 mm, 3.1 mm, 35.7 Hu, 0.32533 mg/mL, 39.4 Hu, and 0.348 mg/mL, respectively.

CONCLUSION

The combination of conventional features and additional information provided by dual-energy spectral CT shows a high accuracy in the CAD scheme. The quantitative information of spectral CT may prove useful in the diagnosis and classification of SOAs and MCNs with machine learning algorithms.

Magnetic resonance imaging of less common pancreatic malignancies and pancreatic tumors with malignant potential

Pancreatic tumors are an increasingly common finding in abdominal imaging. Various kinds of pathologies of the pancreas are well known, but it often remains difficult to classify the lesions radiologically in respect of type and grade of malignancy. Magnetic resonance imaging (MRI) is the method of choice for the evaluation of pancreatic pathologies due to its superior soft tissue contrast. In this article we present a selection of less common malignant and potentially malignant pancreatic neoplasms with their characteristic appearance on established MRI sequences with and without contrast enhancement.

High-throughput and high-sensitivity quantitative analysis of serum unsaturated fatty acids by chip-based nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry: early stage diagnostic biomarkers of pancreatic cancer

In this study, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) coupled with chip-based direct-infusion nanoelectrospray ionization source (CBDInanoESI) in a negative ion mode is first employed to evaluate the effect of serum and its corresponding supernatant matrixes on the recoveries of serum free fatty acids (FFAs) based on spike-and-recovery experimental strategy by adding analytes along with analog internal standard (IS). The recoveries between serum (69.8-115.6%) and the supernatant (73.6-99.0%) matrixes are almost identical. Multiple point internal standard calibration curves between the concentration ratios of individual fatty acids to ISs, (C(17:1) as IS of C(16:1), C(18:3), C(18:2), or C(18:1) or C(21:0) as IS of C(20:4) or C(22:6)) versus their corresponding intensity ratios were constructed for C(16:1), C(18:3), C(18:2), C(18:1), C(20:4) and C(22:6), respectively, with correlation coefficients of greater than 0.99, lower limits of detection between 0.3 and 1.8 nM, and intra- and inter-day precision (relative standard deviations <18%), along with the linear dynamic range of three orders of magnitude. Sequentially, this advanced analytical platform was applied to perform simultaneous quantitative and qualitative analysis of multiple targets, e.g., serum supernatant unsaturated FFAs from 361 participants including 95 patients with pancreatic cancer (PC), 61 patients with pancreatitis and 205 healthy controls. Experimental results indicate that the levels of C(18:1), C(18:2), C(18:3), C(20:4) and C(22:6), as well as the level ratios of C(18:2)/C(18:1) and C(18:3)/C(18:1) of the PC patients were significantly decreased compared with those of healthy controls and the patients with pancreatitis (p < 0.01). It is worth noting that the ratio of C(18:2)/C(18:1), polyunsaturated fatty acids (PUFAs) (C(18:2), C(18:3), C(20:4), and C(22:6)), panel a (C(16:1), C(18:3), C(18:2), C(20:4) and C(22:6)) and panel b (C(18:2)/C(18:1) and C(18:3)/C(18:1)) performed excellent diagnostic ability, with an area under the receiver operating characteristic curve of ≥0.869, sensitivity of ≥85.7%, and specificity of ≥86.7% for differentiating the early stage PC from non-cancer subjects, which are greatly higher than those of clinically used serum biomarker CA 19-9. More importantly, this platform can also provide a fast and easy way to quantify the levels of FFAs in less than 30 s per sample.

Italian consensus guidelines for the diagnostic work-up and follow-up of cystic pancreatic neoplasms

This report contains clinically oriented guidelines for the diagnostic work-up and follow-up of cystic pancreatic neoplasms in patients fit for treatment. The statements were elaborated by working groups of experts by searching and analysing the literature, and then underwent a consensus process using a modified Delphi procedure. The statements report recommendations regarding the most appropriate use and timing of various imaging techniques and of endoscopic ultrasound, the role of circulating and intracystic markers and the pathologic evaluation for the diagnosis and follow-up of cystic pancreatic neoplasms.

Imaging pitfalls of pancreatic serous cystic neoplasm and its potential mimickers

The aim of this article is to clarify diagnostic pitfalls of pancreatic serous cystic neoplasm (SCN) that may result in erroneous characterization. Usual and unusual imaging findings of SCN as well as potential SCN mimickers are presented. The diagnostic key of SCN is to look for a cluster of microcysts (honeycomb pattern), which may not be always found in the center. Fibrosis in SCN may be mistaken for a mural nodule of intraductal papillary mucinous neoplasm (IPMN). The absence of cyst wall enhancement may be helpful to distinguish SCN from mucinous cystic neoplasm. However, oligocystic SCN and branch duct type IPMN may morphologically overlap. In addition, solid serous adenoma, an extremely rare variant of SCN, is difficult to distinguish from neuroendocrine tumor.

Clinical decision making in the management of pancreatic cystic neoplasms

Pancreatic cystic lesions continue to pose diagnostic and management dilemmas for physicians. This may be related, in part, to the fact that these lesions represent a range of diagnostic possibilities, from inflammatory cysts and nonmucinous cysts to mucinous cysts, which may or may not have foci of invasive malignancy. Adequate characterization of cystic lesions is necessary to help devise a management plan. Moreover, patient-related factors such as comorbid conditions are often essential in deciding whether patients should be managed by a conservative approach of watchful waiting versus surgical resection, if so indicated. This review summarizes the recent advances in the management of pancreatic cystic neoplasms.