Imaging Pancreatic Cysts with CT and MRI

Global Prevalence of Pancreatic Cystic Lesions in the General Population on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis

BACKGROUND & AIMS

Understanding the burden of pancreatic cystic lesions (PCLs) in the general population is important for clinicians and policymakers. In this systematic review, we sought to estimate the global prevalence of PCLs using magnetic resonance imaging (MRI) and to investigate factors that contribute to its variation.

METHODS

We searched MEDLINE, EMBASE, and Cochrane Central, from database inception through February 2023. We included full-text articles that reported the prevalence of PCLs using MRI in the general population. A proportional meta-analysis was performed, and the prevalence of PCLs was pooled using a random-effects model.

RESULTS

Fifteen studies with 65,607 subjects were identified. The pooled prevalence of PCLs was 16% (95% confidence interval [CI], 13%-18%; I2 = 99%), most of which were under 10 mm. Age-specific prevalence of PCLs increased from 9% (95% CI, 7%-12%) at 50 to 59 years, to 18% (95% CI, 14%-22%) at 60 to 69 years, 26% (95% CI, 20%-33%) at 70 to 79 years, and 38% at 80 years and above (95% CI, 25%-52%). There was no difference in prevalence between sexes. Subgroup analysis showed higher PCL prevalence when imaging findings were confirmed by independent radiologist(s) (25%; 95% CI, 16%-33%) than when chart review alone was used (5%; 95% CI, 4%-7%; P < .01). There was no independent association of PCL prevalence with geographic location (Europe, North America, or Asia), MRI indication (screening vs evaluation of non-pancreatic pathology), enrollment period, sample size, magnet strength (1.5 vs 3 tesla), and MRI sequence (magnetic resonance cholangiopancreatography vs no magnetic resonance cholangiopancreatography).

CONCLUSION

In this systematic review, the global prevalence of PCLs using a highly sensitive noninvasive imaging modality ranged between 13% and 18%.

Pancreatic cyst prevalence and detection with photon counting CT compared with conventional energy integrating detector CT

PURPOSE

To calculate the prevalence of pancreatic cysts on photon counting CT (PCCT) and compare with that of 128-slice conventional energy-integrating detector CT (EIDCT).

METHOD

A retrospective single institution database search identified all contrast-enhanced abdominal CT examinations performed at an outpatient facility that has both a PCCT and EIDCT between 4/11/2022 and 7/26/2022. The presence and size of pancreatic cysts were recorded. In patients with PCCT reported pancreatic cysts, prior CT imaging (EIDCT) was reviewed for reported pancreatic cysts. Fisher's exact test was used to compare the pancreatic cyst detection rate for PCCT and EIDCT. Wilcoxon rank sum test was used to compare cyst size and patient age. A p <.05 indicated statistical significance.

RESULTS

2494 patients were included. Our pancreatic cyst detection rate was 4.9 % (49/1009) with PCCT and 3.0 % (44/1485) for EIDCT (p =.017). For CT angiograms, pancreatic cysts were detected in 6.6 % (21/319) with PCCT and 0.0 % (0/141) with EIDCT (p <.001). Pancreatic cyst detection rate was not statistically different for portal venous, enterography, renal mass, pancreas, 3-phase liver, or venogram protocols (all p >.05). Mean[SD] pancreatic cyst size was 13.7[9.7]mm for PCCT and 15.3[14.7] for EIDCT (p =.95). 55.1 % (27/49) of PCCT and 61.4 % (27/44) of EIDCT that described pancreatic cysts had prior contrast-enhanced EIDCTs. Of these, 40.7 % (11/27) of PCCT and 14.8 % (4/27) of EIDCT described pancreatic cysts were not previously reported (p =.027).

CONCLUSIONS

Photon-counting CT afforded greater pancreatic cyst detection than conventional energy-integrating detector CT, particularly with CT angiograms.

EUS-guided fine needle aspiration-based clues to mistaken or uncertain identity: serous pancreatic cysts

BACKGROUND/OBJECTIVES

Pancreatic serous cystic neoplasms (SCN) present a diagnostic challenge given their increasing frequency of detection and benign nature yet relatively high rate of misdiagnosis. Here, imaging and analyses associated with EUS-guided fine-needle aspiration (EUS-FNA) are evaluated for their ability to provide a correct preoperative diagnosis of SCN.

METHODS

A surgical cohort with confirmed pathological diagnosis of SCN (n = 62) and a surveillance cohort with likely SCN (n = 31) were assessed for imaging (CT/MRI/EUS) and EUS-FNA-based analyses (cytology/DNA analysis for Von Hippel-Lindau [VHL] gene alterations/biomarkers).

RESULTS

In the surgical cohort, CT/MRI and EUS respectively predicted SCN in 4 of 58(7%) and 19 of 62(31%). Cyst fluid cytology and VHL alterations predicted SCN in 1 of 51(2%) and 5 of 21(24%), respectively. High specificity cyst fluid biomarkers (vascular endothelial growth factor [VEGF]/glucose/carcinoembryonic antigen [CEA]/amylase) correctly identified SCN in 25 of 27(93%). In the surveillance cohort, cyst fluid biomarkers predicted SCN in 12 of 12(100%) while VHL alterations identified SCN 3 of 10(30%).

CONCLUSION

High specificity cyst fluid biomarkers provided the most sensitive means of diagnosing SCN preoperatively. To obtain a preoperative diagnosis of SCN at the highest level of certainty, a multidisciplinary approach should be taken to inform appropriate SCN management.

Clinical Characteristics of Patients With Surgically Resected Pancreatic Cysts: A Retrospective Analysis of 136 Patients

OBJECTIVES

To retrospectively analyze the characteristics of pancreatic cysts with respect to histopathological diagnosis and various diagnostic imaging tools.

METHODS

The clinical features of 136 patients and characteristics of histopathologically diagnosed cysts were retrospectively assessed. The diagnostic accuracy of endoscopic ultrasound (EUS), computed tomography (CT), and magnetic resonance imaging (MRI) for pancreatic cysts was compared. Risk factors for high-grade dysplasia/invasive cancer in patients with intraductal papillary mucinous neoplasms (IPMNs) were also determined.

RESULTS

The final analysis included 30 serous cystic neoplasms (SCNs) (21.6%), 13 mucinous cystic neoplasms (MCNs) (9.4%), 65 IPMNs (46.8%), and 13 solid pseudopapillary neoplasms (SPNs) (9.4%). The percentage of women with MCNs, SPNs, SCNs, and IPMNs was 100.0, 76.9, 73.3, and 47.7%, respectively (P < .001). The percentages of patients over 60 years of age with IPMNs, SCNs, MCNs, and SPNs were 73.9, 23.3, 0, and 0%, respectively (P < .001). The percentage of cysts located in the body and tail of the pancreas in MCNs, SCNs, SPNs, and IPMNs was 100, 70, 53.9, and 46.2%, respectively (P < .001). A unique honeycomb appearance was observed in 26.7% of SCNs. The overall diagnostic accuracy of EUS, CT, and MRI for pancreatic cysts was 82.6, 72.5, and 73.9%, respectively. Lesion size and presence of solid components were independent predictors of high-risk IPMNs.

CONCLUSIONS

Patient characteristics and cyst features can help to differentiate pancreatic cyst types and identify high-risk IPMNs. The diagnostic accuracy of EUS for pancreatic cysts is superior to that of CT and MRI.

The Role of Magnetic Resonance Imaging (MRI) in the Diagnosis of Pancreatic Cystic Lesions (PCLs)

Pancreatic cystic lesions (PCLs) are a common incidental finding on cross-sectional imaging. Given the high signal to noise and contrast resolution, multi-parametric capability and lack of ionizing radiation, magnetic resonance imaging (MRI) has become the non-invasive method of choice to predict cyst type, risk stratify the presence of neoplasia, and monitor changes during surveillance. In many patients with PCLs, the combination of MRI and the patient's history and demographics will suffice to stratify lesions and guide treatment decisions. In other patients, especially those with worrisome or high-risk features, a multimodal diagnostic approach that includes endoscopic ultrasound (EUS) with fluid analysis, digital pathomics, and/or molecular analysis is often necessary to decide on management options. The application of radiomics and artificial intelligence in MRI may improve the ability to non-invasively stratify PCLs and better guide treatment decisions. This review will summarize the evidence on the evolution of MRI for PCLs, the prevalence of PCLs using MRI, and the MRI features to diagnose specific PCL types and early malignancy. We will also describe topics such as the utility of gadolinium and secretin in MRIs of PCLs, the limitations of MRI for PCLs, and future directions.

Evaluation of the Effects of Folic Acid Combined with Atorvastatin on the Poststroke Cognitive Impairment by Low-Rank Matrix Denoising Algorithm-Based MRI Imaging

This research aimed to study the optimization effects of the low-rank matrix denoising (LRMD) algorithm based on the Gaussian mixture model (GMM) on MRI images of stroke patients, aiming to evaluate the effects of atorvastatin combined with folic acid on poststroke cognitive impairment (PSCI) in patients with ischemic stroke. First, the GMM-based low-rank matrix denoising (LRMD) algorithm was constructed and applied to process MRI images of 64 patients with ischemic stroke. Then, the MRI images before and after processing were compared for the denoising degree and quality. An image with 5% noise was not as clear as an MRI image with 1% noise, and the effects of atorvastatin combined with folic acid on PSCI in patients with ischemic stroke were discussed. It was found that the denoising degree of MRI images processed by the GMM-based LRMD algorithm was significantly improved, the image quality was significantly enhanced (P < 0.05), and the diagnosis accuracy and efficiency of stroke patients were heightened. Atorvastatin combined with folic acid reduce the homocysteine (HCY) and total cholesterol (TC) levels, as well as Montreal Cognitive Scale (MOCA) scores of PSCI patients (P < 0.05). In conclusion, the MRI images processed by the LRMD algorithm have good quality. Folic acid combined with atorvastatin can effectively reduce HCY and TC levels, thereby alleviating PSCI of stroke patients.

Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm (P < 0.05). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group (P < 0.05). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient's anxiety and ensure that high-quality MRI images were obtained after the examination.

Facile Synthesis of Holmium-Based Nanoparticles as a CT and MRI Dual-Modal Imaging for Cancer Diagnosis

The rapid development of medical imaging has boosted the abilities of modern medicine. As single modality imaging limits complex cancer diagnostics, dual-modal imaging has come into the spotlight in clinical settings. The rare earth element Holmium (Ho) has intrinsic paramagnetism and great X-ray attenuation due to its high atomic number. These features endow Ho with good potential to be a nanoprobe in combined x-ray computed tomography (CT) and T₂-weighted magnetic resonance imaging (MRI). Herein, we present a facile strategy for preparing HoF₃ nanoparticles (HoF₃ NPs) with modification by PEG 4000. The functional PEG-HoF₃ NPs have good water solubility, low cytotoxicity, and biocompatibility as a dual-modal contrast agent. Currently, there is limited systematic and intensive investigation of Ho-based nanomaterials for dual-modal imaging. Our PEG-HoF₃ NPs provide a new direction to realize in vitro and vivo CT/MRI imaging, as well as validation of Ho-based nanomaterials will verify their potential for biomedical applications.

Biomarker Risk Score Algorithm and Preoperative Stratification of Patients with Pancreatic Cystic Lesions

BACKGROUND

Pancreatic cysts are incidentally detected in up to 13% of patients undergoing radiographic imaging. Of the most frequently encountered types, mucin-producing (mucinous) pancreatic cystic lesions may develop into pancreatic cancer, while nonmucinous ones have little or no malignant potential. Accurate preoperative diagnosis is critical for optimal management, but has been difficult to achieve, resulting in unnecessary major surgery. Here, we aim to develop an algorithm based on biomarker risk scores to improve risk stratification.

STUDY DESIGN

Patients undergoing surgery and/or surveillance for a pancreatic cystic lesion, with diagnostic imaging and banked pancreatic cyst fluid, were enrolled in the study after informed consent (n = 163 surgical, 67 surveillance). Cyst fluid biomarkers with high specificity for distinguishing nonmucinous from mucinous pancreatic cysts (vascular endothelial growth factor [VEGF], glucose, carcinoembryonic antigen [CEA], amylase, cytology, and DNA mutation) were selected. Biomarker risk scores were used to design an algorithm to predict preoperative diagnosis. Performance was tested using surgical (retrospective) and surveillance (prospective) cohorts.

RESULTS

In the surgical cohort, the biomarker algorithm outperformed the preoperative clinical diagnosis in correctly predicting the final pathologic diagnosis (91% vs 73%; p < 0.000001). Specifically, nonmucinous serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN) were correctly classified more frequently by the algorithm than clinical diagnosis (96% vs 30%; p < 0.000008 and 92% vs 69%; p = 0.04, respectively). In the surveillance cohort, the algorithm predicted a preoperative diagnosis with high confidence based on a high biomarker score and/or consistency with imaging from ≥1 follow-up visits.

CONCLUSIONS

A biomarker risk score-based algorithm was able to correctly classify pancreatic cysts preoperatively. Importantly, this tool may improve initial and dynamic risk stratification, reducing overdiagnosis and underdiagnosis.

Cyst fluid glucose: An alternative to carcinoembryonic antigen for pancreatic mucinous cysts

Pancreatic cystic lesions (PCLs) have been increasingly recognized in clinical practice. Although inflammatory cysts (pseudocysts) are the most common PCLs detected by cross-sectional imaging modalities in symptomatic patients in a setting of acute or chronic pancreatitis, incidental pancreatic cysts with no symptoms or history of pancreatitis are usually neoplastic cysts. For these lesions, it is imperative to identify mucinous cysts (intraductal papillary mucinous neoplasms and mucinous cystic neoplasms) due to the risk of their progression to malignancy. However, no single imaging modality alone is sufficient for a definitive diagnosis of all PCLs. The cyst fluid obtained by endoscopic ultrasound-guided fine needle aspiration provides additional information for the differential diagnosis of PCLs. Current recommendations suggest sending cyst fluid for cytology evaluation and measurement of carcinoembryonic antigen (CEA) levels. Unfortunately, the sensitivity of cytology is greatly limited, and cyst fluid CEA has demonstrated insufficient accuracy as a predictor of mucinous cysts. More recently, cyst fluid glucose has emerged as an alternative to CEA for distinguishing between mucinous and nonmucinous lesions. Herein, the clinical utility of cyst fluid glucose and CEA for the differential diagnosis of PCLs was evaluated.

Managing Incidental Pancreatic Cysts

PURPOSE OF REVIEW

The goal of this review is to critically analyze the current literature regarding the management of incidental pancreatic cysts. Given their increased rates of detection due to the frequent use of cross-sectional imaging, correctly identifying the subset of high risk lesions that are appropriate for surgical resection is critical. However, the existing consensus and societal guidelines discussed in this review lack high quality data to create evidence-based recommendations, making achieving this important aim challenging.

RECENT FINDINGS

Several recent studies have focused on the natural history of pancreatic cysts and defining the role of endoscopic ultrasound, which remains unclear. EUS-guided diagnostic tools include molecular analysis of obtained fluid; EUS-guided FNA, FNB, and intracystic forceps biopsy of the cyst wall; and confocal endomicroscopy. While their precise role in diagnosing pancreatic cystic neoplasms remains to be defined, they represent promising innovations that may play a future role in cyst assessment and management. Large, long-term, prospective studies of incidentally identified pancreatic cysts are essential to fully understand their natural history and potential for neoplastic progression. Given the absence of such data at present, an individualized patient approach is recommended.