Diagnostic Accuracy of MRI for Differentiation of Benign and Malignant Pancreatic Cystic Lesions Compared to CT and Endoscopic Ultrasound: Systematic Review and Meta-analysis

Should All Pancreatic Cystic Lesions with Worrisome or High-Risk Features Be Resected? A Clinical and Radiological Machine Learning Model May Help to Answer

RATIONALE AND OBJECTIVES

According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) model to identify malignant PCLs for surgery among preoperative PCLs with worrisome or high-risk features.

MATERIALS AND METHODS

Clinical and radiological details of 317 pathologically confirmed PCLs with worrisome or high-risk features were retrospectively analyzed and applied to ML models including Support Vector Machine, Logistic Regression (LR), Decision Tree, Bernoulli NB, Gaussian NB, K Nearest Neighbors and Linear Discriminant Analysis. The diagnostic ability for malignancy of the optimal model with the highest diagnostic AUC in the cross-validation procedure was further evaluated in internal (n = 77) and external (n = 50) testing cohorts, and was compared to two published guidelines in internal mucinous cyst cohort.

RESULTS

Ten clinical and radiological feature-based LR model was the optimal model with the highest AUC (0.951) in the cross-validation procedure. In the internal testing cohort, LR model reached an AUC, accuracy, sensitivity, and specificity of 0.927, 0.909, 0.914, and 0.905; in the external testing cohort, LR model reached 0.948, 0.900, 0.963, and 0.826. When compared to the European guidelines and the ACG guidelines, LR model demonstrated significantly better accuracy and specificity in identifying malignancy, while maintaining the same high sensitivity.

CONCLUSION

Clinical- and radiological-based LR model can accurately identify malignant PCLs in patients with worrisome or high-risk features, possessing diagnostic performance better than the European guidelines as well as ACG guidelines.

Diagnosis and Management of Pancreatic Cysts

As pancreatic cyst incidence rises, likely due to the ubiquitous increase in cross-sectional imaging, their management presents multiple challenges for both the practitioner and patient. It is critical that all pancreatic cysts are appropriately characterized, as treatment decisions depend on an accurate diagnosis. Diagnostic modalities such as cytology, biopsy, and cyst fluid biomarkers allow for definitive diagnosis of virtually all lesions. Some cysts, such as intraductal papillary mucinous neoplasms, mucinous cystic neoplasms, and cystic pancreatic endocrine neoplasms, have malignant potential and must be surveyed. Other cysts, such as serous cystadenomas and pancreatic fluid collections, do not have malignant potential. Surveillance strategies vary widely depending on cyst type and size and while multiple medical societies advocate surveillance, their published surveillance guidelines are heterogenous. Cysts with high-risk stigmata or worrisome features are usually resected, depending on the patient's surgical fitness. In patients unfit for resection, newer endoscopic ablative techniques are advocated. Controversial aspects regarding cyst management include whether surveillance can be stopped, how surveillance should be performed, and the extensive financial burden cyst management places on the health care system. Further study into the natural history of cystic lesions, including definitive determination of the rate of malignant transformation for each cyst type, is essential.

Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

Percentage of Pancreatic Cysts on MRI With a Pancreatic Carcinoma: Systematic Review and Meta-Analysis

BACKGROUND

Pancreatic cystic lesions (PCLs) are frequent on MRI and are thought to be associated with pancreatic adenocarcinoma (PDAC) necessitating long-term surveillance based on older studies suffering from selection bias.

PURPOSE

To establish the percentage of patients with PCLs on MRI with a present or future PDAC.

STUDY TYPE

Systematic review, meta-analysis.

POPULATION

Adults with PCLs on MRI and a present or future diagnosis of PDAC were eligible. MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Scopus were searched to April 2022 (PROSPERO:CRD42022320502). Studies limited to PCLs not requiring surveillance, <100 patients, or those with a history/genetic risk of PDAC were excluded.

FIELD STRENGTH/SEQUENCE

≥1.5 T with ≥1 T2-weighted sequence.

ASSESSMENT

Two investigators extracted data, with discrepancies resolved by a third. QUADAS-2 assessed bias. PDAC was diagnosed using a composite reference standard.

STATISTICAL TESTS

A meta-analysis of proportions was performed at the patient-level with 95% confidence intervals (95% CI).

RESULTS

Eight studies with 1289 patients contributed to the percentage of patients with a present diagnosis of PDAC, and 10 studies with 3422 patients to the percentage with a future diagnosis. Of patients with PCLs on MRI, 14.8% (95% CI 2.4-34.9) had a PDAC at initial MRI, which decreased to 6.0% (2.2-11.3) for studies at low risk of bias. For patients without PDAC on initial MRI, 2.0% (1.1-3.2) developed PDAC during surveillance, similar for low risk of bias studies at 1.9% (0.7-3.6), with no clear trend of increased PDAC for longer surveillance durations. For patients without worrisome features or high-risk stigmata, 0.9% (0.1-2.2) developed PDAC during surveillance. Of 10, eight studies had a median surveillance ≥3 years (range 3-157 months). Sources of bias included retrospectively limiting PCLs to those with histopathology and inconsistent surveillance protocols.

DATA CONCLUSION

A low percentage of patients with PCLs on MRI develop PDAC while on surveillance. The first MRI revealing a PCL should be scrutinized for PDAC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Structured training program on confocal laser endomicroscopy for pancreatic cystic lesions: a multicenter prospective study among early-career endosonographers (with video)

BACKGROUND AND AIMS

Data on how to teach endosonographers needle-based confocal laser endomicroscopy (nCLE)-guided histologic diagnosis of pancreatic cystic lesions (PCLs) are limited. Hence, we developed and tested a structured educational program to train early-career endosonographers in nCLE-guided diagnosis of PCLs.

METHODS

Twenty-one early-career nCLE-naïve endosonographers watched a teaching module outlining nCLE criteria for diagnosing PCLs. Participants then reviewed 80 high-yield nCLE videos, recorded diagnoses, and received expert feedback (phase 1). Observers were then randomized to a refresher feedback session or self-learning at 4 weeks. Eight weeks after training, participants independently assessed the same 80 nCLE videos without feedback and provided histologic predictions (phase 2). Diagnostic performance of nCLE to differentiate mucinous versus nonmucinous PCLs and to diagnose specific subtypes were analyzed using histopathology as the criterion standard. Learning curves were determined using cumulative sum analysis.

RESULTS

Accuracy and diagnostic confidence for differentiating mucinous versus nonmucinous PCLs improved as endosonographers progressed through nCLE videos in phase 1 (P < .001). Similar trends were observed with the diagnosis of PCL subtypes. Most participants achieved competency interpreting nCLE, requiring a median of 38 assessments (range, 9-67). During phase 2, participants independently differentiated PCLs with high accuracy (89%), high confidence (83%), and substantial interobserver agreement (κ = .63). Accuracy for nCLE-guided PCL subtype diagnoses ranged from 82% to 96%. The learned nCLE skills did not deteriorate at 8 weeks and were not impacted by a refresher session.

CONCLUSIONS

We developed a practical, effective, and durable educational intervention to train early-career endosonographers in nCLE-guided diagnosis of PCLs.

A Comparison of Single Dimension and Volume Measurements in the Risk Stratification of Pancreatic Cystic Lesions

The incidence of pancreatic cystic lesions (PCLs) has been rising due to improvements in imaging. Of these, intraductal papillary mucinous neoplasms (IPMNs) are the most common and are thought to contribute to almost 20% of pancreatic adenocarcinomas. All major society guidelines for the management of IPMNs use size defined by maximum diameter as the primary determinant of whether surveillance or surgical resection is recommended. However, there is no consensus on how these measurements should be obtained or whether a single imaging modality is superior. Furthermore, the largest diameter may fail to capture the complexity of PCLs, as most are not perfectly spherical. This article reviews current PCL measurement techniques in CT, MRI, and EUS and posits volume as a possible alternative to the largest diameter.

Diagnostic value of deep learning-assisted endoscopic ultrasound for pancreatic tumors: a systematic review and meta-analysis

Background and aims

Endoscopic ultrasonography (EUS) is commonly utilized in the diagnosis of pancreatic tumors, although as this modality relies primarily on the practitioner's visual judgment, it is prone to result in a missed diagnosis or misdiagnosis due to inexperience, fatigue, or distraction. Deep learning (DL) techniques, which can be used to automatically extract detailed imaging features from images, have been increasingly beneficial in the field of medical image-based assisted diagnosis. The present systematic review included a meta-analysis aimed at evaluating the accuracy of DL-assisted EUS for the diagnosis of pancreatic tumors diagnosis.

Methods

We performed a comprehensive search for all studies relevant to EUS and DL in the following four databases, from their inception through February 2023: PubMed, Embase, Web of Science, and the Cochrane Library. Target studies were strictly screened based on specific inclusion and exclusion criteria, after which we performed a meta-analysis using Stata 16.0 to assess the diagnostic ability of DL and compare it with that of EUS practitioners. Any sources of heterogeneity were explored using subgroup and meta-regression analyses.

Results

A total of 10 studies, involving 3,529 patients and 34,773 training images, were included in the present meta-analysis. The pooled sensitivity was 93% (95% confidence interval [CI], 87-96%), the pooled specificity was 95% (95% CI, 89-98%), and the area under the summary receiver operating characteristic curve (AUC) was 0.98 (95% CI, 0.96-0.99).

Conclusion

DL-assisted EUS has a high accuracy and clinical applicability for diagnosing pancreatic tumors.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023391853, identifier CRD42023391853.

Pancreatic Cysts: Radiology

This article reviews the types of pancreatic cysts encountered in Radiologic practice. It summarizes the malignancy risk of each of the following: serous cystadenoma, mucinous cystic tumor, intraductal papillary mucinous neoplasm main duct and side branch, and some miscellaneous cysts such as neuroendocrine tumor and solid pseudopapillary epithelial neoplasm. Specific reporting recommendations are given. The choice between radiology follow-up versus endoscopic analysis is discussed.

Comparative Performance of Endoscopic Ultrasound-Based Techniques in Patients With Pancreatic Cystic Lesions: A Network Meta-Analysis

INTRODUCTION

Evidence on the comparative diagnostic performance of endoscopic ultrasound (EUS)-based techniques for pancreatic cystic lesions (PCLs) is limited. This network meta-analysis comprehensively compared EUS-based techniques for PCL diagnosis.

METHODS

A comprehensive literature search was performed for all comparative studies assessing the accuracy of 2 or more modalities for PCL diagnosis. The primary outcome was the diagnostic efficacy for mucinous PCLs. Secondary outcomes were the diagnostic efficacy for malignant PCLs, diagnostic success rate, and adverse event rate. A network meta-analysis was conducted using the ANOVA model to assess the diagnostic accuracy of each index.

RESULTS

Forty studies comprising 3,641 patients were identified. The network ranking of the superiority index for EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) and EUS-guided through-the-needle biopsy (EUS-TTNB) were significantly higher than other techniques for differentiating mucinous PCLs; besides, EUS-TTNB was also the optimal technique in identifying malignant PCLs. The evidence was inadequate for EUS-nCLE diagnosing malignant PCLs and contrast-enhanced harmonic EUS diagnosing both mucinous and malignant PCLs. Glucose showed a high sensitivity but low specificity, and molecular analysis (KRAS, GNAS, and KRAS + GNAS mutations) showed a high specificity but low sensitivity for diagnosing mucinous PCLs. Satisfactory results were not obtained during the evaluation of the efficiency of pancreatic cyst fluid (PCF) biomarkers in detecting malignant PCLs.

DISCUSSION

For centers with relevant expertise and facilities, EUS-TTNB and EUS-nCLE were better choices for the diagnosis of PCLs. Further studies are urgently required for further improving PCF biomarkers and validating the diagnostic performance of the index techniques.

Pancreatic Cystic Lesions: Review of the Current State of Diagnosis and Surveillance

Pancreatic cystic lesions (PCLs) are both common and often incidental. These encompass a range of pathologies with varying degrees of concern for malignancy. Although establishing a diagnosis is helpful for determining malignant potential, many PCLs are either too small to characterize or demonstrate nonspecific morphologic features. The most salient modalities involved in diagnosis and surveillance are magnetic resonance imaging, multidetector computerized tomography, and endoscopic ultrasound. Fine needle aspiration has a role in conjunction with molecular markers as a diagnostic tool, particularly for identifying malignant lesions. Although several major consensus guidelines exist internationally, there remains uncertainty in establishing the strength of the association between all PCLs and pancreatic adenocarcinoma, and in showing a benefit from extended periods of imaging surveillance. No consensus exists between the major guidelines, particularly regarding surveillance duration, frequency, or endpoints. This review paper discusses PCL subtypes, diagnosis, and compares the major consensus guidelines with considerations for local adaptability along with questions regarding current and future priorities for research.

Pancreatic Cystic Lesions on MRI: What Is The Likelihood of a Present or Future Diagnosis of Pancreatic Carcinoma?

BACKGROUND

Pancreatic cystic lesions (PCLs) are followed for years due to older and likely biased works demonstrating a strong association with pancreatic carcinoma; more recent data are needed clarifying this relationship.

PURPOSE

To determine the association between PCLs on MRI and a synchronous or future diagnosis of pancreatic carcinoma.

STUDY TYPE

Single-center retrospective cohort.

POPULATION

A total of 192 patients (111 female, 58%) with median age 66 years (range 26-87 years) with PCLs on abdominal MRI from 2011 to 2016.

FIELD STRENGTH/SEQUENCES

1.5 T and 3 T, including T₂ WI, T₁ WI, diffusion weighted imaging and contrast-enhanced T₁ WI.

ASSESSMENT

Each PCL was reviewed independently by 2 of 10 fellowship-trained abdominal radiologists. Fukuoka guideline worrisome features and high-risk stigmata were evaluated. Follow-up imaging and clinical notes were reviewed within a system that captures pancreatic carcinoma for the region, for a median follow-up of 67 months (interquartile range: 43-88 months).

STATISTICAL TESTS

Pancreatic carcinoma prevalence and incidence rate for future carcinoma with 95% confidence intervals (95% CI). Fisher exact test, logistic regression with odds ratios (OR) and the Wilcoxon rank-sum test were used to assess PCL morphologic features with the Kolmogorov-Smirnov test used to assess for normality. P < 0.05 defined statistical significance.

RESULTS

The prevalence of pancreatic carcinoma on initial MRI showing a PCL was 2.4% (95% CI: 0.9%, 5.2%). Thickened/enhancing cyst wall was associated with pancreatic carcinoma, OR 52 (95% CI: 4.5, 1203). Of 189 patients with a PCL but without pancreatic carcinoma at the time of initial MRI, one developed high-grade dysplasia and none developed invasive carcinoma for an incidence rate of 0.97 (95% CI: 0.02, 5.43) and 0 (95% CI: 0, 3.59) cases per 1000 person-years, respectively.

DATA CONCLUSION

A low percentage of patients with a PCL on MRI had a pancreatic carcinoma at the time of initial evaluation and none developed carcinoma over a median 67 months of follow-up.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: 5.

Pancreatic Incidentaloma

Pancreatic incidentalomas (PIs) represent a clinical entity increasingly recognized due to advances in and easier access to imaging techniques. By definition, PIs should be detected during abdominal imaging performed for indications other than a pancreatic disease. They range from small cysts to invasive cancer. The incidental diagnosis of pancreatic cancer can contribute to early diagnosis and treatment. On the other hand, inadequate management of PIs may result in overtreatment and unneeded morbidity. Therefore, there is a strong need to evaluate the nature and clinical features of individual PIs. In this review, we summarize the major characteristics related to PIs and present suggestions for their management.

Advances in the Diagnosis of Pancreatic Cystic Lesions

Pancreatic cystic lesions (PCLs) are a heterogenous group of lesions ranging from benign to malignant. There has been an increase in PCLs prevalence in recent years, mostly due to advances in imaging techniques, increased awareness of their existence and population aging. Reliable discrimination between neoplastic and non-neoplastic cystic lesions is paramount to ensuring adequate treatment and follow-up. Although conventional diagnostic techniques such as ultrasound (US), magnetic resonance imaging (MRI) and computer tomography (CT) can easily identify these lesions, assessing the risk of malignancy is limited. Endoscopic ultrasound (EUS) is superior to cross-sectional imaging in identifying potentially malignant lesions due to its high resolution and better imaging characteristics, and the advantage of allowing for cyst fluid sampling via fine-needle aspiration (FNA). More complex testing, such as cytological and histopathological analysis and biochemical and molecular testing of the aspirated fluid, can ensure an accurate diagnosis.

Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions

The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34–68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25–64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.