Differentiation of solid-pseudopapillary tumors of the pancreas from pancreatic neuroendocrine tumors by using endoscopic ultrasound

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.

Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis

Background

Accurate detection of the histological grade of pancreatic neuroendocrine tumors (PNETs) is important for patients' prognoses and treatment. Here, we investigated the performance of radiological image-based artificial intelligence (AI) models in predicting histological grades using meta-analysis.

Method

A systematic literature search was performed for studies published before September 2023. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed by the QUADAS-2 tool.

Results

A total of 26 studies were included, 20 of which met the meta-analysis criteria. We found that the AI-based models had high area under the curve (AUC) values and showed moderate predictive value. The pooled distinguishing abilities between different grades of PNETs were 0.89 [0.84-0.90]. By performing subgroup analysis, we found that the radiomics feature-only models had a predictive value of 0.90 [0.87-0.92] with I2 = 89.91%, while the pooled AUC value of the combined group was 0.81 [0.77-0.84] with I2 = 41.54%. The validation group had a pooled AUC of 0.84 [0.81-0.87] without heterogenicity, whereas the validation-free group had high heterogenicity (I2 = 91.65%, P=0.000). The machine learning group had a pooled AUC of 0.83 [0.80-0.86] with I2 = 82.28%.

Conclusion

AI can be considered as a potential tool to detect histological PNETs grades. Sample diversity, lack of external validation, imaging modalities, inconsistent radiomics feature extraction across platforms, different modeling algorithms and software choices were sources of heterogeneity. Standardized imaging, transparent statistical methodologies for feature selection and model development are still needed in the future to achieve the transformation of radiomics results into clinical applications.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022341852.

Role of Endoscopic Ultrasound in the Evaluation of Pancreatic Cystic Neoplasms: A Concise Review

Pancreatic cystic lesions are being discovered as incidental lesions during cross-sectional imaging studies of the abdomen with increasing frequency. Endoscopic ultrasound is an important diagnostic modality for managing pancreatic cystic lesions. There are various types of pancreatic cystic lesions, from benign to malignant. Endoscopic ultrasound has a multifactorial role in delineating the morphology of pancreatic cystic lesions, ranging from fluid and tissue acquisition for analysis-fine needle aspiration and through-the-needle biopsy, respectively-to advanced imaging techniques, such as contrast-harmonic mode endoscopic ultrasound and EUS-guided needle-based confocal laser endomicroscopy. In this review, we will summarize and provide an update on the specific role of EUS in the management of pancreatic cystic lesions.

Differential diagnosis between pancreatic solid pseudopapillary tumors and pancreatic neuroendocrine tumors based on contrast enhanced ultrasound imaging features

PURPOSES

To evaluate the application of contrast enhanced ultrasound (CEUS) in preoperatively differential diagnosis between pancreatic solid pseudopapillary tumors (SPTs) and pancreatic neuroendocrine tumors (pNETs).

PATIENTS AND METHODS

This retrospective study was approved by Institutional Review Board. Patients with surgical resection and histopathological diagnosis as SPTs and pNETs were included. All patients underwent B mode ultrasound (BMUS) and CEUS examinations within one week before surgical operation. On BMUS, the size, location, echogenicity, calcification, and margin of lesions were observed and recorded. On CEUS imaging, enhancement patterns, and enhancement degrees were recorded and analyzed. An independent t-test or Mann-Whitney U test was used for comparison between continuous variables. Chi-square test was used to compare the CEUS patterns.

RESULTS

From February 2017 to Dec 2022, patients diagnosed as SPTs (n = 39) and pNETs (n = 48) were retrospectively included. On BMUS, anechoic cystic changes (19/39, 48.72%) and hyperechoic calcification (14/39, 35.90%) are more commonly detected in SPTs (P = 0.000). On CEUS imaging, the majority of SPTs (27/39, 69.23%) showed hypo-enhancement in the arterial phase, while most of the pNETs (36/48, 75.00%) showed hyper- or iso-enhancement in the arterial phase (P = 0.000). In the venous phase, most of the SPTs (32/39, 82.05%) showed hypo-enhancement, while over half of pNETs (29/48, 60.42%) showed hyper- or iso-enhancement compared to pancreatic parenchyma (P = 0.001).

CONCLUSIONS

CEUS is a valuable and non-invasive imaging method to make preoperatively differential diagnoses between SPTs and pNETs.

The incidence, prevalence, and survival analysis of pancreatic neuroendocrine tumors in the United States

PURPOSE

The incidence of pancreatic neuroendocrine tumors (pNETs) was increasing. The main purpose of this study was to statistically analyze the incidence and prevalence of pNETs and the main risk factors for the prognosis.

METHODS

Based on the Surveillance, Epidemiology, and End Results (SEER) database, with three registries integrated, this study comprehensively displayed the annual age adjust incidence of pNETs from 1975 to 2018, the estimated 20-year limited-duration prevalence, and conducted the univariate and multivariate survival analysis.

RESULTS

The incidence of pNETs has increased to about 1.5 per 100,000 population, and the prevalence has reached about 0.008% with the aged, Grade 1 and nonfunctional tumors accounting for the majority. The average median overall survival (OS), 5-year survival rate, and median disease-free survival (DFS) of pNETs patients from 1975 to 2018 were 85 months, 57.55%, and 220 months, respectively. From 2000 to 2018, the median OS was 94 months, and the 5-year survival rate was 59.94%. In multivariate survival analysis, the greatest risk factor was Grade 3&4 with HR = 3.62 (3.10-4.28), followed by distant stage with HR = 2.77 (2.28-3.36), and aged over 80 years old with HR = 2.26 (1.33-3.83). Surgery was a protective prognostic factor with HR = 0.34 (0.29-0.40).

CONCLUSION

The incidence and prevalence of pNETs were still increasing, but the trend was gradual and aging in recent years. The survival time of pNETs was longer but has not changed much in recent years. The degrees of malignancy, stage, and operation were the most important prognosis factors.

Multimodality imaging differentiation of pancreatic neuroendocrine tumors and solid pseudopapillary tumors with a nomogram model: A large single-center study

Background

Pancreatic neuroendocrine tumors (pNETs) and solid pseudopapillary tumors (SPTs) are two of the most common pancreatic neoplasms with different treatment procedures. However, the broad heterogeneity of pNETs and SPTs in clinical manifestations and radiological features often confuse the presurgical discrimination in clinical practice, and the clinical and molecular differentiation of the two tumors remains elusive to date. We presume that a large and comprehensive study into the multimodality features of pNETs and SPTs is necessary for precise clinical management.

Methods

We collected and analyzed the clinicopathological information and multimodality features of nonfunctional pNET and SPT patients, for a total of 631 cases from 2006 to 2021. Univariate analysis of imaging features, including contrast-enhanced computed tomography (CT), magnetic resonance imaging, endoscopic ultrasound (EUS) and nuclear medicine imaging, and clinical characteristics was performed, and CT features and clinical information were integrated to establish a nomogram model.

Results

We recruited 354 nonfunctional pNET and 277 SPT patients in our cohort. Regarding demographic information, pNET patients had a lower female percentage (55.4% vs. 72.9%), smaller tumor size (2.8 vs. 4.8 cm), and older age (53.4 vs. 35.3 years). In CT imaging and EUS, pNETs tended to appear as solid and homogenous lesions with strong enhancement intensity. Multifocal lesions, duct dilation, and lymph node (LN) enlargement were more likely to be observed in pNETs, while calcification was more common in SPT lesions. On positron emission tomography (PET)/CT, pNETs exhibited significant sensitivity to somatostatin receptor scintigraphy (SRS), with positive rates of 81.4% and 95% on ⁹⁹mTc-HYNIC-TOC and ⁶⁸Ga-DOTATATE PET/CT, respectively, while SPTs were all negative on SRS. Multivariate analysis identifies tumor size, age, enhancement intensity, calcification, and LN enlargement as statistically significant variables.

Conclusions

Compared to SPT patients, pNET patients exhibit an older age and smaller tumor size. CT manifestations of strong intensity, LN enlargement, and no calcification could indicate a higher possibility of pNET. Meanwhile, the similarity in the immunohistochemical profile indicates that the two tumors could potentially develop from the same origin.

Factors associated with misdiagnosis of preoperative endoscopic ultrasound in patients with pancreatic cystic neoplasms undergoing surgical resection

PURPOSE

Pancreatic cystic lesions (PCLs) include various types of cysts. Accurate preoperative diagnosis is essential to avoid unnecessary surgery on benign cysts. In this study, we aimed to identify the factors associated with misdiagnosis in preoperative endoscopic ultrasound (EUS) imaging and clinicopathological findings for PCLs.

METHODS

This study included 302 surgically resected patients with PCLs who underwent preoperative EUS. The preoperative EUS findings were re-evaluated and compared with the pathological diagnosis. Additionally, the factors associated with misdiagnosis of PCLs were investigated by multivariate analysis.

RESULTS

The preoperative diagnoses of PCLs were intraductal papillary mucinous neoplasm (IPMN) in 213 patients (70.5%), mucinous cystic neoplasm (MCN) in 33 patients (10.9%), serous neoplasm (SN) in 7 patients (2.3%), cystic degeneration cyst of solid tumor in 46 patients (15.2%), and pancreatic cancer with cystic degeneration in three patients (1.0%). A discrepancy between preoperative and postoperative diagnosis was found in 47 patients (15.6%). Based on the pathological diagnosis, the sensitivity of preoperative EUS imaging was IPMN 97.6% (206/211), MCN 90.0% (18/19), cystic degeneration 87.1% (27/31), and SN 15.4% (2/13). Multivariate analysis revealed that main pancreatic duct (MPD) communication ( - ) (odds ratio (OR), 4.54; 95% confidence interval (CI) 1.29-15.9), honeycomb-like structure ( +) (OR, 14.7; 95% CI 2.61-83.3), and MPD size ≦ 2 mm (OR, 16.3; 95% CI 3.93-67.6) were independently associated with misdiagnosis.

CONCLUSION

For cases in which MCN and cystic degeneration of solid tumor are presumed based on preoperative EUS imaging and cases with PCLs with a honeycomb-like structure, diagnosis with multimodalities or fluid analysis with EUS-guided fine-needle aspiration should be considered.

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.

MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas

BACKGROUND

This study aims to investigate the value of radiomics parameters derived from contrast enhanced (CE) MRI in differentiation of hypovascular non-functional pancreatic neuroendocrine tumors (hypo-NF-pNETs) and solid pseudopapillary neoplasms of the pancreas (SPNs).

METHODS

Fifty-seven SPN patients and twenty-two hypo-NF-pNET patients were enrolled. Radiomics features were extracted from T1WI, arterial, portal and delayed phase of MR images. The enrolled patients were divided into training cohort and validation cohort with the 7:3 ratio. We built four radiomics signatures for the four phases respectively and ROC analysis were used to select the best phase to discriminate SPNs from hypo-NF-pNETs. The chosen radiomics signature and clinical independent risk factors were integrated to construct a clinic-radiomics nomogram.

RESULTS

SPNs occurred in younger age groups than hypo-NF-pNETs (P < 0.0001) and showed a clear preponderance in females (P = 0.0185). Age was a significant independent factor for the differentiation of SPNs and hypo-NF-pNETs revealed by logistic regression analysis. With AUC values above 0.900 in both training and validation cohort (0.978 [95% CI, 0.942-1.000] in the training set, 0.907 [95% CI, 0.765-1.000] in the validation set), the radiomics signature of the arterial phase was picked to build a clinic-radiomics nomogram. The nomogram, composed by age and radiomics signature of the arterial phase, showed sufficient performance for discriminating SPNs and hypo-NF-pNETs with AUC values of 0.965 (95% CI, 0.923-1.000) and 0.920 (95% CI, 0.796-1.000) in the training and validation cohorts, respectively. Delong Test did not demonstrate statistical significance between the AUC of the clinic-radiomics nomogram and radiomics signature of arterial phase.

CONCLUSION

CE-MRI-based radiomics approach demonstrated great potential in the differentiation of hypo-NF-pNETs and SPNs.

Role of Endoscopic Ultrasound in the Diagnosis of Pancreatic Neuroendocrine Neoplasms

Although pancreatic neuroendocrine neoplasms (PNENs) are relatively rare tumors, their number is increasing with advances in diagnostic imaging modalities. Even small lesions that are difficult to detect using computed tomography or magnetic resonance imaging can now be detected with endoscopic ultrasound (EUS). Contrast-enhanced EUS is useful, and not only diagnosis but also malignancy detection has become possible by evaluating the vascularity of tumors. Pathological diagnosis using EUS with fine-needle aspiration (EUS-FNA) is useful when diagnostic imaging is difficult. EUS-FNA can also be used to evaluate the grade of malignancy. Pooling the data of the studies that compared the PNENs grading between EUS-FNA samples and surgical specimens showed a concordance rate of 77.5% (κ-statistic = 0.65, 95% confidence interval = 0.59–0.71, p < 0.01). Stratified analysis for small tumor size (2 cm) showed that the concordance rate was 84.5% and the kappa correlation index was 0.59 (95% confidence interval = 0.43–0.74, p < 0.01). The evolution of ultrasound imaging technologies such as contrast-enhanced and elastography and the artificial intelligence that analyzes them, the evolution of needles, and genetic analysis, will further develop the diagnosis and treatment of PNENs in the future.

Emodin Reverses Gemcitabine Resistance of Pancreatic Cancer Cell Lines Through Inhibition of IKKβ/NF-κB Signaling Pathway

Background

Pancreatic cancer is one of the most malignant tumors, and gemcitabine has been considered as the standard treatment and been widely utilized as a first-line drug for advanced pancreatic cancer, but gemcitabine-resistance always occurs after a short period of treatment.

Methods

Two pancreatic cancer cell lines Panc-1 and MIA-PaCa-2 were used as the study subject and their gemcitabine-resistant cells were established. Both drug-resistant cells were divided into four groups: blank, emodin, gemcitabine, and emodin+gemcitabine. Cell viability was detected by MTT assay. Flow cytometry was performed to detect cell apoptosis rate and P-gp function. Quantitative real-time polymerase chain reaction and Western blotting were used to detect Survivin, XIAP, Caspase-9/3, NF-κB p65, IKKβ and IκB-α mRNA/protein expressions, respectively. Electrophoretic mobility shift assay (EMSA) was performed to detect NF-κB binding activity. Rhodamine 123 efflux assay was used to detect P-gp function.

Results

Emodin could inhibit cell activity in all cell lines. Both emodin and gemcitabine can significantly increase the apoptosis rate, and the combination of the two drugs can further significantly increase the apoptosis rate in normal pancreatic cancer cell lines. In both drug-resistant pancreatic cancer cell lines, it can be observed that although gemcitabine can increase the apoptosis rate, the effect of promoting apoptosis is significantly lower than that of emodin; the drug combination can still significantly increase the apoptosis rate on the basis of emodin alone. Emodin can significantly reduce the mRNA and protein expression levels of Survivin, XIAP, NF-κB, and IKKβ, and significantly increase the mRNA and protein expression levels of Caspase-3/9 and IκB-α. Emodin significantly reduced NF-κB activity and emodin significantly promoted P-gp fluorescence intensity from Rhodamine 123 efflux assay.

Conclusion

Emodin inhibits the expression of IKKβ, thereby inhibiting the expression and activity of downstream NF-κB, and inhibits P-gp function at the same time, ultimately achieving the purpose of reversing the drug-resistance of pancreatic cancer cell lines.