Evaluation of contrast-enhanced computed tomography for the differential diagnosis of hypovascular pancreatic neuroendocrine tumors from chronic mass-forming pancreatitis

Impact of functionality and grading on survival in pancreatic neuroendocrine tumor patients receiving peptide receptor radionuclide therapy

Background

Peptide receptor radionuclide therapy (PRRT) is a well-established treatment option for neuroendocrine tumors (NET), yet randomized controlled trials have not provided data on its impact on overall survival. The real-world efficacy of PRRT and its association with tumor functionality and grading in pancreatic neuroendocrine tumors (PanNET) remains underexplored.

Methods

A retrospective analysis of 166 patients with histologically confirmed metastatic PanNET was performed. Subgroup analyses examined progression-free survival (PFS) and overall survival (OS) following PRRT cycles, stratified by tumor grading, tumor functionality and bone metastases.

Results

Of 166 patients, 100 (60.2%) received PRRT with a median of four cycles. In the PRRT cohort, 68% of patients had deceased. PFS after four and eight consecutive cycles was 20 and 18 months, respectively (p=0.4). OS for the entire cohort was 79 months, with patients receiving 4+ cycles of PRRT having an OS of 87 months and those receiving 5+ cycles achieving an OS of 100 months. Patients with grade 1 or 2 tumors had a significantly longer median OS of 97 months compared to 74.5 months for grade 3 tumors (p = 0.0055). There was no significant difference in OS between functioning and non-functioning tumors after PRRT. Patients with bone metastases who received PRRT had a significantly shorter OS than those without (74 vs. 89 months, p = 0.013). In 19% of patients who received PRRT, therapy was discontinued due to progressive disease, toxicity or death.

Conclusions

Patients receiving extended cycles of PRRT showed improved survival outcomes in metastatic PanNET, particularly in patients with lower tumor grades and without bone metastases. No survival difference was seen between functioning and non-functioning PanNET, while patients with grade 3 tumors and bone metastases had significantly shorter survival despite PRRT.

A nomogram to preoperatively predict the aggressiveness of pancreatic neuroendocrine tumors based on CT features and 3D CT radiomic features

OBJECTIVES

Combining Computed Tomography (CT) intuitive anatomical features with Three-Dimensional (3D) CT multimodal radiomic imaging features to construct a model for assessing the aggressiveness of pancreatic neuroendocrine tumors (pNETs) prior to surgery.

METHODS

This study involved 242 patients, randomly assigned to training (170) and validation (72) cohorts. Preoperative CT and 3D CT radiomic features were used to develop a model predicting pNETs aggressiveness. The aggressiveness of pNETs was characterized by a combination of factors including G3 grade, nodal involvement (N + status), presence of distant metastases, and/or recurrence of the disease.

RESULTS

Three distinct predictive models were constructed to evaluate the aggressiveness of pNETs using CT features, 3D CT radiomic features, and their combination. The combined model demonstrated the greatest predictive accuracy and clinical applicability in both the training and validation sets (AUCs (95% CIs) = 0.93 (0.90-0.97) and 0.89 (0.79-0.98), respectively). Subsequently, a nomogram was developed using the features from the combined model, displaying strong alignment between actual observations and predictions as indicated by the calibration curves. Using a nomogram score of 86.06, patients were classified into high- and low-aggressiveness groups, with the high-aggressiveness group demonstrating poorer overall survival and shorter disease-free survival.

CONCLUSION

This study presents a combined model incorporating CT and 3D CT radiomic features, which accurately predicts the aggressiveness of PNETs preoperatively.

Quantitative evaluation of pancreatic neuroendocrine tumors utilizing dual-source CT perfusion imaging

OBJECTIVE

We aimed to quantitatively analyze the perfusion characteristics of pancreatic neuroendocrine tumors (pNETs) utilizing dual-source CT imaging.

METHODS

Dual-source CT perfusion scans were obtained from patients with pNETs confirmed by surgical or biopsy pathology. Perfusion parameters, including blood flow (BF), blood volume (BV), capillary permeability surface (PS), mean transit time (MTT), contrast transit time to the start (TTS), and contrast transit time to the peak (TTP), were statistically analyzed and compared with nearby healthy tissue. Time density curves (TDCs) were plotted to further understand the dynamic enhancement characteristics of the tumors. Additionally, receiver operating characteristic curves (ROCs) were generated to assess their diagnostic value.

RESULTS

Twenty patients with pNETs, containing 26 lesions, were enrolled in the study, including 6 males with 8 lesions and 14 females with 18 lesions. The average values of BF, BV, PS, MTT, TTP and TTS for the 26 lesions (336.61 ± 216.72 mL/100mL/min, 41.96 ± 16.99 mL/100mL, 32.90 ± 11.91 mL/100 mL/min, 9.44 ± 4.40 s, 19.14 ± 5.6 s, 2.57 ± 1.6 s) were different from those of the adjacent normal pancreatic tissue (44.32 ± 55.35 mL/100mL/min, 28.64 ± 7.95 mL/100mL, 26.69 ± 14.88 mL/100 mL/min, 12.89 ± 3.69 s, 20.33 ± 5.18 s, 2.69 ± 1.71 s). However, there were no statistical differences in PS and TTS between the lesions and the adjacent normal pancreatic tissue (P > 0.05). The areas under the ROC curve for BF, BV, and PS were all greater than 0.5, whereas the areas under the ROC curve for MTT, TTP, and TTS were all less than 0.5.

CONCLUSION

CT perfusion parameters such as BF, BV, MTT, and TTP can distinguish pNETs from healthy tissue. The area under the ROC curve for BF, BV, and PS demonstrates substantial differentiating power for diagnosing pNET lesions.

Liver metastasized pancreatic neuroendocrine tumor in a 17-year-old female: A case report

Key Clinical Message

Pancreatic neuroendocrine tumors (PNETs) are rare and often misdiagnosed due to their vague symptoms and tumor heterogeneity. Early detection using computed tomography (CT) is essential, particularly in regions without access to advanced diagnostic tools like immunohistochemistry and genetic testing.

Abstract

Neuroendocrine tumors (NETs) are rare tumors in adults and extremely rare in the pediatric population, as pancreatic NETs (pNETs) have an incidence rate of <0.1 per million. We present a case of a 17-year-old female with a liver metastasized pNET. A 17-year-old female with a history of intermittent abdomen-back pain presented to the clinic with severe upper abdominal pain radiating to the shoulder. The routine tests were normal. An ultrasound showed multiple lesions in the liver, which were confirmed by a computed tomography (CT) that uncovered a pancreatic lesion too. A liver biopsy proved it was a metastasized pNET with positive NET markers on IMC staining. The metaiodobenzylguanidine (MIBG) scan flared the liver lesion. The patient was started on Octreotide long-acting release (LAR) 30 mg once monthly. The rarity of these tumors makes their diagnosis difficult, but they should not be omitted and must be considered when there are long-lasting symptoms that are not compatible with common illnesses. These tumors are curable in their early stages.

Comparison between solid pseudopapillary neoplasms of the pancreas and pancreatic ductal adenocarcinoma with cystic changes using computed tomography

BACKGROUND

Solid pseudopapillary neoplasms of the pancreas (SPN) share similar imaging findings with pancreatic ductal adenocarcinoma with cystic changes (PDAC with cystic changes), which may result in unnecessary surgery.

AIM

To investigate the value of computed tomography (CT) in differentiation of SPN from PDAC with cystic changes.

METHODS

This study retrospectively analyzed the clinical and imaging findings of 32 patients diagnosed with SPN and 14 patients diagnosed with PDAC exhibiting cystic changes, confirmed through pathological diagnosis. Quantitative and qualitative analysis was performed, including assessment of age, sex, tumor size, shape, margin, density, enhancement pattern, CT values of tumors, CT contrast enhancement ratios, "floating cloud sign," calcification, main pancreatic duct dilatation, pancreatic atrophy, and peripancreatic invasion or distal metastasis. Multivariate logistic regression analysis was used to identify relevant features to differentiate between SPN and PDAC with cystic changes, and receiver operating characteristic curves were obtained to evaluate the diagnostic performance of each variable and their combination.

RESULTS

When compared to PDAC with cystic changes, SPN had a lower age (32 years vs 64 years, P < 0.05) and a slightly larger size (5.41 cm vs 3.90 cm, P < 0.05). SPN had a higher frequency of "floating cloud sign" and peripancreatic invasion or distal metastasis than PDAC with cystic changes (both P < 0.05). No significant difference was found with respect to sex, tumor location, shape, margin, density, main pancreatic duct dilatation, calcification, pancreatic atrophy, enhancement pattern, CT values of tumors, or CT contrast enhancement ratios between the two groups (all P > 0.05). The area under the receiver operating characteristic curve of the combination was 0.833 (95% confidence interval: 0.708-0.957) with 78.6% sensitivity, 81.3% specificity, and 80.4% accuracy in differentiation of SPN from PDAC with cystic changes.

CONCLUSION

A larger tumor size, "floating cloud sign," and peripancreatic invasion or distal metastasis are useful CT imaging features that are more common in SPN and may help discriminate SPN from PDAC with cystic changes.

Synchronous neuroendocine liver metastases in comparison to primary pancreatic neuroendocrine tumors on MRI and SSR-PET/CT

Background

The study aimed to compare and correlate morphological and functional parameters in pancreatic neuroendocrine tumors (pNET) and their synchronous liver metastases (NELM), while also assessing prognostic imaging parameters.

Methods

Patients with G1/G2 pNET and synchronous NELM underwent pretherapeutic abdominal MRI with DWI and 68Ga-DOTATATE/TOC PET/CT were included. ADC (mean, min), SNR_art and SNT_T2 (SNR on arterial phase and on T2) and SUV (max, mean) for three target NELM and pNET, as well as tumor-free liver and spleen (only in PET/CT) were measured. Morphological parameters including size, location, arterial enhancement, cystic components, T2-hyperintensity, ductal dilatation, pancreatic atrophy, and vessel involvement were noted. Response evaluation used progression-free survival (PFS) with responders (R;PFS>24 months) and non-responders (NR;PFS ≤ 24 months).

Results

33 patients with 33 pNETs and 95 target NELM were included. There were no significant differences in ADC and SUV values between NELM and pNET. 70% of NELM were categorized as hyperenhancing lesions, whereas the pNETs exhibited significantly lower rate (51%) of hyperenhancement (p<0.01) and significant lower SNR_art. NELM were qualitatively and quantitatively (SNR_T2) significantly more hyperintense on T2 compared to pNET (p=0.01 and p<0.001). NELM of R displayed significantly lower ADCmean value in comparison to the ADC mean value of pNET (0.898 versus 1.037x10-3mm²/s,p=0.036). In NR, T2-hyperintensity was notably higher in NELM compared to pNET (p=0.017). The hepatic tumor burden was significantly lower in the R compared to the NR (10% versus 30%).

Conclusions

Arterial hyperenhancement and T2-hyperintensity differ between synchronous NELM and pNET. These findings emphasize the importance of a multifaceted approach to imaging and treatment planning in patients with these tumors as well as in predicting treatment responses.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma

BACKGROUND

One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma (PDAC) is that most patients are usually diagnosed at late stages. There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.

AIM

To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC.

METHODS

A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography (CT) within 30 d prior to surgery were included in the study. Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system. Radiomics features were extracted from the region of interest (ROI) for each patient using Analysis Kit software. The most important and predictive radiomics features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy maximum relevance (MRMR) method. Random forest (RF) method was used to construct the radiomics model, and 10-times leave group out cross-validation (LGOCV) method was used to validate the robustness and reproducibility of the model.

RESULTS

A total of 792 radiomics features (396 from late arterial phase and 396 from portal venous phase) were extracted from the ROI for each patient using Analysis Kit software. Nine most important and predictive features were selected using Mann-Whitney U test, univariate logistic regression analysis, and MRMR method. RF method was used to construct the radiomics model with the nine most predictive radiomics features, which showed a high discriminative ability with 97.7% accuracy, 97.6% sensitivity, 97.8% specificity, 98.4% positive predictive value, and 96.8% negative predictive value. The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.

CONCLUSION

The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

A nomogram to preoperatively predict the aggressiveness of non-functional pancreatic neuroendocrine tumors based on CT features

OBJECTIVES

To develop a nomogram to predict the aggressiveness of non-functional pancreatic neuroendocrine tumors (NF-pNETs) based on preoperative computed tomography (CT) features.

METHODS

This study included 176 patients undergoing radical resection for NF-pNETs. These patients were randomly divided into the training (n = 123) and validation sets (n = 53). A nomogram was developed based on preoperative predictors of aggressiveness of the NF-pNETs which were identified by univariable and multivariable logistic regression analysis. The aggressiveness of NF-pNETs was defined as a composite measure including G3 grading, N+, distant metastases, and/ or disease recurrence.

RESULTS

Altogether, the number of patients with highly aggressive NF-pNETs was 37 (30.08 %) and 15 (28.30 %) in the training and validation sets, respectively. Multivariable logistic regression analysis identified that tumor size, biliopancreatic duct dilatation, lymphadenopathy, and enhancement pattern were preoperative predictors of aggressiveness. Those variables were used to develop a nomogram with good concordance statistics of 0.89 and 0.86 for predicting aggressiveness in the training and validation sets, respectively. With a nomogram score of 59, patients with NF-pNETs were divided into low-aggressive and high-aggressive groups. The high-aggressive group had decreased overall survival (OS) and disease-free survival (DFS). Moreover, the nomogram showed good performance in predicting OS and DFS at 3, 5, and 10 years.

CONCLUSION

The nomogram integrating CT features helped preoperatively predict the aggressiveness of NF-pNETs and could potentially facilitate clinical decision-making.

Duodenal neuroendocrine neoplasms on enhanced CT: establishing a diagnostic model with duodenal gastrointestinal stromal tumors in the non-ampullary area and analyzing the value of predicting prognosis

OBJECTIVE

To identify CT features and establish a diagnostic model for distinguishing non-ampullary duodenal neuroendocrine neoplasms (dNENs) from non-ampullary duodenal gastrointestinal stromal tumors (dGISTs) and to analyze overall survival outcomes of all dNENs patients.

MATERIALS AND METHODS

This retrospective study included 98 patients with pathologically confirmed dNENs (n = 44) and dGISTs (n = 54). Clinical data and CT characteristics were collected. Univariate analyses and binary logistic regression analyses were performed to identify independent factors and establish a diagnostic model between non-ampullary dNENs (n = 22) and dGISTs (n = 54). The ROC curve was created to determine diagnostic ability. Cox proportional hazards models were created and Kaplan-Meier survival analyses were performed for survival analysis of dNENs (n = 44).

RESULTS

Three CT features were identified as independent predictors of non-ampullary dNENs, including intraluminal growth pattern (OR 0.450; 95% CI 0.206-0.983), absence of intratumoral vessels (OR 0.207; 95% CI 0.053-0.807) and unenhanced lesion > 40.76 HU (OR 5.720; 95% CI 1.575-20.774). The AUC was 0.866 (95% CI 0.765-0.968), with a sensitivity of 90.91% (95% CI 70.8-98.9%), specificity of 77.78% (95% CI 64.4-88.0%), and total accuracy rate of 81.58%. Lymph node metastases (HR: 21.60), obstructive biliary and/or pancreatic duct dilation (HR: 5.82) and portal lesion enhancement ≤ 99.79 HU (HR: 3.02) were independent prognostic factors related to poor outcomes.

CONCLUSION

We established a diagnostic model to differentiate non-ampullary dNENs from dGISTs. Besides, we found that imaging features on enhanced CT can predict OS of patients with dNENs.

Differential diagnostic value of tumor markers and contrast-enhanced computed tomography in gastric hepatoid adenocarcinoma and gastric adenocarcinoma

OBJECTIVE

This study aimed to investigate the effectiveness of tumor markers and contrast-enhanced computed tomography (CE-CT) in differentiating gastric hepatoid adenocarcinoma (GHA) from gastric adenocarcinoma (GA).

METHODS

This retrospective study included 160 patients (44 with GHA vs. 116 with GA) who underwent preoperative CE-CT. Preoperative serum concentrations of tumor biomarkers and CT imaging features were analyzed, including alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), tumor location, growth pattern, size, enhancement pattern, cystic changes, and mass contrast enhancement. Multivariate logistic regression analyses were performed to evaluate useful tumor markers and CT imaging features for differentiating GHA from GA.

RESULTS

When compared to GA, GHA showed a higher serum AFP [13.27 ng/ml (5.2-340.1) vs. 2.7 ng/ml (2.2-3.98), P <0.001] and CEA levels [4.07 ng/ml (2.73-12.53) vs. 2.42 ng/ml (1.38-4.31), P <0.001]. CT imaging showed GHA with a higher frequency of tumor location in the gastric antrum (P <0.001). GHA had significantly lower attenuation values at the portal venous phase [PCA, (82.34 HU ± 8.46 vs. 91.02 HU ± 10.62, P <0.001)] and delayed phase [DCA, (72.89 HU ± 8.83 vs. 78.27 HU ± 9.51, P <0.001)] when compared with GA. Multivariate logistic regression analyses revealed that tumor location, PCA, and serum AFP level were independent predictors of differentiation between GHA and GA. The combination of these three predictors performed well in discriminating GHA from GA, with an AUC of 0.903, a sensitivity of 86.36%, and a specificity of 81.90%.

CONCLUSIONS

Integrated evaluation of tumor markers and CT features, including tumor location, PCA, and serum AFP, allowed for more accurate differentiation of GHA from GA.

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review

BACKGROUND AND AIMS

Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases.

METHODS

Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist.

RESULTS

A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points.

CONCLUSIONS

DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.

Mass-Forming Chronic Pancreatitis: Diagnostic Performance of PET/CT

Mass-forming chronic pancreatitis and pancreatic ductal adenocarcinoma are most commonly located in the head of pancreas, and there is a marked overlap in clinical features and imaging findings that makes it diagnostically challenging, although prognosis and management of both these entities differ. Differentiation is made even more difficult when surgical exploratory biopsy is obtained. Radical surgical resection remains the standard of care for pancreatic ductal adenocarcinoma and conservative treatment is effective for mass-forming chronic pancreatitis. Misdiagnosis of mass-forming chronic pancreatitis as pancreatic ductal adenocarcinoma results in unnecessary surgical intervention, and misdiagnosis of pancreatic ductal adenocarcinoma as mass-forming chronic pancreatitis results in delay in surgical intervention when required. Fluorodeoxyglucose-positron emission tomography/computed tomography can reliably be used for tissue characterization of mass-forming chronic pancreatitis and for monitoring disease response following treatment. Although differentiation of mass-like lesions of pancreas is reliably made on histopathology, significant false-negative rate is a major drawback that has a negative effect on diagnosis. This case report describes a rare presentation of mass-forming chronic pancreatitis with florid dystrophic calcifications in a 60-year-old male.

Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma

OBJECTIVE

The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method.

RESULTS

In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80).

CONCLUSION

The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.

Hypovascular pancreatic neuroendocrine tumor with hepatic metastases: A case report and literature review

Hypovascular pancreatic neuroendocrine tumors are uncommon pancreatic tumors and commonly misdiagnosed as pancreatic ductal adenocarcinoma or chronic mass-forming pancreatitis. The liver is the organ most commonly affected by neuroendocrine tumor metastases but hepatic neuroendocrine tumor metastases are quite difficult to discriminate from other hepatic metastases and primary hepatic tumors. We describe a case of a 47-year-old man with incidentally detected multiple hepatic lesions on ultrasound. On further imaging technique including computed tomography and magnetic resonance imaging, the patient had an abnormal hypoenhancing lesion at the pancreatic tail and multiple hyperenhancing hepatic metastases that were diagnosed as hypovascular pancreatic well-differentiated neuroendocrine tumor Grade 2 with multiple hypervascular hepatic metastases after liver biopsy and surgery. Neuroendocrine tumor is a rare etiology among hypoenhancing pancreatic tumors, and must be considered to discriminate from pancreatic adenocarcinomas in cases there are multiple hyperenhancing hepatic metastases on the arterial phase without typical washout on the portal venous phase.