Pancreatic neuroendocrine tumor: review of heterogeneous spectrum of CT appearance

The value of a nomogram model based on CT imaging features in differentiating duodenal gastrointestinal stromal tumors from pancreatic head neuroendocrine tumors

OBJECTIVE

To construct a nomogram model based on multi-slice spiral CT imaging features to predict and differentiate between duodenal gastrointestinal stromal tumors (GISTs) and pancreatic head neuroendocrine tumors (NENs), providing imaging evidence for clinical treatment decisions.

METHODS

A retrospective collection of clinical information, pathological results, and imaging data was conducted on 115 cases of duodenal GISTs and 76 cases of pancreatic head NENs confirmed by surgical pathology at Zhongshan Hospital Fudan University from November 2013 to November 2022. Comparative analysis was performed on the tumor's maximum diameter, shortest diameter, long diameter/short diameter ratio, tumor morphology, tumor border, central position of the lesion, lesion long-axis direction, the relationship between tumor and common bile duct (CBD), duodenal side ulceration of the lesion, calcification, cystic and solid proportion within the tumor, thickened feeding arteries, tumor neovascularization, distant metastasis, and CT values during plain and enhanced scans in arterial and venous phases. Statistical analysis was conducted using t-tests, Mann-Whitney U tests, and χ2 tests. Univariate and multivariate logistic regression analyses were used to identify independent predictors for differentiating duodenal GISTs from pancreatic head NENs. Based on these independent predictors, a nomogram model was constructed, and the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. The nomogram was validated using a calibration curve, and decision curve analysis was applied to assess the clinical application value of the nomogram.

RESULTS

There were significant differences in the duodenal GISTs group and the pancreatic head NENs group in terms of longest diameter (P < 0.001), shortest diameter (P < 0.001), plain CT value (P < 0.001), arterial phase CT value (P < 0.001), venous phase CT value (P = 0.002), lesion long-axis direction (P < 0.001), central position of the lesion (P < 0.001), the relationship between tumor and CBD(< 0.001), border (P = 0.004), calcification (P = 0.017), and distant metastasis (P = 0.018). Multivariate logistic regression analysis identified uncertain location (OR 0.040, 95% CI 0.003-0.549), near the duodenum (OR 0, 95% CI 0-0.009), with the lesion long-axis direction along the pancreas as a reference, along the duodenum (OR 0.106, 95% CI 0.010-1.156) or no significant difference (OR 4.946, 95% CI 0.453-54.017), and the relationship between tumor and CBD (OR 0.013, 95% CI 0.001-0.180), shortest diameter (OR 0.705, 95% CI 0.546-0.909), and calcification (OR 18.638, 95% CI 1.316-263.878) as independent risk factors for differentiating between duodenal GISTs and pancreatic head NENs (all P values < 0.05). The combined diagnostic model's AUC values based on central position of the lesion, calcification, lesion long axis orientation, the relationship between tumor and CBD, shortest diameter, and the joint diagnostic model were 0.937 (0.902-0.972), 0.700(0.624-0.776), 0.717(0.631-0.802), 0.559 (0.473-0.644), 0.680 (0.603-0.758), and 0.991(0.982-0.999), respectively, with a sensitivity of 97.3% and a specificity of 93.0% for the joint diagnostic model. The nomogram model's AUC value was 0.985(0.973-0.996), with a sensitivity and specificity of 94.7% and 93.9%, respectively. The calibration curve indicated good agreement between predicted and actual risks. Decision curve analysis verified the clinical application value of the nomogram.

CONCLUSION

The nomogram model based on CT imaging features effectively differentiates between duodenal GISTs and pancreatic head NENs, aiding in more precise clinical treatment decisions.

Update in the management of gastroenteropancreatic neuroendocrine tumors

Neuroendocrine neoplasms are a diverse group of neoplasms that can occur in various areas throughout the body. Well-differentiated neuroendocrine tumors (NETs) most often arise in the gastrointestinal tract, termed gastroenteropancreatic neuroendocrine tumors (GEP-NETs). Although GEP-NETs are still uncommon, their incidence and prevalence have been steadily increasing over the past decades. The primary treatment for GEP-NETs is surgery, which offers the best chance for a cure. However, because GEP-NETs are often slow-growing and do not cause symptoms until they have spread widely, curative surgery is not always an option. Significant advances have been made in systemic and locoregional treatment options in recent years, including peptide-receptor radionuclide therapy with α and β emitters, somatostatin analogs, chemotherapy, and targeted molecular therapies.

Comprehensive review of pancreatic acinar cell carcinoma: epidemiology, diagnosis, molecular features and treatment

Pancreatic acinar cell carcinoma is a rare form (0.2-4.3%) of pancreatic neoplasm with unique clinical and molecular characteristics, which largely differ from pancreatic ductal adenocarcinoma. Pancreatic acinar cell carcinoma occurs more frequently in males and can occur in children. Serum lipase is elevated in 24-58% of patients with pancreatic acinar cell carcinoma. Pancreatic acinar cell carcinomas tend to be large at diagnosis (median tumour size: ~5 cm) and are frequently located in the pancreas head. Radiologically, pancreatic acinar cell carcinoma generally exhibits a solid appearance; however, necrosis, cystic changes and intratumoral haemorrhage can occur in larger lesions. Immunostaining is essential for the definitive diagnosis of pancreatic acinar cell carcinoma. Compared with pancreatic ductal adenocarcinoma, pancreatic acinar cell carcinoma has a more favourable prognosis. Although radical surgery is recommended for patients with pancreatic acinar cell carcinoma who do not have distant metastases, the recurrence rate is high. The effectiveness of adjuvant therapy for pancreatic acinar cell carcinoma is unclear. The response to FOLFIRINOX is generally favourable, and some patients achieve a complete response. Pancreatic acinar cell carcinoma has a different genomic profile compared with pancreatic ductal adenocarcinoma. Although genomic analyses have shown that pancreatic acinar cell carcinoma rarely has KRAS, TP53 and CDKN2A mutations, it has a higher prevalence of homologous recombination-related genes, including BRCA1/2 and ATM, than pancreatic ductal adenocarcinoma, suggesting high sensitivity to platinum-containing regimens and PARP inhibitors. Targeted therapies for genomic alternations are beneficial. Therefore, genetic testing is important for patients with pancreatic acinar cell carcinoma to choose the optimal therapeutic strategy.

Qualitative imaging features of pancreatic neuroendocrine neoplasms predict histopathologic characteristics including tumor grade and patient outcome

OBJECTIVES

To identify PanNEN imaging features associated with tumor grade and aggressive histopathological features.

METHODS

Associations between histopathological and imaging features of resected PanNEN were retrospectively tested. Histopathologic features included WHO grade, lymphovascular invasion (LVI), growth pattern (infiltrative, circumscribed), and intratumoral fibrosis (mature, immature). Imaging features included size, degree/uniformity of enhancement, progressive enhancement, contour, infiltrative appearance (infiltrative_im), calcifications, cystic components, tumor thrombus, vascular occlusion (VO), duct dilatation, and atrophy. Multinomial logistic regression analyses evaluated the magnitude of associations. Association of variables with outcome was assessed using Cox-proportional hazards regression.

RESULTS

133 patients were included. 3 imaging features (infiltrative_im, ill-defined contour [contour_ill], and VO) were associated with all histopathologic parameters and poor outcome. Increase in grade increased odds of contour_ill by 15.6 times (p = 0.0001, 95% CI 3.8-64.4). PanNEN with VO were 51.1 times (p = 0.0002, 6.5-398.6) more likely to demonstrate LVI. For PanNEN with contour_ill, infiltrative growth pattern was 51.3 times (p < 0.0001, 9.1-288.4), and fibrosis was 14 times (p = 0.0065, 2.1-93.7) more likely. Contour_ill was associated with decreased recurrence-free survival (p = 0.0003, HR 18.29, 3.83-87.3) and VO (p = 0.0004, HR6.08, 2.22-16.68) with decreased overall survival.

CONCLUSIONS

Infiltrative_im, contour_ill, and VO on imaging are associated with higher grade/histopathological parameters linked to tumor aggression, and poor outcome.

Evolutionary Trajectories of Primary and Metastatic Pancreatic Neuroendocrine Tumors Based on Genomic Variations

Liver metastases are common in pancreatic neuroendocrine tumors (PanNETs) patients and they are considered a poor prognostic marker. This study aims to analyze the spatiotemporal patterns of genomic variations between primary and metastatic tumors, and to identify the key related biomolecular pathways. We performed next-generation sequencing on paired tissue specimens of primary PanNETs (n = 11) and liver metastases (n = 12). Low genomic heterogeneity between primary PanNETs and liver metastases was observed. Genomic analysis provided evidence that polyclonal seeding is a prevalent event during metastatic progression, and may be associated with the progression-free survival. Besides this, copy number variations of BRCA1/BRCA2 seem to be associated with better prognosis. Pathways analysis showed that pathways in cancer, DNA repair, and cell cycle regulation-related pathways were significantly enriched in primary PanNETs and liver metastases. The study has shown a high concordance of gene mutations between the primary tumor and its metastases and the shared gene mutations may occur during oncogenesis and predates liver metastasis, suggesting an earlier onset of metastasis in patients with PanNETs, providing novel insight into genetic changes in metastatic tumors of PanNETs.

Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

Purpose

Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs).

Methods

In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People's Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group (n = 108) and the recurrent group (n = 117), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data (P < 0.05) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set.

Results

Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all P < 0.05). The area under the receiver operating characteristic (ROC) curves (AUC) of the combined model was significantly higher [AUC:0.824, 95% CI 0.0342 (0.751-0.883)] than that of the clinical data model [AUC:0.786, 95% CI 0.0384(0.709-0.851)] and the radiomics model [AUC:0.712, 95% CI 0.0426(0.631-0.785)]. The decision curve also confirmed that the combined model had a higher clinical net benefit. The same results were achieved in the test set.

Conclusion

The prognosis of patients with GP-NENs is generally poor. The combined model based on clinical data and CT radiomics can help to early predict the prognosis of patients with GP-NENs, and then necessary interventions could be provided to improve the survival rate and quality of life of patients.

Total Pancreatectomy for Multicentric Cystic Neuroendocrine Tumor of the Pancreas: A Case Report

Pancreatic neuroendocrine tumors (PNETs) are uncommon pancreatic neoplasms with malignant potential, heterogeneous clinical behavior, as well as imaging appearance. These tumors represent less than 3% of all pancreatic neoplasms with typical CT presentation as solid, well-circumscribed, hypervascular lesions. Cystic PNET is a rare pancreatic tumor which is nowadays more often detected due to the widespread use of high-resolution cross-sectional imaging. They are mainly solitary lesions most commonly localized in the body and the tail of the pancreas. Due to cystic presentation these lesions often present a diagnostic challenge to both experienced radiologists and pathologists. Herein, we present a rare case of synchronous, multiple cystic and solid pancreatic neuroendocrine tumors, which due to their extensiveness required total dudenopancreatectomy with splenectomy. Histopathological findings confirmed microscopic and macroscopic cystic components as well as typical solid variants of neuroendocrine tumors along the entire pancreas.

Machine intelligence in non-invasive endocrine cancer diagnostics

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.

Tumor-to-tumor metastasis: an extremely rare combination with renal cell carcinoma as the donor and a pancreatic neuroendocrine tumor as the recipient

BACKGROUND

Tumor-to-tumor metastasis is a rare phenomenon in which primary tumor cells metastasize hematogenously into another tumor. Herein, we report an extremely rare case of a renal cell carcinoma metastasis into a pancreatic neuroendocrine tumor exhibiting a tumor-to-tumor metastasis. Ours is the third reported case worldwide.

CASE PRESENTATION

The patient, a 72-year-old male, was referred to our hospital for further examination and treatment due to high levels of prostate-specific antigen. A left renal tumor and pancreatic head tumor were revealed incidentally on screening computed tomography. There were suspected to be a renal cell carcinoma and primary pancreatic neuroendocrine tumor or pancreatic metastasis from the renal cell carcinoma according to preoperative examination. The left nephrectomy and subtotal stomach-preserving pancreaticoduodenectomy were performed because of the pancreatic tumor indicated for operation in either case of diagnosis. Postoperative pathological examination showed a diagnosis of clear cell renal cell carcinoma for the left renal tumor. The pancreatic tumor was diagnosed with clear cell renal cell carcinoma metastasis into the pancreatic neuroendocrine tumor, that is to say tumor-to-tumor metastasis.

CONCLUSION

In some cases, conservative approach is selected for pancreatic neuroendocrine tumor patients who meet some requirements. However, if such patients exhibit tumor-to-tumor metastasis which combines with renal cell carcinoma and pancreatic neuroendocrine tumor as this case, conservative approach leads to progression of renal cell carcinoma. Therefore, conceiving the possibility of tumor-to-tumor metastasis, it is necessary to carefully choose a treatment plan for pancreatic neuroendocrine tumor patients associated with renal cell carcinoma, not easily choosing conservative approach.

Added Value of Tomoelastography for Characterization of Pancreatic Neuroendocrine Tumor Aggressiveness Based on Stiffness

Simple Summary

The prediction of pancreatic neuroendocrine tumor (PNET) aggressiveness is important for treatment planning. The aim of this study was to evaluate the diagnostic performance of magnetic resonance elastography (MRE) with tomoelastography postprocessing (tomoelastography) in differentiating PNET from healthy pancreatic tissue and to correlate PNET stiffness with aggressiveness using asphericity derived from positron emission tomography (PET) as reference. In this prospective study we showed in a group of 13 patients with PNET that tomoelastography detected PNET by increased stiffness (p < 0.01) with a high diagnostic performance (AUC = 0.96). PNET was positively correlated with PET derived asphericity (r = 0.81). Tomoelastography provides quantitative imaging markers for the detection of PNET and the prediction of greater tumor aggressiveness by increased stiffness.

Abstract

Purpose: To evaluate the diagnostic performance of tomoelastography in differentiating pancreatic neuroendocrine tumors (PNETs) from healthy pancreatic tissue and to assess the prediction of tumor aggressiveness by correlating PNET stiffness with PET derived asphericity. Methods: 13 patients with PNET were prospectively compared to 13 age-/sex-matched heathy volunteers (CTR). Multifrequency MR elastography was combined with tomoelastography-postprocessing to provide high-resolution maps of shear wave speed (SWS in m/s). SWS of pancreatic neuroendocrine tumor (PNET-T) were compared with nontumorous pancreatic tissue in patients with PNET (PNET-NT) and heathy pancreatic tissue (CTR). The diagnostic performance of tomoelastography was evaluated by ROC-AUC analysis. PNET-SWS correlations were calculated with Pearson’s r. Results: SWS was higher in PNET-T (2.02 ± 0.61 m/s) compared to PNET-NT (1.31 ± 0.18 m/s, p < 0.01) and CTR (1.26 ± 0.09 m/s, p < 0.01). An SWS-cutoff of 1.46 m/s distinguished PNET-T from PNET-NT (AUC = 0.89; sensitivity = 0.85; specificity = 0.92) and a cutoff of 1.49 m/s differentiated pancreatic tissue of CTR from PNET-T (AUC = 0.96; sensitivity = 0.92; specificity = 1.00). The SWS of PNET-T was positively correlated with PET derived asphericity (r = 0.81; p = 0.01). Conclusions: Tomoelastography provides quantitative imaging markers for the detection of PNET and the prediction of greater tumor aggressiveness by increased stiffness.

Anatomic and Functional Imaging of Neuroendocrine Tumors

OPINION STATEMENT

Neuroendocrine tumors (NETs) can occur in a wide variety of organs and display a spectrum of pathologic behavior. Accurate and effective imaging is paramount to the diagnosis, staging, therapy, and surveillance of patients with NET. There have been continuous advancements in the imaging of NET which includes anatomic and functional techniques.

Differential Diagnosis of Nonhypervascular Pancreatic Neuroendocrine Neoplasms From Pancreatic Ductal Adenocarcinomas, Based on Computed Tomography Radiological Features and Texture Analysis

RATIONALE AND OBJECTIVES

To determine computed tomography (CT) radiological features and texture features that are rewarding in differentiating nonhypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs).

MATERIALS AND METHODS

We compared patients to pathologically proven nonhypervascular PNENs and age-matched controls with pathologically proven PDACs in a 1:2 ratio. Preoperative CT images in the arterial phase (AP) and portal vein phase (PVP) were obtained. Two radiologists independently reviewed the morphological characteristics of each tumor. Three-dimensional regions of interest (ROIs), drawn using ITK-SNAP software, were input into AK software (Artificial Intelligent Kit, GE) to extract texture features from AP and PVP images. Differences between PNENs and PDACs were analyzed with the chi-squared test, least absolute shrinkage and selection operator, kappa statistics, and uni- and multivariate logistic regression analyses.

RESULTS

In total, 40 nonhypervascular PNENs and 80 PDACs were evaluated. Maximum diameter on axial section, margin, calcification, vascularity in the tumor, and tumor heterogeneity were significantly different between PDACs and nonhypervascular PNENs. Multivariate analysis showed well-defined tumor margin (odds ratio: 21.0) and presence of calcification (odds ratio: 4.4) were significant predictors of nonhypervascular PNENs. The area under the receiver operating characteristic curve of the radiological feature model, AP texture model, and PVP texture model were 0.780, 0.855, and 0.929, respectively, based on logistic regression.

CONCLUSION

A well-defined margin and calcification in the tumor were helpful in discriminating nonhypervascular PNENs from PDACs. Texture analysis of contrast-enhanced CT images could be beneficial in differentially diagnosing nonhypervascular PNENs and PDACs.

Unusual Case of a Pancreatic Neuroendocrine Tumor Containing a Central Scar

We report a previously unreported case of a pancreatic neuroendocrine tumor with a central scar mimicking a serous neoplasm. To our knowledge, this atypical imaging morphology of pancreatic neuroendocrine tumor has not been described before. Our report adds to the body literature that describes atypical imaging variants of neuroendocrine tumors and highlights that clinicians should be aware of the broad imaging characteristics of neuroendocrine tumors.

The role of multimodal imaging in guiding resectability and cytoreduction in pancreatic neuroendocrine tumors: focus on PET and MRI

Pancreatic neuroendocrine tumors (pNETs) are rare neoplasms that secrete peptides and neuro-amines. pNETs can be sporadic or hereditary, syndromic or non-syndromic with different clinical presentations and prognoses. The role of medical imaging includes locating the tumor, assessing its extent, and evaluating the feasibility of curative surgery or cytoreduction. Pancreatic NETs have very distinctive phenotypes on CT, MRI, and PET. PET have been demonstrated to be very sensitive to detect either well-differentiated pNETs using ⁶⁸Gallium somatostatin receptor (SSTR) radiotracers, or more aggressive undifferentiated pNETS using ¹⁸F-FDG. A comprehensive interpretation of multimodal imaging guides resectability and cytoreduction in pNETs. The imaging phenotype provides information on the differentiation and proliferation of pNETs, as well as the spatial and temporal heterogeneity of tumors with prognostic and therapeutic implications. This review provides a structured approach for standardized reading and reporting of medical imaging studies with a focus on PET and MR techniques. It explains which imaging approach should be used for different subtypes of pNET and what a radiologist should be looking for and reporting when interpreting these studies.