New insights in diagnosis and treatment of gastroenteropancreatic neuroendocrine neoplasms

Predictive and prognostic nomogram models for liver metastasis in colorectal neuroendocrine neoplasms: a large population study

Background

In recent years, the incidence of patients with colorectal neuroendocrine neoplasms (CRNENs) has been continuously increasing. When diagnosed, most patients have distant metastases. Liver metastasis (LM) is the most common type of distant metastasis, and the prognosis is poor once it occurs. However, there is still a lack of large studies on the risk and prognosis of LM in CRNENs. This study aims to identify factors related to LM and prognosis and to develop a predictive model accordingly.

Methods

In this study, the Surveillance, Epidemiology, and End Results (SEER) database was used to collect clinical data from patients with CRNENs. The logistic regression analyses were conducted to identify factors associated with LM in patients with CRNENs. The patients with LM formed the prognostic cohort, and Cox regression analyses were performed to evaluate prognostic factors in patients with liver metastasis of colorectal neuroendocrine neoplasms (LM-CRNENs). Predictive and prognostic nomogram models were constructed based on the multivariate logistic and Cox analysis results. Finally, the capabilities of the nomogram models were verified through model assessment metrics, including the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) curve.

Results

This study ultimately encompassed a total of 10,260 patients with CRNENs. Among these patients, 501 cases developed LM. The result of multivariate logistic regression analyses indicated that histologic type, tumor grade, T stage, N stage, lung metastasis, bone metastasis, and tumor size were independent predictive factors for LM in patients with CRNENs (p < 0.05). Multivariate Cox regression analyses indicated that age, primary tumor site, histologic type, tumor grade, N stage, tumor size, chemotherapy, and surgery were independent prognostic factors (p < 0.05) for patients with LM-CRNENs. The predictive and prognostic nomogram models were established based on the independent factors of logistic and Cox analyses. The nomogram models can provide higher accuracy and efficacy in predicting the probability of LM in patients with CRNENs and the prognosis of patients with LM.

Conclusion

The factors associated with the occurrence of LM in CRNENs were identified. On the other hand, the relevant prognostic factors for patients with LM-CRNENs were also demonstrated. The nomogram models, based on independent factors, demonstrate greater efficiency and accuracy, promising to provide clinical interventions and decision-making support for patients.

Critical considerations for the management of gastrointestinal mixed neuroendocrine non-neuroendocrine neoplasms and pure neuroendocrine carcinomas

Mixed neuroendocrine non-neuroendocrine neoplasms constitute rare tumors that are located mainly in the gastrointestinal (GI) tract and have high degrees of malignancy, and the frequency of these tumors has been increasing. They consist of a neuroendocrine neoplastic component with another component of adenocarcinoma usually and have a dismal prognosis. The rare GI pure neuroendocrine carcinoma is highly aggressive and requires complex and extensive management since a genetic distinction exists between it and GI non-neuroendocrine neoplasms, which are generally slow-growing lesions. The most common GI-mixed neuroendocrine non-neuroendocrine neoplasms are colorectal, followed by gastric, mainly in the gastroesophageal junction. Current imaging modalities of nuclear medicine and radiology play important roles in the accuracy of diagnosis. Liquid biopsy may contribute to early detection and timely diagnosis. Ultrasonography, either endoscopic or abdominal, is a technique that contributes to a diagnosis; additionally, contrast-enhanced ultrasonography is very helpful in follow-up appointments. Histopathology establishes a definite diagnosis and stage by evaluating the cell differentiation grade and the cell proliferation index Ki67. The genetic profile can be valuable in diagnosis and gene therapy. Surgical resection with wide lymphadenectomy, whenever possible, and adjuvant chemotherapy constitute the main therapeutic management strategies. Targeted therapy and immunotherapy achieve encouraging results.

Deep Learning and Automatic Differentiation of Pancreatic Lesions in Endoscopic Ultrasound: A Transatlantic Study

INTRODUCTION

Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include mucinous (M-PCN) and nonmucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commonest pancreatic solid lesion (PSL), followed by pancreatic neuroendocrine tumor (P-NET). Although EUS is preferred for pancreatic lesion evaluation, its diagnostic accuracy is suboptimal. This multicentric study aims to develop a convolutional neural network (CNN) for detecting and distinguishing PCN (namely M-PCN and NM-PCN) and PSL (particularly P-DAC and P-NET).

METHODS

A CNN was developed with 378 EUS examinations from 4 international reference centers (Centro Hospitalar Universitário São João, Hospital Universitario Puerta de Hierro Majadahonda, New York University Hospitals, Hospital das Clínicas Faculdade de Medicina da Universidade de São Paulo). About 126.000 images were obtained-19.528 M-PCN, 8.175 NM-PCN, 64.286 P-DAC, 29.153 P-NET, and 4.858 normal pancreas images. A trinary CNN differentiated normal pancreas tissue from M-PCN and NM-PCN. A binary CNN distinguished P-DAC from P-NET. The total data set was divided into a training and testing data set (used for model's evaluation) in a 90/10% ratio. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, and accuracy.

RESULTS

The CNN had 99.1% accuracy for identifying normal pancreatic tissue, 99.0% and 99.8% for M-PCN and NM-PCN, respectively. P-DAC and P-NET were distinguished with 94.0% accuracy.

DISCUSSION

Our group developed the first worldwide CNN capable of detecting and differentiating the commonest PCN and PSL in EUS images, using examinations from 4 centers in 2 continents, minimizing the impact of the demographic bias. Larger multicentric studies are needed for technology implementation.

Navigating the landscape of theranostics in nuclear medicine: current practice and future prospects

Theranostics refers to the combination of diagnostic biomarkers with therapeutic agents that share a specific target expressed by diseased cells and tissues. Nuclear medicine is an exciting component explored for its applicability in theranostic concepts in clinical and research investigations. Nuclear theranostics is based on the employment of radioactive compounds delivering ionizing radiation to diagnose and manage certain diseases employing binding with specifically expressed targets. In the realm of personalized medicine, nuclear theranostics stands as a beacon of potential, potentially revolutionizing disease management. Studies exploring the theranostic profile of radioactive compounds have been presented in this review along with a detailed explanation of radioactive compounds and their theranostic applicability in several diseases. It furnishes insights into their applicability across diverse diseases, elucidating the intricate interplay between these compounds and disease pathologies. Light is shed on the important milestones of nuclear theranostics beginning with radioiodine therapy in thyroid carcinomas, MIBG labelled with iodine in neuroblastoma, and several others. Our perspectives have been put forth regarding the most important theranostic agents along with emerging trends and prospects.

Risk factors for positive resection margins after endoscopic resection for gastrointestinal neuroendocrine tumors

BACKGROUND

In recent years, the incidence of gastrointestinal neuroendocrine tumors (GI-NETs) has remarkably increased due to the widespread use of screening gastrointestinal endoscopy. Currently, the most common treatments are surgery and endoscopic resection. Compared to surgery, endoscopic resection possesses a higher risk of resection margin residues for the treatment of GI-NETs.

METHODS

A total of 315 patients who underwent surgery or endoscopic resection for GI-NETs were included. We analyzed their resection modality (surgery, ESD, EMR), margin status, Preoperative marking and Prognosis.

RESULTS

Among 315 patients included, 175 cases underwent endoscopic resection and 140 cases underwent surgical treatment. A total of 43 (43/175, 24.57%) and 10 (10/140, 7.14%) patients exhibited positive resection margins after endoscopic resection and surgery, respectively. Multivariate regression analysis suggested that no preoperative marking and endoscopic treatment methods were risk factors for resection margin residues. Among the patients with positive margin residues after endoscopic resection, 5 patients underwent the radical surgical resection and 1 patient underwent additional ESD resection. The remaining 37 patients had no recurrence during a median follow-up of 36 months.

CONCLUSIONS

Compared with surgery, endoscopic therapy has a higher margin residual rate. During endoscopic resection, preoperative marking may reduce the rate of lateral margin residues, and endoscopic submucosal dissection may be preferred than endoscopic mucosal resection. Periodical follow-up may be an alternative method for patients with positive margin residues after endoscopic resection.

Risk of metastasis and survival in patients undergoing different treatment strategies with T1 colonic neuroendocrine tumors

PURPOSE

The efficacy and safety of local excision (LE) for small (< 1‒2 cm) colonic neuroendocrine tumors (NETs) is controversial due to the higher metastasis risk when compared with rectal NETs. The study aimed to evaluate the metastasis risk of T1 colonic NETs and compare patients' long-term prognosis after LE or radical surgery (RS).

METHODS

The Surveillance Epidemiology and End Results database was used to identify patients with T1 colonic NETs (2004‒2015). Multivariable logistic regression was performed to assess factors associated with metastasis risk. Propensity score matching was used to balance the variables. Cancer-specific survival (CSS) and overall survival (OS) were calculated to estimate the prognosis of patients with T1N0M0 colonic NETs who underwent LE or RS.

RESULTS

Of the 610 patients with colonic NETs, 46 (7.54%) had metastasis at diagnosis. Tumor size (11-20 mm) (OR = 9.51; 95% confidence interval (CI): 4.32‒21.45; P < 0.001), right colon (OR = 15.79; 95% CI 7.20‒38.56; P < 0.001), submucosal infiltration (OR = 2.08; 95% CI 0.84‒5.57; P = 0.125) were independent risk factors associated with metastasis. Of the 515 patients with T1N0M0 colonic NETs, the overall long-term prognosis of LE was as good as that of RS groups (after matching, 5-year CSS: 97.9% vs. 94.6%, P = 0.450; 5-year OS: 92.7% vs. 85.6%, P = 0.009).

CONCLUSION

Tumor size (11‒20 mm) and site (right colon) are associated with metastasis in T1 colonic NETs. In the absence of metastasis, LE could be a viable option for 0‒10 mm T1 colonic NETs with well/moderate differentiation in the left colon in terms of long-term survival.

Risk factors, prognostic factors, and nomograms for distant metastases in patients with gastroenteropancreatic neuroendocrine tumors: a population-based study

Background

Patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) have a poor prognosis for distant metastasis. Currently, there are no studies on predictive models for the risk of distant metastasis in GEP-NETs.

Methods

In this study, risk factors associated with metastasis in patients with GEP-NETs in the Surveillance, Epidemiology, and End Results (SEER) database were analyzed by univariate and multivariate logistic regression, and a nomogram model for metastasis risk prediction was constructed. Prognostic factors associated with distant metastasis in patients with GEP-NETs were analyzed by univariate and multivariate Cox, and a nomogram model for prognostic prediction was constructed. Finally, the performance of the nomogram model predictions is validated by internal validation set and external validation set.

Results

A total of 9145 patients with GEP-NETs were enrolled in this study. Univariate and multivariate logistic analysis demonstrated that T stage, N stage, tumor size, primary site, and histologic types independent risk factors associated with distant metastasis in GEP-NETs patients (p value < 0.05). Univariate and multivariate Cox analyses demonstrated that age, histologic type, tumor size, N stage, and primary site surgery were independent factors associated with the prognosis of patients with GEP-NETs (p value < 0.05). The nomogram model constructed based on metastasis risk factors and prognostic factors can predict the occurrence of metastasis and patient prognosis of GEP-NETs very effectively in the internal training and validation sets as well as in the external validation set.

Conclusion

In conclusion, we constructed a new distant metastasis risk nomogram model and a new prognostic nomogram model for GEP-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.

Gastroenteropancreatic Neuroendocrine Tumors-Current Status and Advances in Diagnostic Imaging

Gastroenteropancreatic neuroendocrine neoplasia (GEP-NEN) is a heterogeneous and complex group of tumors that are often difficult to classify due to their heterogeneity and varying locations. As standard radiological methods, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT) are available for both localization and staging of NEN. Nuclear medical imaging methods with somatostatin analogs are of great importance since radioactively labeled receptor ligands make tumors visible with high sensitivity. CT and MRI have high detection rates for GEP-NEN and have been further improved by developments such as diffusion-weighted imaging. However, nuclear medical imaging methods are superior in detection, especially in gastrointestinal NEN. It is important for radiologists to be familiar with NEN, as it can occur ubiquitously in the abdomen and should be identified as such. Since GEP-NEN is predominantly hypervascularized, a biphasic examination technique is mandatory for contrast-enhanced cross-sectional imaging. PET/CT with somatostatin analogs should be used as the subsequent method.

Early-onset pancreatic neuroendocrine neoplasms: A distinct disease with improved survival compared with old individuals

Background

The incidence, clinicopathologic characteristics, treatment patterns, and survival of early-onset pancreatic neuroendocrine neoplasms (EOPanNENs) have not been well explored.

Methods

Patients diagnosed with PanNENs were identified from the SEER database between 2000 and 2018. EOPanNENs were defined as diagnosis in patients aged less than 50 years, while the remaining were defined as later-onset pancreatic neuroendocrine neoplasms (LOPanNENs). Incidence, clinical features, management, and prognosis were analyzed in our study. Multivariable analyses were performed to identify factors associated with overall survival (OS) in EOPanNENs and LOPanNENs, respectively.

Results

A total of 5172 patients with PanNENs were included: 1267 (24.5%) in the EOPanNENs cohort and 3905 (75.5%) in the LOPanNENs cohort. The age-adjusted incidence rate significantly increased among later-onset cases, while it remained relatively stable in early-onset cases. EOPanNENs were more frequently to be female, unmarried, and with better tumor differentiation compared with LOPanNENs. Of note, early-onset patients presented with a higher rate of lymph node involvement, and they were more likely to receive surgical treatment. For local-regional disease at presentation, surgery alone was the most frequently used regimen over the last two decades. With regard to distant stage, a combination of surgery and chemotherapy was more often utilized. Risk factors for PanNENs survival were more correlated with LOPanNENs compared with EOPanNENs. The OS and cancer-specific survival (CSS) were significantly better in the EOPanNENs group. Further analyses showed that EOPanNENs ≤ 2cm were associated with more favorable survival outcomes than EOPanNENs&gt;2cm.

Conclusion

EOPanNENs are a clinically rare and distinct entity from LOPanNENs. The advantages in survival for the EOPanNENs cohort over time were largely driven by the indolent clinical courses including better tumor differentiation and intensified surgical treatment. Further investigations are warranted to better understand the characteristics of this disease subgroup.

An Insight on Functioning Pancreatic Neuroendocrine Neoplasms

Pancreatic neuroendocrine neoplasms (PanNENs) are rare neoplasms arising from islets of the Langerhans in the pancreas. They can be divided into two groups, based on peptide hormone secretion, functioning and nonfunctioning PanNENs. The first group is characterized by different secreted peptides causing specific syndromes and is further classified into subgroups: insulinoma, gastrinoma, glucagonoma, somatostatinoma, VIPoma and tumors producing serotonin and adrenocorticotrophic hormone. Conversely, the second group does not release peptides and is usually associated with a worse prognosis. Today, although the efforts to improve the therapeutic approaches, surgery remains the only curative treatment for patients with PanNENs. The development of high-throughput techniques has increased the molecular knowledge of PanNENs, thereby allowing us to understand better the molecular biology and potential therapeutic vulnerabilities of PanNENs. Although enormous advancements in therapeutic and molecular aspects of PanNENs have been achieved, there is poor knowledge about each subgroup of functioning PanNENs.Therefore, we believe that combining high-throughput platforms with new diagnostic tools will allow for the efficient characterization of the main differences among the subgroups of functioning PanNENs. In this narrative review, we summarize the current landscape regarding diagnosis, molecular profiling and treatment, and we discuss the future perspectives of functioning PanNENs.

Molecular factors, diagnosis and management of gastrointestinal tract neuroendocrine tumors: An update

The prevalence of gastrointestinal neuroendocrine tumors (GI-NETs) is increasing, and despite recent advances in their therapy, it remains inadequate in patients with advanced well-differentiated neuroendocrine tumors. These tumors present many challenges concerning the molecular basis and genomic profile, pathophysiology, clinicopathological features, histopathologic classification, diagnosis and treatment. There has been an ongoing debate on diagnostic criteria and clinical behavior, and various changes have been made over the last few years. Neuroendocrine carcinoma of the gastrointestinal system is a rare but highly malignant neoplasm that is genetically distinct from gastrointestinal system neuroendocrine tumors (NETs). The diagnosis and management have changed over the past decade. Emerging novel biomarkers and metabolic players in cancer cells are useful and promising new diagnostic tools. Progress in positron emission tomography-computerized tomography and scintigraphy with new radioactive agents (⁶⁴Cu-DOTATATE or ⁶⁸Ga-DOTATATE) replacing enough octreoscan, has improved further the current diagnostic imaging. Promising results provide targeted therapies with biological agents, new drugs, chemotherapy and immunotherapy. However, the role of surgery is important, since it is the cornerstone of management. Simultaneous resection of small bowel NETs with synchronous liver metastases is a surgical challenge. Endoscopy offers novel options not only for diagnosis but also for interventional management. The therapeutic option should be individualized based on current multidisciplinary information.