Established and novel circulating neuroendocrine tumor biomarkers for diagnostic, predictive and prognostic use

Bone and muscle mass characteristics in patients with gastroenteropancreatic neuroendocrine neoplasms

BACKGROUND

Neuroendocrine neoplasms (NEN) are rare tumors arising from neuroendocrine cells most commonly in the gastrointestinal-tract. In recent years, advancements in therapeutics have increased survival rates in patients with NEN leading to a greater clinical burden compared to the general population.

METHODS

The aim of this single-center case-control study was to investigate the incidence of low bone mass and changes in body composition in adult patients diagnosed with gastroenteropancreatic neuroendocrine tumors (GEPNET). Enrolled participants underwent measurements of bone mineral density (BMD) at the lumbar spine (LS), femoral neck (FN), and total hip (TH) and body composition analysis with calculation of total fat-mass (TFM) and relative skeletal mass index (RSMI), by dual X-ray absorptiometry.

RESULTS

Ninety GEPNET patients (28 with Pancreatic-NET, 20 with small-intestine-NET, 42 with gastric-NET), and 50 age and sex-matched controls were enrolled. The mean disease duration was 5±4.4 years, the majority of patients (54/90) was classified as stage-1, and were not receiving systemic-treatment (76/90). The incidence of osteoporosis/osteopenia was threefold higher in the patients' cohort, compared to controls (OR: 3.17 95% CI 1.16-7.8, p < 0.001). Among NEN patients, gastric-NET had the lowest bone mass, especially in LS. In addition, GEPNET patients demonstrated significantly lower TFM and RSMI, compared to controls (TFM: 31.6 ± 9.6 kg vs. 38.6 ± 6.4 kg, respectively, p = 0.03; RSMI: 6.4 ± 1.1 vs. 8.2 ± 0.6, respectively, p < 0.001). Within our patients' cohort, RSMI was significantly associated with LS-BMD (rho = 0.49, p < 0.001) and TH-BMD (rho = 0.58, p < 0.001), and TFM was associated with TH-BMD (rho = 0.31, p = 0.004).

CONCLUSIONS

Patients with GEPNET even at an early stage exhibit significantly lower bone, muscle and fat mass compared to the non-NET population, highlighting the importance of continuous monitoring of the musculoskeletal system in these patients.

Rectal neuroendocrine tumors: Can we predict their behavior?

Rectal neuroendocrine tumors (r-NETs) are the second most common type of neuroendocrine tumor in the gastrointestinal tract, with an increase in incidence in the last decades. They are low-grade tumors and, given their low risk of metastasis, current guidelines recommend endoscopic resection for small lesions. The GATIS predicting score, proposed by Zeng et al, represents an innovative model designed to predict individualized survival outcomes for patients with r-NETs, analyzing the relationship between clinicopathological features and patient prognoses. The authors identified tumor grade, T stage, tumor size, age, and prognostic nutritional index as key prognostic factors, demonstrating that the GATIS Score provides a more accurate prognosis assessment compared to the World Health Organization classification or the tumor-node-metastasis staging system. Nevertheless, further larger prospective studies are necessary, and the scientific community's efforts in this context should be directed toward developing international multicentric prospective studies, with the ultimate aim of accurately defining and understanding the behavior of these conditions.

Pancreatic Neuroendocrine Tumors-Diagnostic Pitfalls of Non-Diabetic Severe Hypoglycemia: Literature Review and Case Report

Background: Hypoglycemia in the case of persons without diabetes is a rare event, being usually, initially misinterpreted based on the symptoms that can mimic various diseases, especially of a neuro-psychiatric nature. In the case of the identification of insulin-mediated hypoglycemia, the evaluation of pancreatic neuroendocrine tumors, which represent the most common and worrisome causes of non-diabetic insulin-mediated hypoglycemia, must be considered. Case Report: We present the case of a 57-year-old patient, hospitalized for a history of approximately one month of recurrent episodes of symptoms suggestive for severe hypoglycemia. The biological evaluation performed during an episode of hypoglycemia showed a plasma glucose value of 44 mg/dL, insulinemia 16.3 µU/mL, C peptide 3.72 ng/mL, HbA1c 4.99%, absence of urinary ketone bodies and anti-insulin antibodies <0.03 U/mL. The CT and MRI examination showed a 15.3/15 mm rounded tumor in the pancreatic corporeo-caudal region. The pancreatic tumor formation was enucleated and the histopathological and immunohistochemical analysis confirmed the diagnosis of the pancreatic neuroendocrine tumor with a positive reaction for chromogranin A, synaptophysin and insulin, without malignancy features (Ki 67 positive in 1% of the tumor cells). The postoperative evolution was favorable, without episodes of hypoglycemia, the fasting insulinemia one day after surgery being 4.1 µU/mL and HbA1c at three weeks postoperatively being 5.51%. Conclusions: The management of patients with hyperinsulinemic hypoglycemia secondary to insulinoma involves multidisciplinary collaboration with an important role in recognizing symptoms suggestive of hypoglycemia in a person without diabetes, initiating biological and imaging evaluation, establishing the optimal therapeutic option and histopathological confirmation.

NETest and Gastro-Entero-Pancreatic Neuroendocrine Tumors: Still Far from Routine Clinical Application? A Systematic Review

Background: Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) are the most prevalent subgroup among NETs and include heterogeneous tumors characterized by different clinical behavior and prognosis. The NETest is a tool based on real-time PCR combined with deep learning strategies to specifically identify tumors with a neuroendocrine genotype. Despite the promising results achieved regarding its utility in the field of GEP-NETs, the NETest has not yet entered into routine clinical practice. Methods: We performed a systematic review aimed at summarizing available evidence on the application of the NETest in both the diagnosis and the prognostic stratification of GEP-NETs. Results: We identified five studies evaluating the diagnostic role of the NETest and nine studies evaluating its prognostic value. The NETest emerged as a reliable biomarker for GEP-NET diagnosis with an accuracy higher than 90%, regardless of tumor stage and grade. However, according to some studies, the NETest showed a low specificity, mainly attributed to interferences with other gastro-intestinal malignancies. In terms of prognostic value, the NETest correlated with the detection of residual disease after surgery in six studies. The NETest was also associated with patients' survival outcomes, namely progression-free survival (PFS) and overall survival (OS) in three studies. Conclusions: According to current systematic review, the value of the NETest both for diagnosis and for prognosis of GEP-NET emerged as robust across different studies. Further prospective analysis on larger GEP-NET series is encouraged to validate this tool, improving patients' diagnosis, management, and follow-up.

CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study

Purpose

To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making.

Methods

A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area under the receiver operating characteristic curve (AUC) and compared by Delong test. Preoperative neuron-specific enolase (NSE) levels were collected as a clinical predictor.

Results

In the training set, the AUCs of the radiomics model (0.878 [95% CI: 0.836, 0.915]) and merged model (0.884 [95% CI: 0.844, 0.919]) significantly outperformed the semantic model (0.718 [95% CI: 0.663, 0.769], p both<.001). In the external test set, the AUCs of the radiomics model (0.787 [95% CI: 0.696, 0.871]), merged model (0.807 [95%CI: 0.720, 0.889]) and semantic model (0.729 [95% CI: 0.631, 0.811]) did not exhibit statistical differences. The radiomics model outperformed NSE in sensitivity in the training set (85.3% vs 20.0%; p <.001) and external test set (88.9% vs 40.7%; p = .002).

Conclusion

The CT radiomics model could non-invasively, effectively and sensitively predict LNEN and LADC presenting as a PSN to assist in treatment strategy selection.