Reconsideration of Clinicopathologic Prognostic Factors in Pancreatic Neuroendocrine Tumors for Better Determination of Adverse Prognosis

Subgrading of G2 Pancreatic Neuroendocrine Tumors as 2A (Ki67 3% to < 10%) Versus 2B (10% to ≤ 20%) Identifies Behaviorally Distinct Subsets in Keeping with the Evolving Management Protocols

BACKGROUND

Grade 1/2 PanNETs are mostly managed similarly, typically without any adjunct treatment with the belief that their overall metastasis rate is low. In oncology literature, Ki67-index of 10% is increasingly being used as the cutoff in stratifying patients to different protocols, although there are no systematic pathology-based studies supporting this approach.

METHODS

Ki67-index was correlated with clinicopathologic parameters in 190 resected PanNETs. A validation cohort (n = 145) was separately analyzed.

RESULTS

In initial cohort, maximally selected rank statistics method revealed 12% to be the discriminatory cutoff (close to 10% rule of thumb). G2b cases had liver/distant metastasis rate of almost threefold higher than that of G2a and showed significantly higher frequency of all histopathologic signs of aggressiveness (tumor size, perineural/vascular invasion, infiltrative growth pattern, lymph node metastasis). In validation cohort, these figures were as striking. When all cases were analyzed together, compared with G1, the G2b category had nine times higher liver/distant metastasis rate (6.1 vs. 58.5%; p < 0.001) and three times higher lymph node metastasis rate (20.5 vs. 65.1%; p < 0.001).

CONCLUSIONS

G2b PanNETs act very similar to G3, supporting management protocols that regard them as potential therapy candidates. Concerning local management, metastatic behavior in G2b cases indicate they may not be as amenable for conservative approaches, such as watchful waiting or enucleation. This substaging should be considered into diagnostic guidelines, and clinical trials need to be devised to determine the more appropriate management protocols for G2b (10% to ≤ 20%) group, which shows liver/distant metastasis in more than half of the cases, which at minimum warrants closer follow-up.

Pancreatic Neuroendocrine Microtumors (WHO 2022) Are Not Always Low-Grade Neoplasms: A Case with a Highly Increased Proliferation Rate

Traditionally considered non-functional low proliferative benign neuroendocrine proliferations measuring less than 5 mm, pancreatic (neuro)endocrine microadenomas are now classified as pancreatic neuroendocrine microtumors in the 2022 WHO classification of endocrine and neuroendocrine tumors. This case report discussed the features of an incidentally identified 4.7-mm glucagon-expressing pancreatic neuroendocrine microtumor with MEN1 mutation only, chromosomally stable and an epigenetic alpha-like phenotype. The tumor was associated with an unexplained increased proliferation rate in Ki-67 of 15%. There was no associated DAXX/ATRX deficiency. The presented case challenges the conventional thought of a low proliferative disease of the so-called "pancreatic neuroendocrine microadenomas" and provides additional support to the 2022 WHO classification that also requires grading of these neoplasms. Despite exhibiting molecular features of less aggressive behavior, the case also underscores the biological complexity of pancreatic neuroendocrine microtumors. By recognizing the heterogenous spectrum of neuroendocrine neoplasms, the current case also contributes to ongoing discussions on how to optimize the clinical management of such tumors.

European Neuroendocrine Tumour Society (ENETS) 2023 guidance paper for nonfunctioning pancreatic neuroendocrine tumours

This ENETS guidance paper for well-differentiated nonfunctioning pancreatic neuroendocrine tumours (NF-Pan-NET) has been developed by a multidisciplinary working group, and provides up-to-date and practical advice on the management of these tumours. Using the extensive experience of centres treating patients with NF-Pan-NEN, the authors of this guidance paper discuss 10 troublesome questions in everyday clinical practice. Our many years of experience in this field are still being verified in the light of the results of new clinical, which set new ways of proceeding in NEN. The treatment of NF-Pan-NEN still requires a decision of a multidisciplinary team of specialists in the field of neuroendocrine neoplasms.

A Machine Learning Approach Using [18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor

PURPOSE

We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs).

PROCEDURES

A total of 58 patients with PNETs who underwent pretherapeutic [18F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method. The predictive performances of ML models using neural network (NN) and random forest algorithms were compared by the areas under the receiver operating characteristic curves (AUROCs) and validated by stratified five-fold cross validation.

RESULTS

We developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001).

CONCLUSIONS

Integration of clinical features and [18F]FDG PET-based radiomics using ML algorithms improved the prediction of high-grade PNET and poor prognosis in a non-invasive manner.

Promising targetable biomarkers in pancreatic neuroendocrine tumours

INTRODUCTION

In the treatment scenario of PanNETs-targeted therapies are desired but limited, as rarity and heterogeneity on PanNETs pose limitations to their development.

AREAS COVERED

We performed a literature review searching for promising druggable biomarkers and potential treatments to be implemented in the next future. We focused on treatments which have already reached clinical experimentation, although in early phases. Six targets were identified, namely Hsp90, HIFa, HDACs, CDKs, uPAR, and DDR. Even though biological rational is strong, so far reported efficacy outcomes are quite disappointing. The reason of that should be searched in the patients' heterogeneity, lack of biomarker selection, poor knowledge of interfering mechanisms as well as difficulties in patients accrual. Moreover, different ways to assess treatment efficacy should be considered, other than response rate, in light of the more indolent nature of NETs.

EXPERT OPINION

Development of targeted treatments in PanNETs is still an uncovered area, far behind other more frequent cancers. Rarity of NETs led to accrual of unselected populations, possibly jeopardizing the drug efficacy. Better patients' selection, both in terms of topography, grading and biomarkers is crucial and will help understanding which role targeted therapies can really play in these tumors.

Venous Invasion in Pancreatic Neuroendocrine Tumors Is Independently Associated With Disease-free Survival and Overall Survival

In this study, we evaluated venous invasion and its association with survival in patients with resected pancreatic neuroendocrine tumor (PanNET). Surgical Pathology Archives were searched for pancreatectomies performed for PanNET between October 1, 2005, and December 31, 2019. Hematoxylin and eosin (H&E)-stained slides were evaluated for venous invasion, and Movat's stain was performed in all cases with no venous invasion detected on H&E stains. Pathology reports and electronic medical records were also reviewed. Venous invasion was identified in 23 of 145 (15.9%) cases on H&E stains, and Movat's stain identified additional 34 cases with venous invasion (39.3% overall). Orphan arteries with adjacent well-defined tumor nodules or subtle hyalinizing nodules in hyalinizing tumors are highly specific for venous invasion. In stage I-III cases (n=122), venous invasion was associated with larger tumor size, higher World Health Organization (WHO) tumor grade, perineural invasion, extrapancreatic extension, lymph node metastasis, and liver metastasis ( P <0.05). In univariate analyses, tumor size, WHO grade, venous invasion, perineural invasion, T stage, and lymph node metastasis all correlated with disease-free survival; however, only venous invasion was associated with worse disease-free survival in multivariate analyses ( P <0.01). In all-stage cases, venous invasion was the only attributor associated with worse overall survival in multivariate analyses ( P =0.03). In summary, venous invasion in PanNET can be histologically subtle, and Movat's stain can greatly increase the detection rate. More importantly, enhanced venous invasion by Movat's stain correlates independently with disease-free survival in patients with stage I-III tumors and overall survival in all-stage patients.

Neuroendocrine Neoplasms of the Pancreas: Diagnostic Challenges and Practical Approach

Most pancreatic neuroendocrine neoplasms are slow-growing, and the patients may survive for many years, even after distant metastasis. The tumors usually display characteristic organoid growth patterns with typical neuroendocrine morphology. A smaller portion of the tumors follows a more precipitous clinical course. The classification has evolved from morphologic patterns to the current World Health Organization classification, with better-defined grading and prognostic criteria. Recent advances in molecular pathology have further improved our understanding of the pathogenesis of these tumors. Various issues and challenges remain, including the correct recognition of a neuroendocrine neoplasm, accurate classification and grading of the tumor, and differentiation from mimickers. This review focuses on the practical aspects during the workup of pancreatic neuroendocrine neoplasms and attempts to provide a general framework to help achieve an accurate diagnosis, classification, and grading.

Risk Stratification of Pancreatic Neuroendocrine Neoplasms Based on Clinical, Pathological, and Molecular Characteristics

Pancreatic neuroendocrine neoplasms consist of heterogeneous diseases. Depending on the novel features detected by various modern technologies, their classification and related prognosis predictions continue to change and develop. The role of traditional clinicopathological prognostic factors, including classification systems, is also being refined, and several attempts have been made to predict a more accurate prognosis through novel serum biomarkers, genetic factors, and epigenetic factors that have been identified through various state-of-the-art molecular techniques with multiomics sequencing. In this review article, the latest research results including the traditional approach to prognostic factors and recent advanced strategies for risk stratification of pancreatic neuroendocrine neoplasms based on clinical, pathological, and molecular characteristics are summarized. Predicting prognosis through multi-factorial assessments seems to be more efficacious, and prognostic factors through noninvasive methods are expected to develop further advances in liquid biopsy in the future.

A New Preoperative Scoring System for Predicting Aggressiveness of Non-Functioning Pancreatic Neuroendocrine Neoplasms

The management of non-functioning pancreatic neuroendocrine neoplasms (NF-PanNENs) is still controversial. This study aimed to develop a new scoring system for treatment decisions at initial diagnosis based on the identification of the predictive factors for aggressive NF-PanNENs. Seventy-seven patients who had been pathologically diagnosed with NF-PanNENs were enrolled. We retrospectively reviewed 13 variables that could be assessed preoperatively. Univariate and multivariate stepwise logistic regression analyses were performed to identify factors for the aggressiveness of NF-PanNENs, and a scoring system was developed by assigning weighted points proportional to their β regression coefficient. Tumor size > 20 mm on contrast-enhanced computed tomography, tumor non-vascularity, and Ki-67 labeling index ≥5% on endoscopic ultrasound-guided fine-needle aspiration specimens were identified as independent factors for predicting the aggressiveness of NF-PanNENs. The new scoring system, developed using the identified factors, had an excellent discrimination ability, with area under the curve of 0.92 (95% CI, 0.85–0.99), and good calibration (p = 0.72, Hosmer-Lemeshow test). Ten-year overall survival rates in low-risk (0 point), intermediate-risk (1 to 2 points), and high-risk (3 to 4 points) groups were 100%, 90.9%, and 24.3%, respectively. This new scoring system would be useful for treatment decisions and prognostic prediction at initial diagnosis.