Clinical outcome and long-term survival of 150 consecutive patients with pancreatic neuroendocrine tumors: A comprehensive analysis by the World Health Organization 2010 grading classification

Palliative surgery: state of the science and future directions

Palliative surgical procedures are operations that aim to alleviate symptoms in a patient with serious, life-limiting illness. They are common, particularly within the field of surgical oncology. However, few high-quality studies have attempted to measure the durability of improvements in symptoms and quality of life after palliative surgery. Furthermore, many of the studies that do exist are outdated and employ highly inconsistent definitions of palliative surgery. Consequently, the paucity of robust and reliable evidence on the benefits, risks, and trade-offs of palliative surgery hampers clinical decision-making for patients and their surgeons. The evidence for palliative surgery suggests that, with effective communication about goals of care and careful patient selection, palliative surgery can provide symptomatic relief and reduce healthcare burdens for certain seriously ill patients.

Prognostic value of CT-based radiomics in grade 1-2 pancreatic neuroendocrine tumors

BACKGROUND

Surgically resected grade 1-2 (G1-2) pancreatic neuroendocrine tumors (PanNETs) exhibit diverse clinical outcomes, highlighting the need for reliable prognostic biomarkers. Our study aimed to develop and validate CT-based radiomics model for predicting postsurgical outcome in patients with G1-2 PanNETs, and to compare its performance with the current clinical staging system.

METHODS

This multicenter retrospective study included patients who underwent dynamic CT and subsequent curative resection for G1-2 PanNETs. A radiomics-based model (R-score) for predicting recurrence-free survival (RFS) was developed from a development set (441 patients from one institution) using least absolute shrinkage and selection operator-Cox regression analysis. A clinical model (C-model) consisting of age and tumor stage according to the 8th American Joint Committee on Cancer staging system was built, and an integrative model combining the C-model and the R-score (CR-model) was developed using multivariable Cox regression analysis. Using an external test set (159 patients from another institution), the models' performance for predicting RFS and overall survival (OS) was evaluated using Harrell's C-index. The incremental value of adding the R-score to the C-model was evaluated using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

The median follow-up periods were 68.3 and 59.7 months in the development and test sets, respectively. In the development set, 58 patients (13.2%) experienced recurrence and 35 (7.9%) died. In the test set, tumors recurred in 14 patients (8.8%) and 12 (7.5%) died. In the test set, the R-score had a C-index of 0.716 for RFS and 0.674 for OS. Compared with the C-model, the CR-model showed higher C-index (RFS, 0.734 vs. 0.662, p = 0.012; OS, 0.781 vs. 0.675, p = 0.043). CR-model also showed improved classification (NRI, 0.330, p < 0.001) and discrimination (IDI, 0.071, p < 0.001) for prediction of 3-year RFS.

CONCLUSIONS

Our CR-model outperformed the current clinical staging system in prediction of the prognosis for G1-2 PanNETs and added incremental value for predicting postoperative recurrence. The CR-model enables precise identification of high-risk patients, guiding personalized treatment planning to improve outcomes in surgically resected grade 1-2 PanNETs.

Value of markers of systemic inflammation for the prediction of postoperative progression in patients with pancreatic neuroendocrine tumors

Background

Non-invasive prognostic predictors for rare pancreatic neuroendocrine tumors (PNETs) are lacking. We aimed to approach the prognostic value of preoperative systemic inflammatory markers in patients with PNETs.

Methods

The clinical data of 174 patients with PNETs undergoing surgical treatment were retrospectively analyzed to explore the correlation of neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and platelet to white blood cell ratio (PWR) with clinicopathological parameters and the progression of tumor after the operation. The optimal cutoff values for predictors and the area under the curve (AUC) of the receiver operating characteristic (ROC) were estimated. Univariate and multivariate Cox proportional hazards models were used to assess the relation between NLR, LMR, PLR, and progression-free survival (PFS), examined by the Kaplan-Meier and log-rank tests.

Results

The scores of the NLR (P = 0.039) and PLR (P = 0.011) in the progression group were significantly higher than those in the progression-free group, and the LMR was significantly lower than those in the progression-free group (P = 0.001). The best cutoff values of NLR, LMR, and PLR before operation were 2.28, 4.36, and 120.91. The proportions of tumor progression in the high NLR group (P = 0.007) and high PLR group (P = 0.013) obviously increased, and the proportion of tumor development in the low LMR group was higher than that in the high LMR group (P < 0.001). The K-M survival curve showed that the progression-free survival rate was lower in the high NLR group (P = 0.004), the low LMR group (P < 0.001), and the high PLR group (P = 0.018). The results of the multivariate Cox proportional hazards model suggested that preoperative LMR (HR = 3.128, 95% CI: 1.107~8.836, P = 0.031) was an independent predictor of PFS.

Conclusion

The markers of systemic inflammation, especially LMR, can predict the postoperative progression of PNETs.

Pancreatic neuroendocrine tumor: prediction of tumor grades by radiomics models based on ultrasound images

OBJECTIVE

We aimed to investigate whether the radiomics analysis based on B-mode ultrasound (BMUS) images could predict histopathological tumor grades in pancreatic neuroendocrine tumors (pNETs).

METHODS

A total of 64 patients with surgery and histopathologically confirmed pNETs were retrospectively included (34 male and 30 female, mean age 52.4 ± 12.2 years). Patients were divided into training cohort (n = 44) and validation cohort (n = 20). All pNETs were classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity according to WHO 2017 criteria. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator were used for feature selection. Receiver operating characteristic curve analysis was used to evaluate the model performance.

RESULTS

Finally, 18 G1 pNETs, 35 G2 pNETs, and 11 G3 pNETs patients were included. The radiomic score derived from BMUS images to predict G2/G3 from G1 displayed a good performance with an area under the receiver operating characteristic curve of 0.844 in the training cohort, and 0.833 in the testing cohort. The radiomic score achieved an accuracy of 81.8% in the training cohort and 80.0% in the testing cohort, a sensitivity of 0.750 and 0.786, a specificity of 0.833 and 0.833 in the training/testing cohorts. Clinical benefit of the score also exhibited superior usefulness of the radiomic score, as shown by the decision curve analysis.

CONCLUSIONS

Radiomic data constructed from BMUS images have the potential for predicting histopathological tumor grades in patients with pNETs.

ADVANCES IN KNOWLEDGE

The radiomic model constructed from BMUS images has the potential for predicting histopathological tumor grades and Ki-67 proliferation indexes in patients with pNETs.

Prevalence of Sarcopenia and Impact on Survival in Patients with Metastatic Gastroenteropancreatic Neuroendocrine Tumours

Sarcopenia in patients with cancer is associated with adverse outcomes such as shorter survival. However, there exists little evidence regarding the prevalence of sarcopenia in patients with metastatic gastroenteropancreatic neuroendocrine tumours (GEP-NETs). Patients with a histologically confirmed newly diagnosed metastatic GEP-NET between 2006 and 2018, CT scan, and anthropometric data at diagnosis were included in this study. CT scans were analysed for the presence of sarcopenia and correlated with overall survival (OS). In total, 183 patients, 87 male (48%), with a median age of 62 years (IQR 52-68 years), were included. In 44 patients (24%), there was a pancreas NET, and in 136 patients, there was a small bowel NET (74%). Sarcopenia was present in 128 patients (69%) and unrelated to BMI (median 25.1). There were significant survival differences between patients with pancreatic and small bowel NETs at 86 vs. 141 months, respectively (p = 0.04). For patients with pancreatic NETs, the presence of sarcopenia was independently associated with shorter OS (HR 3.79 95% CI 1.1-13.03, p-value 0.035). A high prevalence of sarcopenia at the time of diagnosis of a metastatic GEP-NET was seen and associated with worse OS in patients with pancreatic NETs. Further research should focus on how to reverse sarcopenia and its impact on OS and/or quality of life.

Development and validation of a novel nomogram for predicting survival rate in pancreatic neuroendocrine neoplasms

BACKGROUND

Over the past decades, the incidence and prevalence of pancreatic neuroendocrine neoplasms (pNENs) have steadily increased. However, accurate prediction of the prognosis and treatment of this condition are currently challenging. This study aims to develop and validate a personalized nomogram to predict the survival of patients with pNENs.

MATERIALS AND METHODS

A total of 9739 patients with pNENs were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, the patients were randomly assigned to a derivation cohort (n = 6874) and a validation cohort (n = 2865). The survival of patients was assessed using the Cox proportional hazards (PHs) regression analysis. Then, the nomogram that predicted 3-and 5-year survival rates were developed in the derivation cohort. Further, the predictive performance of the nomogram was evaluated through discrimination and calibration.

RESULTS

The Cox regression analysis revealed that age, differentiation, the extent of tumor, M staging, and surgery were independent prognostic predictors for pNENs. The nomogram showed superior discrimination capability than AJCC staging in both derived and validation cohorts (C-index: 0.874 versus 0.721 and 0.833 versus 0.721). The calibration curves showed that the practical and predicted survival rates effectively coincided, specifically for the 3-year survival rate.

CONCLUSION

Our nomogram is a valuable tool for the prediction of the survival rate for patients with pNENs; this may promote individualized prognostic evaluation and treatment.

Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms

PURPOSE

To evaluate the reproducibility of textural features of pancreatic neuroendocrine neoplasms (PNENs), obtained under various CT-scanning conditions.

METHODS AND MATERIALS

We included 12 patients with PNENs and 2 contrast enhanced CT (CECT): 1) from our center according to standard CT-protocol; 2) from another institution. Two radiologists independently segmented the entire neoplasm volume using a 3D region of interest by LIFEx application on the arterial phase and then copied it to the other phases. 52 texture features were calculated for each phase. As a criterion for the segmentation consistency, a value of neoplasm volume was compared using the Bland-Altman method. The Kendall concordance coefficient was calculated to assess the texture features reproducibility in three scenarios: 1) different radiologists, same CECT; 2) same radiologist, different CECT; 3) different radiologists, different CECT.

RESULTS

For the scenario 1 the neoplasm volumes (except one large PNEN) were found within two standard deviations; this indicates high consistency of the segmentation. For the first scenario, Kendall's coefficient exceeded a threshold of 0.7 for all 52 features for all CT phases. For the second and third scenario, the concordance coefficient exceeded a threshold of 0.7 in 38, 28, 42, 45 and in 36, 25, 36, 44 features for the native, arterial, venous and delayed phases, respectively.

CONCLUSION

The highest reproducibility was found in the first scenario compared to the second and third: 100 % vs. 74 % and 67 %. Reproducible texture features can be reliably used to assess the PNENs structure.

Systematic Review and Metaanalysis of Lymph Node Metastases of Resected Pancreatic Neuroendocrine Tumors

BACKGROUND

The optimal surgical strategy for pancreatic neuroendocrine tumors (PNETs) is unknown. However, current guidelines recommend a watch-and-wait strategy for small nonfunctional PNETs (NF-PNETs). The aim of this study is to investigate the risk stratification and prognostic significance of lymph node metastasis (LNM) of PNETs to guide decision-making for lymphadenectomy.

PATIENTS AND METHODS

The MEDLINE and Web of Science databases were systematically searched for studies reporting either risk factors of LNM in resected PNETs or survival of patients with LNM. The weighted average incidence of LNM was calculated according to tumor characteristics. Random-effects metaanalyses were performed, and pooled hazard ratios (HR) and their 95% confidence intervals (CI) were calculated to determine the impact of LNM on overall survival (OS). In subgroup analyses, NF-PNETs were assessed.

RESULTS

From a total of 5883 articles, 98 retrospective studies with 13,374 patients undergoing resection for PNET were included. In all PNETs, the weighted median rates of LNM were 11.5% for small (≤ 2 cm) PNETs and 15.8% for G1 PNETs. In NF-PNETs, the rates were 11.2% for small PNETs and 10.3% for G1 PNETs. LNM of all PNETs (HR 3.87, 95% CI 3.00-4.99, P < 0.001) and NF-PNETs (HR 4.98, 95% CI 2.81-8.83, P < 0.001) was associated with worse OS.

CONCLUSIONS

LNM is potentially prevalent even in small and well-differentiated PNETs and is associated with worse prognosis. A watch-and-wait strategy for small NF-PNETs should be reappraised, and oncologic resection with lymphadenectomy can be considered. Prospective and controlled studies are needed in the future.

Prognostic and predictive factors on overall survival and surgical outcomes in pancreatic neuroendocrine tumors: recent advances and controversies

Introduction: Recent advances in diagnostic modalities and therapeutic agents have raised the importance of prognostic factors in predicting overall survival, as well as predictive factors for surgical outcomes, in tailoring therapeutic strategies of patients with pancreatic neuroendocrine neoplasms (panNENs).Areas covered: Numerous recent studies of panNEN patients report the prognostic values of a number of clinically related factors (clinical, laboratory, imaging, treatment-related factors), pathological factors (histological, classification, grading) and molecular factors on long-term survival. In addition, an increasing number of studies showed the usefulness of various factors, specifically biomarkers and molecular makers, in predicting recurrence and mortality related to surgical treatment. Recent findings (from the last 3 years) in each of these areas, as well as recent controversies, are reviewed.Expert commentary: The clinical importance of prognostic and predictive factors for panNENs is markedly increased for both overall outcome and post resection, as a result of recent advances in all aspects of the diagnosis, management and treatment of panNENs. Despite the proven prognostic utility of routinely used tumor grading/classification and staging systems, further studies are required to establish these novel prognostic factors to support their routine clinical use.

Pancreatic neuroendocrine tumors

PURPOSE OF REVIEW

Pancreatic neuroendocrine tumors (pNETs) are a rare, heterogeneous group of pancreatic neoplasms with a wide range of malignant potential. They may manifest as noninfiltrative, slow-growing tumors, locally invasive masses, or even swiftly metastasizing cancers.

RECENT FINDINGS

In recent years, because of the increasing amount of scientific literature available for pNETs, the classification, prognostic stratification criteria, and available consensus guidelines for diagnosis and therapy have been revised and updated.

SUMMARY

The vast majority of new pNET diagnoses consist of incidentally discovered lesions on cross-sectional imaging. The biologic behavior of pNETs is defined by the grade and stage of the tumor. Surgery is the only curative treatment and it, therefore, represents the first therapeutic choice for any localized pNET; however, recent evidence suggests that patients with small (<2 cm), nonfunctioning G1 tumors can be safely observed.An aggressive surgical approach towards liver metastases is recommended in selected cases, as well as liver-directed therapies for disease control. In the presence of unresectable progressive disease, somatostatin analogs, targeted therapies such as everolimus, peptide receptor radionuclide therapy, and systemic chemotherapy are all useful tools for prolonging survival.

Simple Vascular Architecture Classification in Predicting Pancreatic Neuroendocrine Tumor Grade and Prognosis

BACKGROUND AND AIM

Vascularity is a critical feature in the evaluation of pancreatic neuroendocrine tumor (PNET). When done by EUS, contrast agents are recommended. However, vascular architecture (VA) can also be evaluated by routine Doppler flow in EUS without contrast agents. Our aim was to provide a simple VA classification in EUS for PNET grade and prognosis.

METHODS

All pathologically proven PNET cases with EUS between 2012 and 2018 were retrospectively analyzed. The Doppler imaging was retrieved for VA classification. Predictive model construction was performed by machine learning algorithms.

RESULTS

A total of 112 PNET cases were evaluated, among which 93 cases were subjected to VA classification. The VA was classified into type A (peritumoral with or without intratumoral vessels [A1 or A2]); type B (only intratumoral vessels); and type C (flow was absent). The VA classification was significantly correlated with tumor grades: 74% type A1 was G1, 73% type B was G2, and 58% type C was G3. Multivariate analysis indicated that elevated serum CA19-9 and type C classification were the independent predictors of G3 tumor. Five machine learning models were constructed, among which random forest was the best one with an AUC of 0.9972. Low-risk patients classified by this model exhibited better prognosis than high-risk patients (p = 0.0087).

CONCLUSIONS

In the novel simple VA classification, peritumoral, intratumoral, and absent vessels are prone to be G1, G2, and G3, respectively. Combined with serum CA19-9 and lesion size, the VA classification could predict tumor grade and prognosis in PNET.