Prognostic validity of the American joint committee on cancer eighth edition staging system for well-differentiated pancreatic neuroendocrine tumors

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.

A relook at gastroenteropancreatic neuroendocrine tumours as per 2019 WHO classification-A tertiary centre experience

INTRODUCTION

Neuroendocrine neoplasm of GIT (gastrointestinal tract) and pancreas is heterogenous with variable clinical features and disease outcomes. Despite multiple attempts of risk stratification by grading and staging, some have unpredictable clinical courses. Well-differentiated grade 3 neuroendocrine tumour (G3NET) is a recent subcategory introduced in the 2019 WHO classification based on morphology, molecular profile and prognosis distinguishing it from neuroendocrine carcinoma(NEC). This study aimed at describing the spectrum of NENs encountered in a tertiary centre with focus on reclassifying previously reported G3 tumours into G3 NET and NEC and comparing the survival between them.

METHODOLOGY

This is an 8-year retrospective study of all gastro-entero-pancreatic neuroendocrine neoplasms reclassified according to the 2019 WHO classification based on morphology and Ki-67 index with analysis of the survival rates between the categories. Minimum follow-up period was 20 months.

RESULTS

Eighty-six patients with NENs of gastro-entero-pancreas were included, with median age group of 40-60 years (age range 9 to 79 years) and male:female ratio of 1.7:1. The tumour grade correlated with the TNM staging and most of the syndromic NETs were low grade. Eleven percent of the tumours were reclassified as well-differentiated G3NETs. The survival of G3 NETs was higher than NEC.

CONCLUSION

Grading of NEN is vital for therapeutic decisions and for prognostication. Currently, morphology is the key to recognise the well-differentiated G3 NETs, but can be subject to interobserver variability. Molecular surrogates may play a role in accurately identifying these entities, the validity of which is warranted.