PNET-PRISM: a multicenter-validated radiomics nomogram for noninvasive grading of pancreatic neuroendocrine tumors

TL;DR

A new CT-based radiomics model called PNET-PRISM accurately grades pancreatic neuroendocrine tumors noninvasively, offering a safer alternative when biopsies fail.

Contribution

PNET-PRISM is a validated radiomics nomogram that improves noninvasive grading of PNETs and outperforms clinical models.

Findings

PNET-PRISM achieved AUCs of 0.92, 0.89, and 0.87 in training, validation, and external test sets.

The model provided accurate grading in 52% of cases where EUS-FNA yielded insufficient tissue.

M-DLR Score was significantly associated with progression-free survival (HR = 2.05).

Abstract

Accurate preoperative grading of pancreatic neuroendocrine tumors (PNETs) is essential for optimal treatment selection, yet endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) yields inadequate tissue in up to 40% of cases and carries procedural risks, necessitating reliable noninvasive alternatives.

This multicenter retrospective study included 407 surgically confirmed PNET patients across training (n = 244), validation (n = 106), and external test (n = 57) cohorts. We developed a pancreatic radiomics integrated scoring model for PNET (PNET-PRISM), integrating multidimensional CT radiomics features from intratumoral, peritumoral, habitat, and deep learning domains using automated segmentation. A multidimensional deep learning radiomics score (M-DLR Score) was constructed from 13,542 features and combined with clinical variables for preoperative grade prediction.

PNET-PRISM demonstrated robust performance with AUCs of 0.92, 0.89, and 0.87 in training, validation, and external test sets, respectively, significantly outperforming clinical-only models (ΔAUC = 0.15–0.22, all p < 0.001). The model achieved perfect sensitivity (100%) in external validation and provided accurate grading in 13 of 25 patients (52%) where EUS-FNA yielded insufficient tissue. Net Reclassification Improvement analysis demonstrated significant improvement over clinical models across all datasets (NRI = 0.318–0.406, p ≤ 0.070). M-DLR Score stratification showed a significant association with progression-free survival (HR = 2.050, 95% CI: 1.484–2.833, p < 0.001).

This validated radiomics-based nomogram serves as a powerful noninvasive decision-support tool for PNET risk stratification, effectively complementing EUS-FNA limitations and enabling optimized treatment pathways, particularly when biopsy is contraindicated or nondiagnostic.

This CT-based radiomics nomogram reliably grades pancreatic neuroendocrine tumors (PNETs) and predicts prognosis. This study addresses endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) limitations and advances clinical radiology by enabling safer triage and personalized management when tissue diagnosis is uncertain or unavailable.

A CT-based pancreatic radiomics integrated scoring model for PNET (PNET-PRISM) helps when endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) fails.In 407 patients, PRISMPNET-PRISM achieved a high area under the curve (AUC) and 100% external sensitivity for triage.The multidimensional deep learning radiomics (M-DLR) score stratified progression-free survival (hazard ratio (HR) ≈ 2.05) and rescued nondiagnostic biopsies.

A CT-based pancreatic radiomics integrated scoring model for PNET (PNET-PRISM) helps when endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) fails.

In 407 patients, PRISMPNET-PRISM achieved a high area under the curve (AUC) and 100% external sensitivity for triage.

The multidimensional deep learning radiomics (M-DLR) score stratified progression-free survival (hazard ratio (HR) ≈ 2.05) and rescued nondiagnostic biopsies.

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