Development and validation of CT-based radiomics deep learning signatures to preoperatively predict lymph node metastasis in non-functional pancreatic neuroendocrine tumor: A multi-cohort study

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Introduction: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to surgical methods, prognosis, recurrence.

Aim(s): To develop and validate a combination model based on contrast-enhanced CT images to preoperatively predict the lymph node metastasis (LNM) in NF-PanNETs.

Materials and methods: Retrospective data were gathered for 320 patients with NF-PanNETs who underwent CT imaging at two institutions (Center 1, n= 236 and Center 2, n=84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of area under the curve (AUC).

Conference:

Presenting Author: Tang W

Authors: Tang W, Chen J, Gu W,

Keywords: Non-functional pancreatic neuroendocrine tumor, Radiomics, Deep learning, Lymph node metastasis,