Prediction of solid pseudopapillary tumor invasiveness of the pancreas based on multiphase contrast-enhanced CT radiomics nomogram.

ABSTRACT:

Objectives

To construct a multiphase contrast-enhanced CT-based radiomics nomogram that combines traditional CT features and radiomics signature for predicting the invasiveness of pancreatic solid pseudopapillary neoplasm (PSPN).

Methods

A total of 114 patients with surgical pathologic diagnoses of PSPN were retrospectively included and classified into training (n = 79) and validation sets (n = 35). Univariate and multivariate analyses were adopted for screening traditional CT features significantly associated with the invasiveness of PSPN as independent predictors, and a traditional CT model was established. Radiomics features were extracted from the contrast-enhanced CT images, and logistic regression analysis was employed to establish a machine learning model, including a

SUBMITTER: Ren D 

PROVIDER: S-EPMC12010104 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature