Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors.
Journal:
Academic radiology
Published Date:
Dec 4, 2023
Abstract
RATIONALE AND OBJECTIVES: To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) using different machine learning (ML) methods.
Authors
Keywords
Adult
Aged
Algorithms
Diagnosis, Differential
Female
Humans
Machine Learning
Male
Middle Aged
Neoplasm Grading
Neuroendocrine Tumors
Pancreatic Neoplasms
Radiographic Image Interpretation, Computer-Assisted
Retrospective Studies
Sensitivity and Specificity
Support Vector Machine
Tomography, X-Ray Computed