Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.

ABSTRACT:

Purpose

This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in identifying sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC), and non-Hodgkin's lymphoma (NHL) using various machine learning (ML) models.

Methods

A retrospective analysis of 132 patients (50 with SCC, 42 with NHL, 40 with ACC) was conducted. The dataset was split 80/20 into training and testing cohorts. HC radiomics and DTL features were extracted from T2-weighted, ADC, and contrast-enhanced T1-weighted MRI images. ResNet50, a pre-trained convolutional neural network, was used for DTL feature extraction. LASSO regression was applied to select features and create radiomic signatures. Seven ML models were

SUBMITTER: Lin N 

PROVIDER: S-EPMC11938957 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature