A ¹⁸F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma.

ABSTRACT:

Background

To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

The study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor vo

SUBMITTER: Yuan P 

PROVIDER: S-EPMC11556142 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature