The deep learning radiomics nomogram helps to evaluate the lymph node status in cervical adenocarcinoma/adenosquamous carcinoma.

ABSTRACT:

Objectives

The accurate assessment of lymph node metastasis (LNM) can facilitate clinical decision-making on radiotherapy or radical hysterectomy (RH) in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC). This study aims to develop a deep learning radiomics nomogram (DLRN) to preoperatively evaluate LNM in cervical AC/ASC.

Materials and methods

A total of 652 patients from a multicenter were enrolled and randomly allocated into primary, internal, and external validation cohorts. The radiomics features were extracted from axial T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The DL features from T2WI, DWI, and CE-T1WI were exported from Resnet 34, which was pretrained by 14 million natural images of t

SUBMITTER: Xiao ML 

PROVIDER: S-EPMC11671353 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature