[¹⁸F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation.

ABSTRACT:

Purpose

To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[¹⁸F] fluoro-2-deoxy-D-glucose ([¹⁸F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC).

Methods

One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of f

SUBMITTER: Ferreira M 

PROVIDER: S-EPMC8440288 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature