A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.

ABSTRACT:

Background/purpose

Severe lymphopenia during pelvic radiotherapy (RT) predicts poor survival in patients with cervical cancer. However, the risk of severe lymphopenia has not been well predicted. We developed a machine learning model using clinical and dosimetric information to predict grade 4 (G4) lymphopenia during pelvic RT in patients with cervical cancer.

Methods

This retrospective study included cervical cancer patients treated with definitive pelvic RT ± induction/concurrent chemotherapy. Clinical information and a set of dosimetric parameters of external beam radiotherapy plan were collected. G4 lymphopenia during RT, which was also referred to as G4 absolute lymphocyte count (ALC) nadir, was defined as ALC nadir <0.2 × 10⁹ cells/L during RT according to C

SUBMITTER: Xu Z 

PROVIDER: S-EPMC9524190 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature