Clinical Translation of a Deep Learning Model of Radiation-Induced Lymphopenia for Esophageal Cancer.

ABSTRACT:

Purpose

Radiation-induced lymphopenia is a common immune toxicity that adversely impacts treatment outcomes. We report here our approach to translate a deep-learning (DL) model developed to predict severe lymphopenia risk among esophageal cancer into a strategy for incorporating the immune system as an organ-at-risk (iOAR) to mitigate the risk.

Materials and methods

We conducted "virtual clinical trials" utilizing retrospective data for 10 intensity-modulated radiation therapy (IMRT) and 10 passively-scattered proton therapy (PSPT) esophageal cancer patients. For each patient, additional treatment plans of the modality other than the original were created employing standard-of-care (SOC) dose constraints. Predicted values of absolute lymphocyte count (ALC) nadir for all plan

SUBMITTER: Hu Z 

PROVIDER: S-EPMC11369390 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature