Reasoning and causal inference regarding surgical options for patients with low-grade gliomas using machine learning: A SEER-based study.

ABSTRACT:

Background

Due to the heterogeneity of low-grade gliomas (LGGs), the lack of randomized control trials, and strong clinical evidence, the effect of the extent of resection (EOR) is currently controversial.

Aim

To determine the best choice between subtotal resection (STR) and gross-total resection (GTR) for individual patients and to identify features that are potentially relevant to treatment heterogeneity.

Methods

Patients were enrolled from the SEER database. We used a novel DL approach to make treatment recommendations for patients with LGG. We also made causal inference of the average treatment effect (ATE) of GTR compared with STR.

Results

The patients were divided into the Consis. and In-consis. groups based on whether their actual treatment and model rec

SUBMITTER: Zhu E 

PROVIDER: S-EPMC10709720 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature