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

Abstract

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 recommendations were consistent. Better brain cancer‐specific survival (BCSS) outcomes in the Consis. group was observed. Overall, we also identified two subgroups that showed strong heterogeneity in response to GTR. By interpreting the models, we identified numerous variables that may be related to treatment heterogeneity. Conclusions This is the first study to infer the individual treatment effect, make treatment recommendation, and guide surgical options through deep learning approach in LGG research. Through causal inference, we found that heterogeneous responses to STR and GTR exist in patients with LGG. Visualization of the model yielded several factors that contribute to treatment heterogeneity, which are worthy of further discussion.

Keywords

• causal inference
• deep learning
• gross‐total resection
• low‐grade gliomas
• sub‐total resection