EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy

TL;DR

EXACT-Net leverages electronic health records and large language models to improve lung tumor auto-segmentation accuracy in non-small cell lung cancer radiotherapy, significantly reducing false positives and enhancing detection success.

Contribution

This work introduces a novel EHR-guided auto-segmentation method that combines deep learning with LLMs to improve tumor detection in NSCLC patients.

Findings

250% increase in successful nodule detection

Effective reduction of false positives

Utilization of zero-shot learning with LLMs

Abstract

Lung cancer is a devastating disease with the highest mortality rate among cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, which accounts for 87% of diagnoses, require radiation therapy. Rapid treatment initiation significantly increases the patient's survival rate and reduces the mortality rate. Accurate tumor segmentation is a critical step in the diagnosis and treatment of NSCLC. Manual segmentation is time and labor-consuming and causes delays in treatment initiation. Although many lung nodule detection methods, including deep learning-based models, have been proposed, there is still a long-standing problem of high false positives (FPs) with most of these methods. Here, we developed an electronic health record (EHR) guided lung tumor auto-segmentation called EXACT-Net (EHR-enhanced eXACtitude in Tumor segmentation), where the extracted information from EHRs using a pre-trained large language model (LLM), was used to remove the FPs and keep the TP nodules only. The auto-segmentation model was trained on NSCLC patients' computed tomography (CT), and the pre-trained LLM was used with the zero-shot learning approach. Our approach resulted in a 250% boost in successful nodule detection using the data from ten NSCLC patients treated in our institution.