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

arXiv: https://arxiv.org/abs/2402.14099 · HTML: https://mlsanity.com/paper/2402.14099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://mlsanity.com/paper/2303.11032/) (2023)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features](https://mlsanity.com/paper/1812.01049/) (2018)
- [RadOnc-GPT: A Large Language Model for Radiation Oncology](https://mlsanity.com/paper/2309.10160/) (2023)
- [Automatic Pulmonary Lobe Segmentation Using Deep Learning](https://mlsanity.com/paper/1903.09879/) (2019)
- [Semi-supervised multi-task learning for lung cancer diagnosis](https://mlsanity.com/paper/1802.06181/) (2018)
- [Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet](https://mlsanity.com/paper/1812.01951/) (2018)
- [Lung Nodules Detection and Segmentation Using 3D Mask-RCNN](https://mlsanity.com/paper/1907.07676/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology](https://mlsanity.com/paper/2503.14933/) (2025)

## Abstract (arXiv)

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.