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

• URL: http://arxiv.org/abs/2402.14099v2
• Date: Wed, 31 Jul 2024 21:57:33 GMT
• Title: EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy
• Authors: Hamed Hooshangnejad, Xue Feng, Gaofeng Huang, Rui Zhang, Katelyn Kelly, Quan Chen, Kai Ding,
• Abstract summary: Over 60% of non-small cell lung cancer (NSCLC) patients require radiation therapy. Our approach resulted in a 250% boost in successful nodule detection using the data from ten NSCLC patients treated in our institution.
• Score: 7.531407604292937
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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