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EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy (2402.14099v2)

Published 21 Feb 2024 in eess.IV, cs.CV, and physics.med-ph

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 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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Authors (7)
  1. Hamed Hooshangnejad (3 papers)
  2. Xue Feng (69 papers)
  3. Gaofeng Huang (10 papers)
  4. Rui Zhang (1138 papers)
  5. Quan Chen (91 papers)
  6. Kai Ding (29 papers)
  7. Katelyn Kelly (1 paper)
Citations (3)
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