EXACT-Net：面向非小细胞肺癌放疗的EHR引导肺肿瘤自动分割
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy
AI总结：
本文提出EXACT-Net，利用预训练大语言模型从电子健康记录中提取信息以去除肺结节检测中的假阳性，在十名NSCLC患者数据上使成功结节检测率提升250%。
摘要：
肺癌是一种极具破坏性的疾病，在各类癌症中死亡率最高。超过60%的非小细胞肺癌（NSCLC）患者需要接受放射治疗，而NSCLC占全部肺癌诊断的87%。快速启动治疗可显著提高患者生存率并降低死亡率。准确的肿瘤分割是NSCLC诊断和治疗中的关键步骤。人工分割耗时费力，并会导致治疗启动延迟。尽管已提出许多肺结节检测方法，包括基于深度学习的模型，但大多数方法长期存在假阳性（FPs）过高的问题。在此，我们开发了一种电子健康记录（EHR）引导的肺肿瘤自动分割方法，称为EXACT-Net（EHR-enhanced eXACtitude in Tumor segmentation，即EHR增强的肿瘤分割精确性），其中利用预训练大语言模型（LLM）从EHR中提取的信息被用于去除假阳性，仅保留真阳性（TP）结节。该自动分割模型在NSCLC患者的计算机断层扫描（CT）上训练，预训练LLM采用零样本学习方法使用。我们的方法在使用本院十名NSCLC患者的数据时，使成功结节检测率提升了250%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
