{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T22:11:43Z","timestamp":1793484703454,"version":"4.5.0"},"reference-count":60,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Cancer Institute of the National Institutes of Health","award":["R37CA229417"],"award-info":[{"award-number":["R37CA229417"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Lung cancer is a devastating disease with the highest mortality rate among cancer types. Over 60% of non-small cell lung cancer (NSCLC) patients, accounting for 87% of lung cancer diagnoses, require radiation therapy. Rapid treatment initiation significantly increases the patient\u2019s survival rate and reduces the mortality rate. Accurate tumor segmentation is a critical step in diagnosing and treating 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. Most of these methods still have a long-standing problem of high false positives (FPs). 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. Results: The auto-segmentation model was trained on NSCLC patients\u2019 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. Conclusions: We demonstrated that combining vision-language information in EXACT-Net multi-modal AI framework greatly enhances the performance of vision only models, paving the road to multimodal AI framework for medical image processing.<\/jats:p>","DOI":"10.3390\/cancers16234097","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T06:18:03Z","timestamp":1733725083000},"page":"4097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["EXACT-Net: Framework for EHR-Guided Lung Tumor Auto-Segmentation for Non-Small Cell Lung Cancer Radiotherapy"],"prefix":"10.3390","volume":"16","author":[{"given":"Hamed","family":"Hooshangnejad","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA"},{"name":"Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaofeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Carina Medical, Lexington, KY 40509, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katelyn","family":"Kelly","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2181-9889","authenticated-orcid":false,"given":"Xue","family":"Feng","sequence":"additional","affiliation":[{"name":"Carina Medical, Lexington, KY 40509, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5728-6869","authenticated-orcid":false,"given":"Ziyue","family":"Xu","sequence":"additional","affiliation":[{"name":"NVIDIA Corp., AI Infrastructure, Santa Clara, CA 95050, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8202-0173","authenticated-orcid":false,"given":"Kai","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3322\/caac.21763","article-title":"Cancer Statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J. 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