{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:06:12Z","timestamp":1775743572397,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Electronics and Telecommunications Research Institute (ETRI)","award":["21YR2400"],"award-info":[{"award-number":["21YR2400"]}]},{"name":"Electronics and Telecommunications Research Institute (ETRI)","award":["S3146559"],"award-info":[{"award-number":["S3146559"]}]},{"name":"Electronics and Telecommunications Research Institute (ETRI)","award":["KMDF_PR_20200901_0016"],"award-info":[{"award-number":["KMDF_PR_20200901_0016"]}]},{"name":"Electronics and Telecommunications Research Institute (ETRI)","award":["9991006689"],"award-info":[{"award-number":["9991006689"]}]},{"name":"Technology development Program of MSS","award":["21YR2400"],"award-info":[{"award-number":["21YR2400"]}]},{"name":"Technology development Program of MSS","award":["S3146559"],"award-info":[{"award-number":["S3146559"]}]},{"name":"Technology development Program of MSS","award":["KMDF_PR_20200901_0016"],"award-info":[{"award-number":["KMDF_PR_20200901_0016"]}]},{"name":"Technology development Program of MSS","award":["9991006689"],"award-info":[{"award-number":["9991006689"]}]},{"name":"Korea Medical Device Development Fund","award":["21YR2400"],"award-info":[{"award-number":["21YR2400"]}]},{"name":"Korea Medical Device Development Fund","award":["S3146559"],"award-info":[{"award-number":["S3146559"]}]},{"name":"Korea Medical Device Development Fund","award":["KMDF_PR_20200901_0016"],"award-info":[{"award-number":["KMDF_PR_20200901_0016"]}]},{"name":"Korea Medical Device Development Fund","award":["9991006689"],"award-info":[{"award-number":["9991006689"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to high recurrence rates in patients with non-small cell lung cancer (NSCLC), medical professionals need extremely accurate diagnostic methods to prevent bleak prognoses. However, even the most commonly used diagnostic method, the TNM staging system, which describes the tumor-size, nodal-involvement, and presence of metastasis, is often inaccurate in predicting NSCLC recurrence. These limitations make it difficult for clinicians to tailor treatments to individual patients. Here, we propose a novel approach, which applies deep learning to an ensemble-based method that exploits patient-derived, multi-modal data. This will aid clinicians in successfully identifying patients at high risk of recurrence and improve treatment planning.<\/jats:p>","DOI":"10.3390\/s22176594","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T03:55:38Z","timestamp":1662004538000},"page":"6594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8162-6088","authenticated-orcid":false,"given":"Gihyeon","family":"Kim","sequence":"first","affiliation":[{"name":"Computational Medicine, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea"}]},{"given":"Sehwa","family":"Moon","sequence":"additional","affiliation":[{"name":"Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9273-034X","authenticated-orcid":false,"given":"Jang-Hwan","family":"Choi","sequence":"additional","affiliation":[{"name":"Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, Korea"},{"name":"Department of Artificial Intelligence, Ewha Womans University, Seoul 03760, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11044","DOI":"10.1038\/srep11044","article-title":"Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer","volume":"5","author":"Parmar","year":"2015","journal-title":"Sci. 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