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In this study, we analyzed 418 patients with NSCLC undergoing CRT to develop and validate a novel dynamic risk model that accurately predicts ultimate progression-free survival during treatment. We optimize tissue-free variant calling from plasma samples to facilitate ctDNA monitoring and demonstrate the importance of accounting for persistent clonal hematopoiesis variants. We show that mid-CRT ctDNA concentration is prognostic for disease progression and integrate additional pre-CRT risk factors, including radiomics, into a combined model that improves outcome prediction. Our results suggest that tumor features, radiomics, and mid-CRT ctDNA analysis are complementary and can identify patients at high and low risk of progression to potentially enable response-adapted therapies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance:<\/jats:title>\n                    <jats:p>This study demonstrates that combining tumor features, radiomics, and ctDNA analysis improves outcome prediction in NSCLC treated with CRT therapy. Our integrated model could enable personalized and response-adapted therapies to reduce toxicity and improve outcomes in patients.<\/jats:p>\n                    <jats:p>See related commentary by Anagnostou and Aggarwal, p. 1534<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1158\/2159-8290.cd-24-1704","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T13:31:56Z","timestamp":1745933516000},"page":"1609-1629","update-policy":"https:\/\/doi.org\/10.1158\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer"],"prefix":"10.1158","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7913-5351","authenticated-orcid":false,"given":"Everett J.","family":"Moding","sequence":"first","affiliation":[{"name":"1Department of Radiation Oncology, Stanford University, Stanford, California."},{"name":"2Stanford Cancer Institute, 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