{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T09:53:18Z","timestamp":1776419598833,"version":"3.51.2"},"posted":{"date-parts":[[2026,4,14]]},"group-title":"Epidemiology","reference-count":20,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,4,14]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Our group previously reported that lung cancer (LC) screening history results and subsequent timing of diagnosis are associated with significant differences in survival outcomes. As a follow-up study, we sought to develop novel personalized risk models that considered screening history for incidence cancers, interval LCs, and prevalence LCs.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Using data from the CT-arm of the NLST, four independent case-control analyses were conducted to develop parsimonious risk models. Controls (n=26,038) were those never diagnosed with LC. The four LC case groups were 270 prevalence LCs, 44 interval LCs, 206 screen-detected LCs (SDLCs) that had a baseline positive screen, and 164 SDLCs that had a baseline negative screen. For each case-control analysis, univariable analyses identified statistically significant covariates from 48 variables and then significant covariates were included into a stepwise backward selection approach to identify a model with the most informative covariates.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>For prevalence LCs, the model (AUC=0.711) included age, pack-years smoked, BMI, smoking status, smoking onset age, personal history of cancer, family history of LC, alcohol consumption, and milling occupation. For interval LCs, the model (AUC=0.734) included age, smoking status, smoking onset age, cigar smoking, marital status, and asbestos occupation. For baseline positive SDLCs, the model (AUC=0.685) included age, pack-years smoked, BMI, emphysema, chemicals\/plastics exposure, and milling occupation. For baseline negative SDLCs, the model (AUC=0.701) included age, pack-years smoked, BMI, smoking status, emphysema, sarcoidosis, and sandblasting occupation.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Besides smoking and age, which are inclusion criteria for screening, these models identified other important risk factors which could be used to provide personalized LC risk assessment and screening management.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.04.12.26350705","type":"posted-content","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:55:14Z","timestamp":1776182114000},"source":"Crossref","is-referenced-by-count":0,"title":["Novel risk models based on screening history results and timing of lung cancer diagnosis:\n                  <i>Post hoc<\/i>\n                  analysis of the National Lung Cancer Screening Trial"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9237-7269","authenticated-orcid":false,"given":"Sofia A.","family":"Haddan","sequence":"first","affiliation":[{"name":"Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6834-4710","authenticated-orcid":false,"given":"Asim","family":"Waqas","sequence":"additional","affiliation":[{"name":"Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-0090","authenticated-orcid":false,"given":"Ghulam","family":"Rasool","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3241-3216","authenticated-orcid":false,"given":"Matthew B.","family":"Schabath","sequence":"additional","affiliation":[{"name":"Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026041702000976000_2026.04.12.26350705v1.1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1102873"},{"key":"2026041702000976000_2026.04.12.26350705v1.2","doi-asserted-by":"publisher","DOI":"10.6004\/jnccn.2022.0036"},{"key":"2026041702000976000_2026.04.12.26350705v1.3","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21811"},{"key":"2026041702000976000_2026.04.12.26350705v1.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtcvs.2012.05.060"},{"key":"2026041702000976000_2026.04.12.26350705v1.5","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1211776"},{"key":"2026041702000976000_2026.04.12.26350705v1.6","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2020-215158"},{"issue":"8","key":"2026041702000976000_2026.04.12.26350705v1.7","doi-asserted-by":"crossref","first-page":"e0159880","DOI":"10.1371\/journal.pone.0159880","article-title":"Differences in Patient Outcomes of Prevalence, Interval, and Screen-Detected Lung Cancers in the CT Arm of the National Lung Screening Trial","volume":"11","year":"2016","journal-title":"PLoS One"},{"key":"2026041702000976000_2026.04.12.26350705v1.8","unstructured":"National Cancer Institute (NCI) Cancer Data Access System (CDAS). 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