{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T05:56:00Z","timestamp":1770098160146,"version":"3.49.0"},"posted":{"date-parts":[[2026,1,30]]},"group-title":"Oncology","reference-count":14,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"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":[[2026,1,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Bladder cancer imposes substantial clinical and economic burden, yet key natural-history quantities that determine the potential effectiveness of screening\u2014such as the size of the screen-detectable preclinical reservoir and the preclinical sojourn time\u2014are largely unobservable. The Cancer Intervention and Surveillance Modeling Network (CISNET) uses standardized stress tests to compare independently developed microsimulation models and to clarify how differences in model structure translate into differences in intervention impact. The Maximum Clinical Incidence Reduction (MCLIR) framework estimates the maximum achievable reduction in clinically detected incidence following a one-time, perfect screening intervention, while Realistic Clinical Incidence Reduction (RCLIR) relaxes the perfect-test and\/or perfect-treatment assumptions.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We applied the CISNET MCLIR\/RCLIR protocol to three independently developed bladder cancer microsimulation models (COBRAS, Kystis, and SCOUT), each calibrated to common U.S. epidemiologic targets. We simulated a U.S. birth cohort born in 1950 and compared: (1) no-screening baseline; (2) one-time perfect screening with universally curative treatment (MCLIR) at ages 60, 65, 70, and 75; and (3) one-time realistic screening with cystoscopy sensitivity of 80% and perfect treatment (RCLIR-1) or usual-care treatment effectiveness (RCLIR-2). Outcomes included age-specific clinical incidence and incidence-reduction curves relative to baseline, as well as cumulative reductions over follow-up.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Across models, median ages at first lesion emergence, clinical diagnosis, and onset of muscle-invasive disease were similar, but preclinical sojourn time differed meaningfully: COBRAS produced the shortest median sojourn time (2.1 years) compared with Kystis (3.3 years) and SCOUT (3.1 years). Under MCLIR, all models predicted an immediate drop in clinical incidence followed by attenuation toward zero as new lesions emerged after the screening age. Peak MCLIR at age 65 among White men ranged from 20% (COBRAS) to 21% (Kystis) and 32% (SCOUT), with reductions dissipating within \u223c8 years in COBRAS and persisting \u223c10 years in Kystis and SCOUT. In COBRAS and Kystis, incidence reductions later became negative, consistent with rebound emergence of new lesions among individuals whose first lesions were removed by screening. Under RCLIR-1, peak reductions were smaller and varied substantially across models (13%, 4%, and 37% for COBRAS, Kystis, and SCOUT, respectively). RCLIR-2 further reduced gains and produced more gradual decay, reflecting incomplete prevention under usual-care treatment. Across models, most residual post-screening incidence was attributable to new lesion emergence rather than missed detection or incomplete treatment.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>In a standardized CISNET stress test, three bladder cancer microsimulation models imply a relatively short detectable preclinical phase, placing a modest upper bound on the effectiveness of one-time screening in the general population. Differences in MCLIR\/RCLIR magnitude and persistence are explained by differences in implied sojourn time and detectable reservoir size. These findings motivate evaluation of risk-targeted and repeated early-detection strategies and highlight key empirical priorities for improving inference on bladder cancer natural history.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.01.27.26344939","type":"posted-content","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T22:41:30Z","timestamp":1769812890000},"source":"Crossref","is-referenced-by-count":0,"title":["Understanding Bladder Cancer Screening Limits Through Comparative Modeling: The Maximum Clinical Incidence Reduction (MCLIR) Methodology"],"prefix":"10.64898","author":[{"given":"Hawre","family":"Jalal","sequence":"first","affiliation":[{"name":"School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stella K.","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando","family":"Alarid-Escudero","sequence":"additional","affiliation":[{"name":"Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA"},{"name":"Center for Health Policy, Freeman Spogli Institute, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stavroula A.","family":"Chrysanthopoulou","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Center for Biostatistics and Health Data Science, School of Public Health, Brown University, Providence, RI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0459-1579","authenticated-orcid":false,"given":"David Ulises","family":"Garibay-Trevino","sequence":"additional","affiliation":[{"name":"School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bruce L.","family":"Jacobs","sequence":"additional","affiliation":[{"name":"Division of Health Services Research, Department of Urology, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen M.","family":"Kuntz","sequence":"additional","affiliation":[{"name":"Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praveen","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA"},{"name":"Public Health Dynamics Laboratory, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonah H.","family":"Popp","sequence":"additional","affiliation":[{"name":"Department of Health Services, Policy, and Practice and Center for Evidence Synthesis in Health, School of Public Health, Brown University, Providence, RI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4017-4561","authenticated-orcid":false,"given":"Yuliia","family":"Sereda","sequence":"additional","affiliation":[{"name":"Department of Health Services, Policy, and Practice and Center for Evidence Synthesis in Health, School of Public Health, Brown University, Providence, RI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mutita","family":"Siriruchatanon","sequence":"additional","affiliation":[{"name":"Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John B.","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Clinical Decision Making, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3990-1848","authenticated-orcid":false,"given":"Thomas A.","family":"Trikalinos","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Center for Biostatistics and Health Data Science, School of Public Health, Brown University, Providence, RI, USA"},{"name":"Department of Health Services, Policy, and Practice and Center for Evidence Synthesis in Health, School of Public Health, Brown University, Providence, RI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"CISNET Bladder Cancer Modeling Investigators","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026020210301630000_2026.01.27.26344939v1.1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21820"},{"key":"2026020210301630000_2026.01.27.26344939v1.2","unstructured":"National Cancer Institute, Surveillance, Epidemiology and End Results Program: Cancer Stat Facts: Common Cancer Sites. 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