{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:25:49Z","timestamp":1787063149723,"version":"build-2736575974"},"posted":{"date-parts":[[2026,8,15]]},"group-title":"Cancer Biology","reference-count":34,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T00:00:00Z","timestamp":1786752000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"id":[{"id":"https:\/\/ror.org\/00nx7kq05","id-type":"ROR","asserted-by":"publisher"}]},{"name":"OHSU Foundation"},{"name":"National Institute of Health","award":["P30CA069533"],"award-info":[{"award-number":["P30CA069533"]}]},{"name":"National Institute of Health","award":["R43\/44CA250861"],"award-info":[{"award-number":["R43\/44CA250861"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Significance<\/jats:title>\n                  <jats:p>Rare-cell identification in fluorescence microscopy remains challenging because targets are sparse and background varies between specimens. Combining specimen-specific fluorescence enrichment with image classification may enable efficient and more specific automated detection of rare cells.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Aim<\/jats:title>\n                  <jats:p>We developed a two-stage framework to identify and quantify candidate rare circulating hybrid neoplastic cells (CHCs, ECAD+\/CD45+) in peripheral blood mononuclear cell (PBMC) preparations from tumor-bearing and tumor-na\u00efve mice.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Approach<\/jats:title>\n                  <jats:p>PBMCs from 28 mice were imaged by multichannel fluorescence microscopy. Matched unstained samples established animal-specific ECAD and CD45 background distributions for candidate cell enrichment. Blinded multi-annotator consensus labels were used to train a convolutional neural network (CNN) from DAPI, ECAD, and CD45 image crops. Generalization was evaluated by leave-one-animal-out validation across 10 random initializations. Final classification used a 10-model ensemble, and rare-cell burden was compared between groups using negative-binomial regression with total segmented-cell count as an exposure.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Of the 1,065,512 segmented cells, enrichment retained 10,176 candidates (0.96%), reducing the search space by &gt;99%. Four of five evaluable tumor-bearing animals showed reproducible held-out discrimination, with median quantified area under the receiver operator characteristic curve (AUROCs) of 0.918\u20130.951; one animal was a reproducible outlier (median AUROC, 0.338). Ensemble deployment identified 157.94 positive-consensus cells per 50,000 segmented cells in tumor-bearing animals versus 49.55 in controls. The estimated rare-cell rate was 3.15-fold higher in tumor-bearing animals (95% CI, 0.91\u201310.99; two-sided p=0.071; prespecified one-sided p=0.036).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Specimen-specific fluorescence enrichment combined with supervised image classification reduced the cellular search space and enabled automated quantification of a rare CHC (ECAD+\/CD45+) phenotypes. Cross-animal validation also identified specimen-specific generalization failure, highlighting the importance of biological-specimen-level validation.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.08.14.744773","type":"posted-content","created":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T09:10:17Z","timestamp":1786785017000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep learning-based identification and quantification of rare circulating hybrid cells in orthotopic pancreatic cancer models"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2586-2731","authenticated-orcid":false,"given":"Cody C.","family":"Rounds","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Oregon Health & Science University (OHSU), Portland OR, USA 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