{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T13:10:52Z","timestamp":1780319452578,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Accurate and efficient analysis of mitotic figures in whole-slide images (WSIs) is essential for tumor grading and prognosis. Methods: In this work, we present a three-stage pipeline for WSI-scale mitosis analysis that balances accuracy with clinical throughput: (1) a YOLOv11-based detector to propose mitosis candidates; (2) an ultra-lightweight classifier to refine detections and suppress false positives; and (3) a downstream classifier to distinguish atypical from normal mitoses for deeper biological insight. Results: In benchmark datasets, the two-stage detector improves F1 over detection-only baselines, while the atypical\/normal module achieves strong accuracy, demonstrating cross-domain generalization. We further perform a proof-of-concept survival analysis on early-stage (I\u2013II) cases from the TCGA-BRCA cohort, suggesting that mitosis-derived features may provide modest incremental prognostic information beyond the clinical baseline and nuclei features. Conclusions: Overall, the method delivers accurate detection, robust atypical mitosis classification, and high efficiency, processing gigapixel WSIs in minutes on a single GPU, positioning it for large-scale translational studies and future clinical workflow validation.<\/jats:p>","DOI":"10.3390\/cancers18111807","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:19:42Z","timestamp":1780316382000},"page":"1807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Scalable and Efficient Deep Learning-Based Pipeline for Mitotic Detection and Analysis in Pathology Images"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9599-9066","authenticated-orcid":false,"given":"Xuan","family":"Qi","sequence":"first","affiliation":[{"name":"Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1713-9538","authenticated-orcid":false,"given":"Dominic","family":"LaBella","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Sanford","sequence":"additional","affiliation":[{"name":"University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ismail","family":"Turkbey","sequence":"additional","affiliation":[{"name":"Molecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3738-1292","authenticated-orcid":false,"given":"Maxwell","family":"Lee","sequence":"additional","affiliation":[{"name":"Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1111\/j.1365-2559.1991.tb00229.x","article-title":"Pathological prognostic factors in breast cancer. 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