{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:06:32Z","timestamp":1790633192568,"version":"4.1.0"},"reference-count":64,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH","award":["R01CA262147"],"award-info":[{"award-number":["R01CA262147"]}]},{"name":"NIH","award":["CMMI 2224708"],"award-info":[{"award-number":["CMMI 2224708"]}]},{"name":"NIH","award":["UMO-2021\/41\/B\/ST5\/03032"],"award-info":[{"award-number":["UMO-2021\/41\/B\/ST5\/03032"]}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["R01CA262147"],"award-info":[{"award-number":["R01CA262147"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CMMI 2224708"],"award-info":[{"award-number":["CMMI 2224708"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["UMO-2021\/41\/B\/ST5\/03032"],"award-info":[{"award-number":["UMO-2021\/41\/B\/ST5\/03032"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Massachusetts Life Sciences Center (MLSC)","award":["R01CA262147"],"award-info":[{"award-number":["R01CA262147"]}]},{"name":"Massachusetts Life Sciences Center (MLSC)","award":["CMMI 2224708"],"award-info":[{"award-number":["CMMI 2224708"]}]},{"name":"Massachusetts Life Sciences Center (MLSC)","award":["UMO-2021\/41\/B\/ST5\/03032"],"award-info":[{"award-number":["UMO-2021\/41\/B\/ST5\/03032"]}]},{"name":"NCN","award":["R01CA262147"],"award-info":[{"award-number":["R01CA262147"]}]},{"name":"NCN","award":["CMMI 2224708"],"award-info":[{"award-number":["CMMI 2224708"]}]},{"name":"NCN","award":["UMO-2021\/41\/B\/ST5\/03032"],"award-info":[{"award-number":["UMO-2021\/41\/B\/ST5\/03032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cells"],"abstract":"<jats:p>The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique\u2019s capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (p &lt; 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection.<\/jats:p>","DOI":"10.3390\/cells14010014","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T19:33:07Z","timestamp":1735241587000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Mikhail","family":"Petrov","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadezhda","family":"Makarova","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Monemian","sequence":"additional","affiliation":[{"name":"Cellens, Inc., 529 Main Street, Suite 1M6, Boston, MA 02129, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean","family":"Pham","sequence":"additional","affiliation":[{"name":"Cellens, Inc., 529 Main Street, Suite 1M6, Boston, MA 02129, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0844-8662","authenticated-orcid":false,"given":"Ma\u0142gorzata","family":"Lekka","sequence":"additional","affiliation":[{"name":"Department of Biophysical Microstructures, Institute of Nuclear Physics PAN, PL-31342 Krak\u00f3w, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6260-4326","authenticated-orcid":false,"given":"Igor","family":"Sokolov","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Tufts University, Medford, MA 02155, USA"},{"name":"Departments of Biomedical Engineering and Physics, Tufts University, Medford, MA 02155, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1126\/science.aar3247","article-title":"Detection and localization of surgically resectable cancers with a multi-analyte blood test","volume":"359","author":"Cohen","year":"2018","journal-title":"Science"},{"key":"ref_2","unstructured":"(2024, December 12). 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