{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T08:14:56Z","timestamp":1793261696373,"version":"4.5.0"},"reference-count":71,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2021R1A2C2013630"],"award-info":[{"award-number":["2021R1A2C2013630"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>State-of-the-art artificial intelligence (AI) has recently gained considerable interest in the healthcare sector and has provided solutions to problems through automated diagnosis. Cytological examination is a crucial step in the initial diagnosis of cancer, although it shows limited diagnostic efficacy. Recently, AI applications in the processing of cytopathological images have shown promising results despite the elementary level of the technology. Here, we performed a systematic review with a quantitative analysis of recent AI applications in non-gynecological (non-GYN) cancer cytology to understand the current technical status. We searched the major online databases, including MEDLINE, Cochrane Library, and EMBASE, for relevant English articles published from January 2010 to January 2021. The searched query terms were: \u201cartificial intelligence\u201d, \u201cimage processing\u201d, \u201cdeep learning\u201d, \u201ccytopathology\u201d, and \u201cfine-needle aspiration cytology.\u201d Out of 17,000 studies, only 26 studies (26 models) were included in the full-text review, whereas 13 studies were included for quantitative analysis. There were eight classes of AI models treated of according to target organs: thyroid (n = 11, 39%), urinary bladder (n = 6, 21%), lung (n = 4, 14%), breast (n = 2, 7%), pleural effusion (n = 2, 7%), ovary (n = 1, 4%), pancreas (n = 1, 4%), and prostate (n = 1, 4). Most of the studies focused on classification and segmentation tasks. Although most of the studies showed impressive results, the sizes of the training and validation datasets were limited. Overall, AI is also promising for non-GYN cancer cytopathology analysis, such as pathology or gynecological cytology. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation found across all studies. Future studies with larger datasets with high-quality annotations and external validation are required.<\/jats:p>","DOI":"10.3390\/cancers14143529","type":"journal-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T11:22:24Z","timestamp":1658316144000},"page":"3529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8761-0837","authenticated-orcid":false,"given":"Nishant","family":"Thakur","sequence":"first","affiliation":[{"name":"Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Rizwan","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6575-8870","authenticated-orcid":false,"given":"Jamshid","family":"Abdul-Ghafar","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8615-3064","authenticated-orcid":false,"given":"Yosep","family":"Chong","sequence":"additional","affiliation":[{"name":"Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s13000-021-01081-8","article-title":"Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation","volume":"16","author":"Chong","year":"2021","journal-title":"Diagn. 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