{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T10:37:22Z","timestamp":1789382242553,"version":"build-2803163510"},"reference-count":25,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100008529","name":"Chiba University","doi-asserted-by":"publisher","award":["Strategic Priority Research Promotion Program \"Mul"],"award-info":[{"award-number":["Strategic Priority Research Promotion Program \"Mul"]}],"id":[{"id":"10.13039\/501100008529","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["Grants-in-aid for Scientific Research \/ 19H01172"],"award-info":[{"award-number":["Grants-in-aid for Scientific Research \/ 19H01172"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Int J Surg Pathol"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>\n                    <jats:italic toggle=\"yes\">Objectives.<\/jats:italic>\n                    The diversifying modalities of treatment for gastric cancer raise urgent demands for the rapid and precise diagnosis of metastases in regional lymph nodes, thereby significantly impact the workload of pathologists. Meanwhile, the recent advent of whole-slide scanners and deep-learning techniques have enabled the computer-assisted analysis of histopathological images, which could help to alleviate this impact. Thus, we developed a deep learning-based diagnostic algorithm to detect lymph node metastases of gastric adenocarcinoma and evaluated its performance.\n                    <jats:italic toggle=\"yes\">Methods.<\/jats:italic>\n                    We randomly selected 20 patients with gastric adenocarcinoma who underwent surgery as definitive treatment and were found to be node metastasis-positive. HEMATOXYLIN-eosin (HE) stained glass slides, including a total of 51 metastasis-positive nodes, were retrieved from the specimens of these cases. Other slides with 776 metastasis-negative nodes were also retrieved from other twenty cases with the same disease that were diagnosed as metastasis-negative by the final pathological examinations. All glass slides were digitized using a whole-slide scanner. A deep-learning algorithm to detect metastases was developed using the data in which metastasis-positive parts of the images were annotated by a well-trained pathologist, and its performance in detecting metastases was evaluated.\n                    <jats:italic toggle=\"yes\">Results.<\/jats:italic>\n                    Cross-validation analysis indicated an area of 0.9994 under the receiver operating characteristic curve. Free-response receiver operating characteristic curve (FROC) analysis indicated a sensitivity of 1.00 with three false positives. Further evaluation using an independent dataset also showed similar level of accuracies.\n                    <jats:italic toggle=\"yes\">Conclusion.<\/jats:italic>\n                    This deep learning-based diagnosis-aid system is a promising tool that can assist pathologists involved in gastric cancer care and reduce their workload.\n                  <\/jats:p>","DOI":"10.1177\/10668969221113475","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T01:57:04Z","timestamp":1658973424000},"page":"975-981","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["The Use of Deep Learning-Based Computer Diagnostic Algorithm for Detection of Lymph Node Metastases of Gastric Adenocarcinoma"],"prefix":"10.1177","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7089-3962","authenticated-orcid":false,"given":"Jun","family":"Matsushima","sequence":"first","affiliation":[{"name":"Dokkyo Medical University"},{"name":"Chiba University"},{"name":"Chiba University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamotsu","family":"Sato","sequence":"additional","affiliation":[{"name":"Toshiba Digital Solutions Corporation, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Ohnishi","sequence":"additional","affiliation":[{"name":"Chiba University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichiro","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"Chiba University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Mizutani","sequence":"additional","affiliation":[{"name":"Toshiba Digital Solutions Corporation, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinichiro","family":"Koto","sequence":"additional","affiliation":[{"name":"Toshiba Digital Solutions Corporation, Kanagawa, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-ichiro","family":"Ikeda","sequence":"additional","affiliation":[{"name":"Chiba University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masayuki","family":"Kano","sequence":"additional","affiliation":[{"name":"Chiba University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hisahiro","family":"Matsubara","sequence":"additional","affiliation":[{"name":"Chiba University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5122-6008","authenticated-orcid":false,"given":"Hideki","family":"Hayashi","sequence":"additional","affiliation":[{"name":"Chiba University"},{"name":"Chiba University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-016-0622-4"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1001\/archsurg.1992.01420040034005"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1097\/00000658-199409000-00015"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0039-6109(05)70262-0"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/s1072-7515(02)01594-6"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/ags3.12027"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.suronc.2016.05.004"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-018-0793-2"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25842"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.14585"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1097\/PAS.0000000000001151"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-58467-9"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101549"},{"key":"e_1_3_3_17_2","article-title":"Deep residual learning for image recognition","author":"He K","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J. 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