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This study assesses the diagnostic performance of a three-dimensional convolutional neural network for the automatic detection of small PDAC using both automatic tumor mass detection and indirect indicator evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>High-resolution contrast-enhanced computed tomography (CT) scans from 181 patients diagnosed with PDAC (diameter\u2009\u2264\u20092\u00a0cm) between January 2018 and December 2023 were analyzed. The D\/P ratio, which is the cross-sectional area of the MPD to that of the pancreatic parenchyma, was identified as an indirect indicator. A total of 204 patient data sets including 104 normal controls were analyzed for automatic tumor mass detection and D\/P ratio evaluation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated to detect tumor mass. The sensitivity of PDAC detection was compared with that of the software and radiologists, and tumor localization accuracy was validated against endoscopic ultrasonography (EUS) findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The sensitivity, specificity, PPV, and NPV for tumor mass detection were 77.0%, 76.0%, 75.5%, and 77.5%, respectively; for D\/P ratio detection, 87.0%, 94.2%, 93.5%, and 88.3%, respectively; and for combined tumor mass and D\/P ratio detections, 96.0%, 70.2%, 75.6%, and 94.8%, respectively. No significant difference was observed between the software\u2019s sensitivity and that of the radiologist\u2019s report (software, 96.0%; radiologist, 96.0%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20091). The concordance rate between software findings and EUS was 96.0%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Combining indirect indicator evaluation with tumor mass detection may improve small PDAC detection accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11604-025-01836-z","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T03:58:14Z","timestamp":1752811094000},"page":"1870-1877","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-based automatic detection of pancreatic ductal adenocarcinoma\u2009\u2264\u20092\u00a0cm with high-resolution computed tomography: impact of the combination of tumor mass detection and indirect indicator evaluation"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6123-6576","authenticated-orcid":false,"given":"Mizuki","family":"Ozawa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miyuki","family":"Sone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susumu","family":"Hijioka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hidenobu","family":"Hara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuke","family":"Wakatsuki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshihiro","family":"Ishihara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chihiro","family":"Hattori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryo","family":"Hirano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shintaro","family":"Ambo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minoru","family":"Esaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiko","family":"Kusumoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshiyuki","family":"Matsui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"1836_CR1","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.1158\/0008-5472.can-14-0155","volume":"74","author":"L Rahib","year":"2014","unstructured":"Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. 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Miyuki Sone: activities related to the present article: institution received a research grant from Canon Medical Systems. Chihiro Hattori, Ryo Hirano and Shintaro Ambo: activities not related to the present article: Employee of Canon Medical Systems. Masahiko Kusumoto: activities related to the present article: institution received a research grant from Canon Medical Systems.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was approved by the institutional review board of the author\u2019s affiliated institution (approval number: NCCH-2023-407).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The requirement for informed consent was waived because of the retrospective nature of the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}