{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T03:14:17Z","timestamp":1782184457367,"version":"3.54.5"},"reference-count":9,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The similarity of gallbladder cancer and benign gallbladder lesions brings challenges to diagnosing gallbladder cancer (GBC). This study investigated whether a convolutional neural network (CNN) could adequately differentiate GBC from benign gallbladder diseases, and whether information from adjacent liver parenchyma could improve its performance. Methods: Consecutive patients referred to our hospital with suspicious gallbladder lesions with histopathological diagnosis confirmation and available contrast-enhanced portal venous phase CT scans were retrospectively selected. A CT-based CNN was trained once on gallbladder only and once on gallbladder including a 2 cm adjacent liver parenchyma. The best-performing classifier was combined with the diagnostic results based on radiological visual analysis. Results: A total of 127 patients were included in the study: 83 patients with benign gallbladder lesions and 44 with gallbladder cancer. The CNN trained on the gallbladder including adjacent liver parenchyma achieved the best performance with an AUC of 0.81 (95% CI 0.71\u20130.92), being &gt;10% better than the CNN trained on only the gallbladder (p = 0.09). Combining the CNN with radiological visual interpretation did not improve the differentiation between GBC and benign gallbladder diseases. Conclusions: The CT-based CNN shows promising ability to differentiate gallbladder cancer from benign gallbladder lesions. In addition, the liver parenchyma adjacent to the gallbladder seems to provide additional information, thereby improving the CNN\u2019s performance for gallbladder lesion characterization. However, these findings should be confirmed in larger multicenter studies.<\/jats:p>","DOI":"10.3390\/diagnostics13040704","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T05:25:26Z","timestamp":1676265926000},"page":"704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["The Value of Deep Learning in Gallbladder Lesion Characterization"],"prefix":"10.3390","volume":"13","author":[{"given":"Yunchao","family":"Yin","sequence":"first","affiliation":[{"name":"Department of Radiology, Medical Imaging Center Groningen, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4920-9441","authenticated-orcid":false,"given":"Derya","family":"Yakar","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Imaging Center Groningen, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jules J. G.","family":"Slangen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Imaging Center Groningen, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederik J. H.","family":"Hoogwater","sequence":"additional","affiliation":[{"name":"Department of Surgery, Section Hepato-Pancreato-Biliary Surgery and Liver Transplantation, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas C.","family":"Kwee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Imaging Center Groningen, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4915-6781","authenticated-orcid":false,"given":"Robbert J.","family":"de Haas","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Imaging Center Groningen, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3322\/canjclin.51.6.349","article-title":"Epidemiology and molecular pathology of gallbladder cancer","volume":"51","author":"Miquel","year":"2001","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21387","article-title":"Cancer Statistics, 2017","volume":"67","author":"Siegel","year":"2017","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1532835","DOI":"10.1155\/2017\/1532835","article-title":"Gallbladder Carcinoma in the United States: A Population Based Clinical Outcomes Study Involving 22,343 Patients from the Surveillance, Epidemiology, and End Result Database (1973\u20132013)","volume":"2017","author":"Lau","year":"2017","journal-title":"HPB Surg."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"de Savornin Lohman, E., de Bitter, T., Verhoeven, R., van der Geest, L., Hagendoorn, J., Haj Mohammad, N., Daams, F., Kl\u00fcmpen, H.-J., van Gulik, T., and Erdmann, J. (2020). Trends in Treatment and Survival of Gallbladder Cancer in the Netherlands; Identifying Gaps and Opportunities from a Nation-Wide Cohort. 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Res."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/4\/704\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:33:15Z","timestamp":1760121195000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/4\/704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,13]]},"references-count":9,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["diagnostics13040704"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13040704","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,13]]}}}