{"id":{"repo_id":"radboud","oai_identifier":"oai:repository.ubn.ru.nl:2066/326579"},"canonical_url":"https://search.dev.ndltd.org/etd/radboud/oai:repository.ubn.ru.nl:2066/326579","repository":{"repo_id":"radboud","name":"Radboud University Nijmegen","base_url":"https://repository.ubn.ru.nl/oai/request"},"display":{"title":"Artificial intelligence for pancreatic cancer guided by clinical need","abstract":"Contains fulltext : 326579.pdf (Publisher’s version ) (Open Access)","abstract_html":"Contains fulltext : 326579.pdf (Publisher’s version ) (Open Access)","abstract_has_math":false,"creators":["Schuurmans, M.S."],"institution":"S.l. : s.n.","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Litjens, G.J.S.","Huisman, H.J.","Hermans, J.J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T04:01:29Z","subjects":["Pathology - Radboud University Medical Center"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2066/326579","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Litjens, G.J.S.","Huisman, H.J.","Hermans, J.J."]},{"key":"dc:creator","label":"Author","values":["Schuurmans, M.S."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025"]},{"key":"dc:publisher","label":"Institution","values":["S.l. : s.n."]},{"key":"dc:type","label":"Dc Type","values":["Doctoral thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pathology - Radboud University Medical Center"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.ubn.ru.nl//bitstream/handle/2066/326579/326579.pdf","https://hdl.handle.net/2066/326579"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Contains fulltext : 326579.pdf (Publisher’s version ) (Open Access)","Pancreatic cancer is one of the deadliest forms of cancer: only about 5% of patients survive. An early and accurate diagnosis is therefore crucial, but this remains a major challenge. Radiologists often miss tumors on scans, and current prognostic estimates frequently do not match reality. In this Ph.D. thesis, we demonstrate how artificial intelligence (AI) can help address these issues. A large international study showed that AI performs on par with dozens of experienced radiologists in detecting tumors. Moreover, AI not only outperforms the average radiologist but, importantly, enables radiologists using AI support to diagnose more consistently and safely. Finally, we developed a multimodal AI model that classifies patients based on their actual survival outcomes, allowing for more personalized treatment decisions.","Radboud University, 12 december 2025","Promotores : Litjens, G.J.S., Huisman, H.J. Co-promotor : Hermans, J.J.","207 p."]},{"key":"dc:title","label":"Title","values":["Artificial intelligence for pancreatic cancer guided by clinical need"]}]}],"canonical_facts":{"dc:contributor":["Litjens, G.J.S.","Huisman, H.J.","Hermans, J.J."],"dc:creator":["Schuurmans, M.S."],"dc:date":["2025"],"dc:description":["Contains fulltext : 326579.pdf (Publisher’s version ) (Open Access)","Pancreatic cancer is one of the deadliest forms of cancer: only about 5% of patients survive. An early and accurate diagnosis is therefore crucial, but this remains a major challenge. Radiologists often miss tumors on scans, and current prognostic estimates frequently do not match reality. In this Ph.D. thesis, we demonstrate how artificial intelligence (AI) can help address these issues. A large international study showed that AI performs on par with dozens of experienced radiologists in detecting tumors. Moreover, AI not only outperforms the average radiologist but, importantly, enables radiologists using AI support to diagnose more consistently and safely. Finally, we developed a multimodal AI model that classifies patients based on their actual survival outcomes, allowing for more personalized treatment decisions.","Radboud University, 12 december 2025","Promotores : Litjens, G.J.S., Huisman, H.J. Co-promotor : Hermans, J.J.","207 p."],"dc:identifier":["https://repository.ubn.ru.nl//bitstream/handle/2066/326579/326579.pdf","https://hdl.handle.net/2066/326579"],"dc:publisher":["S.l. : s.n."],"dc:subject":["Pathology - Radboud University Medical Center"],"dc:title":["Artificial intelligence for pancreatic cancer guided by clinical need"],"dc:type":["Doctoral thesis"]},"updated_at":"2026-07-24T04:01:29Z"}