{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:22:30Z","timestamp":1773346950671,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684567","type":"print"},{"value":"9781643684574","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>High-resolution whole slide image scans of histopathology slides have been widely used in recent years for prediction in cancer. However, in some cases, clinical informatics practitioners may only have access to low-resolution snapshots of histopathology slides, not high-resolution scans. We evaluated strategies for training neural network prognostic models in non-small cell lung cancer (NSCLC) based on low-resolution snapshots, using data from the Veterans Affairs Precision Oncology Data Repository. We compared strategies without transfer learning, with transfer learning from general domain images, and with transfer learning from publicly available high-resolution histopathology scans. We found transfer learning from high-resolution scans achieved significantly better performance than other strategies. Our contribution provides a foundation for future development of prognostic models in NSCLC that incorporate data from low-resolution pathology slide snapshots alongside known clinical predictors.<\/jats:p>","DOI":"10.3233\/shti231062","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:01Z","timestamp":1706178301000},"source":"Crossref","is-referenced-by-count":2,"title":["Transfer Learning for Mortality Prediction in Non-Small Cell Lung Cancer with Low-Resolution Histopathology Slide Snapshots"],"prefix":"10.3233","author":[{"given":"Matthew","family":"Clark","sequence":"first","affiliation":[{"name":"Center for Translational Data Science, University of Chicago, Chicago, IL"}]},{"given":"Christopher","family":"Meyer","sequence":"additional","affiliation":[{"name":"Center for Translational Data Science, University of Chicago, Chicago, IL"}]},{"given":"Jaime","family":"Ramos-Cejudo","sequence":"additional","affiliation":[{"name":"VA Boston Healthcare System, Boston, MA"},{"name":"New York University Grossman School of Medicine, New York, NY"}]},{"given":"Danne C.","family":"Elbers","sequence":"additional","affiliation":[{"name":"VA Boston Healthcare System, Boston, MA"},{"name":"Harvard Medical School, Boston, MA"}]},{"given":"Karen","family":"Pierce-Murray","sequence":"additional","affiliation":[{"name":"VA Boston Healthcare System, Boston, MA"}]},{"given":"Rafael","family":"Fricks","sequence":"additional","affiliation":[{"name":"National Artificial Intelligence Institute, Dept. of Veterans Affairs, Washington, DC"}]},{"given":"Gil","family":"Alterovitz","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Boston, MA"},{"name":"National Artificial Intelligence Institute, Dept. of Veterans Affairs, Washington, DC"}]},{"given":"Luigi","family":"Rao","sequence":"additional","affiliation":[{"name":"Dept. of Pathology, Walter Reed National Military Medical Center, Bethesda, MD"},{"name":"Office of the Surgeon General, US Army Medical Command, Falls Church, VA"}]},{"given":"Mary T.","family":"Brophy","sequence":"additional","affiliation":[{"name":"VA Boston Healthcare System, Boston, MA"},{"name":"Boston University School of Medicine, Boston, MA"}]},{"given":"Nhan V.","family":"Do","sequence":"additional","affiliation":[{"name":"VA Boston Healthcare System, Boston, MA"},{"name":"Boston University School of Medicine, Boston, MA"}]},{"given":"Robert L.","family":"Grossman","sequence":"additional","affiliation":[{"name":"Center for Translational Data Science, University of Chicago, Chicago, IL"}]},{"given":"Nathanael R.","family":"Fillmore","sequence":"additional","affiliation":[{"name":"VA Boston Healthcare System, Boston, MA"},{"name":"Harvard Medical School, Boston, MA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:03Z","timestamp":1706178303000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231062","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}