{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:19:08Z","timestamp":1787663948224,"version":"build-2736575974"},"reference-count":36,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100015098","name":"Seoul Metropolitan Government Seoul National University Boramae Medical Center","doi-asserted-by":"publisher","award":["04\u20102022\u20100003"],"award-info":[{"award-number":["04\u20102022\u20100003"]}],"id":[{"id":"10.13039\/501100015098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>We aimed to develop models for predicting programmed death\u2010ligand 1 (PD\u2010L1) expression following neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) using a multifaceted approach that integrates traditional radiomics, deep learning, and machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Pre\u2010 and post\u2010nCRT computed tomography (CT) images obtained before surgery and clinical data were retrieved for 101 patients with locally advanced ESCC who underwent nCRT followed by radical surgery. Post\u2010nCRT PD\u2010L1 expression levels, which were obtained from the surgical specimens, were categorized into three groups: &lt;\u20091%, 1%\u201310%, and\u2009&gt;\u200910%. Tumor regions on both pre\u2010 and post\u2010nCRT CT scans were segmented for radiomic analysis. Radiomics features were extracted and selected prior to modeling using multinomial logistic regression. Predictive models were subsequently developed using machine learning algorithms that integrated radiomics features, clinical variables, and outputs from deep learning architectures, such as Residual Network (ResNet) and TabNet.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using logistic regression, six radiomics features were selected from an initial pool of 1873. Key clinical factors, including radiation technique and total radiation dose, were also identified as predictors. The combined clinical\u2010radiomics model achieved the highest weighted AUROC of 0.8214. However, its improvement over the clinical\u2010only model was small (AUROC, 0.8214 vs. 0.8171), indicating that radiomics provided limited incremental discrimination beyond clinical variables in this cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combined clinical\u2010radiomics model showed feasible performance for assessing post\u2010nCRT PD\u2010L1 expression in patients with locally advanced ESCC, although the incremental discriminative value of radiomics over clinical variables alone was small. Because this study predicted PD\u2010L1 expression rather than actual response to immune checkpoint inhibitors, these findings should be interpreted as hypothesis\u2010generating and require further external validation before clinical application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72084","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T04:40:33Z","timestamp":1786336833000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multivariable Radiomics Model for Predicting Programmed Death\u2010Ligand 1 Expression After Neoadjuvant Chemoradiotherapy in Esophageal Squamous Cell Carcinoma"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6777-4620","authenticated-orcid":false,"given":"Hyun","family":"Do Jung","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, School of Computing Yonsei University  Seoul Republic of Korea"},{"name":"Yonsei Institute for Digital Health Yonsei University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae Hoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Samsung Medical Center, Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoung Hyuck","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Seoul Metropolitan Government\u2013Seoul National University Boramae Medical Center, Seoul National University College of Medicine  Seoul Republic of Korea"},{"name":"Cancer Research Institute Seoul National University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hye Seung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Pathology Seoul National University Hospital, Seoul National University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeman","family":"Son","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Seoul National University Hospital, Seoul National University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hwiyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Yonsei Institute for Digital Health Yonsei University  Seoul Republic of Korea"},{"name":"H\u2010Data Strategy Center Hallym University Medical Center  Chuncheon Republic of Korea"},{"name":"Department of Biomedical Informatics Hallym University Chuncheon Sacred Heart Hospital  Chuncheon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hak Jae","family":"Kim","sequence":"additional","affiliation":[{"name":"Cancer Research Institute Seoul National University College of Medicine  Seoul Republic of Korea"},{"name":"Department of Radiation Oncology Seoul National University Hospital, Seoul National University College of Medicine  Seoul Republic of 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