{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:26:53Z","timestamp":1789576013460,"version":"build-2803163510"},"reference-count":58,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Anti\u2010programmed death 1 (PD\u20101) and anti\u2010programmed death ligand 1 (PD\u2010L1) immune checkpoint inhibitors (ICIs) have changed the treatment landscape of many advanced malignancies. However, immune\u2010related adverse events (irAEs) bring great challenges to clinical benefits. The prediction of irAEs is urgently demanded for early detection and intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients in our center who received anti\u2010PD\u2010(L)1 immunotherapy between January 2019 and May 2023 were collected. Logistic least absolute shrinkage and selection operator (LASSO) regression analysis with 10\u2010fold cross\u2010validation was performed to identify the most relevant variables associated with irAEs. Multivariate logistic regression analysis was used to build a prediction model by introducing features selected in LASSO regression analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Overall, 680 eligible patients were included, of whom 330 patients were included in the irAEs group. In the irAEs group, 455 different irAEs were reported, of which 52 events were grade 3 or higher in severity. Endocrinal toxicities (174\/680, 25.59%) were the most commonly reported irAEs. Through LASSO and logistic regression analysis, we developed a risk assessment model to predict the risk of irAEs based on basophil percentage (BASO%), hemoglobin (Hb), absolute lymphocyte count (ALC), platelet\u2010to\u2010lymphocyte ratio (PLR), lymphocyte\u2010to\u2010monocyte ratio (LMR), blood urea nitrogen level (BUN), the Charlson comorbidity index (CCI) score, Eastern Cooperative Oncology Group Performance Status (ECOG PS), and hepatitis B\/hepatitis B surface antigen carriers. The model had a\n                      <jats:italic>C<\/jats:italic>\n                      \u2010index of 0.727, with good discrimination and calibration capabilities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The prediction model developed in our study can screen and monitor patients with high risk of developing irAEs. It may improve prognosis for pan\u2010cancer patients receiving anti\u2010PD\u2010(L)1 immunotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71603","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:56:23Z","timestamp":1770782183000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Risk Factors and Prediction Model for Early\u2010Onset Immune\u2010Related Adverse Events in Pan\u2010Cancer Patients Undergoing Anti\u2010\n                    <scp>PD<\/scp>\n                    \u2010(L)1 Therapy: A Retrospective Study in a Tertiary\u2010Level Hospital"],"prefix":"10.1002","volume":"15","author":[{"given":"Panpan","family":"Jiao","sequence":"first","affiliation":[{"name":"Department of Oncology Zhongshan Hospital Affiliated to Xiamen University  Xiamen Fujian China"},{"name":"School of Pharmaceutical Sciences Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Oncology Zhongshan Hospital Affiliated to Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijuan","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Oncology Zhongshan Hospital Affiliated to Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Oncology Zhongshan Hospital Affiliated to Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Oncology Zhongshan Hospital Affiliated to Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Pharmacy Zhongshan Hospital Affiliated to Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunling","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Pharmaceutical Sciences Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Oncology Zhongshan Hospital Affiliated to Xiamen University  Xiamen Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(21)00333-8"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1287178"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2019.0393"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.70080"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.14740\/jocmr4511"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.7150\/jca.53242"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/1750743X.2024.2353539"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.906824"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdermsci.2016.12.019"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2024-009215"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-16-2324"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611421113"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2019.3221"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-18-2795"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.intimp.2023.111412"},{"issue":"1","key":"e_1_2_11_17_1","article-title":"Artificial Intelligence for Drug Response Prediction in Disease Models","volume":"23","author":"Ballester P. 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