{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:15:44Z","timestamp":1783577744956,"version":"3.55.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cancer therapy-induced thrombocytopenia (CTIT) is a common hematologic toxicity associated with anti-tumor treatment. Recombinant human interleukin-11 (rhIL-11), as a thrombopoietic agent, is widely used in clinical practice. However, its efficacy and safety exhibit substantial individual variability, and reliable tools for individualized prediction are currently unavailable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Based on real-world data from 12,431 CTIT patients treated with rhIL-11 across 58 Chinese centers (July 2023-June 2024), this study retrospectively collected comprehensive information on demographics, clinical characteristics, and treatment regimens. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to identify key clinical features. Six machine learning algorithms --logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and lightweight gradient boosting machine (LightGBM) --were used to construct separate models for predicting platelet response (efficacy) and adverse events (safety) following rhIL-11 treatment. The dataset was randomly divided into training and test sets in a 7:3 ratio, and an independent external validation set was used to assess model generalizability. SHapley Additive exPlanations (SHAP) analysis was used to provide visual explanations of model feature importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RF model demonstrated superior performance in efficacy prediction, with an AUC of 0.812 (95% CI: 0.797-0.828) in the test set and 0.761 (95% CI: 0.717-0.805) in the external validation set; for safety prediction, the RF model also performed best, with an AUC of 0.796 (95% CI: 0.778-0.814) in the test set and 0.739 (95% CI: 0.695-83), in the external validation set. However, the relatively low specificity of RF models in predicting efficacy and safety limited their potential for practical clinical application. SHAP analysis revealed that the predominant factors influencing efficacy were chemotherapy, prior anti-tumor therapy, history of grade III myelosuppression, targeted therapy, and duration of rhIL-11 treatment; whereas key predictors of safety outcomes included immunotherapy, dosing frequency, chemotherapy, and CTIT grades.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study validated that machine learning models (particularly the RF model combined with SHAP analysis) exhibit highly sensitive preliminary screening performance, while enhancing model interpretability. These findings may facilitate the effective identification of patients most likely to benefit from rhIL-11 and provide references for personalized, precision treatment decisions in CTIT management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1882398","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:35:41Z","timestamp":1783575341000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based models for predicting the efficacy and safety of recombinant human interleukin-11 in the treatment of cancer therapy-induced thrombocytopenia: exploration and preliminary validation from a multicenter retrospective study"],"prefix":"10.3389","volume":"14","author":[{"given":"Yingmei","family":"Wen","sequence":"first","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]},{"name":"Department of Radiation Oncology, The Second Affiliated Hospital of Guangxi Medical University","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxiong","family":"Xia","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Dong","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheming","family":"Liu","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyang","family":"Yu","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuman","family":"Qing","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Li","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Miao","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongling","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Renmin Hospital of Wuhan University","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yao","sequence":"additional","affiliation":[{"name":"Cancer Center, Renmin Hospital of Wuhan University","place":["Wuhan, China"]},{"name":"Hubei Provincial Research Center for Precision Medicine of Cancer","place":["Wuhan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"688969","DOI":"10.3389\/fdata.2021.688969","article-title":"Principles and practice of explainable machine learning","volume":"4","author":"Belle","year":"2021","journal-title":"Front. 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