{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:22:04Z","timestamp":1787728924950,"version":"build-2784847793"},"reference-count":51,"publisher":"Wiley","issue":"16","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/creativecommons.org\/licenses\/by\/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"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Thoracic Cancer"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In patients with small cell lung cancer (SCLC), nutritional status is a key determinant of disease progression, treatment tolerance, and prognosis. The prognostic nutritional index (PNI), reflecting both immune and nutritional conditions, is widely used to evaluate prognostic risk, but its longitudinal monitoring is often limited by incomplete clinical data in real\u2010world settings. This study aimed to propose a decision\u2010support framework for predicting future nutritional status and improving risk screening in SCLC patients with missing data. Using PNI values and related variables from the first four follow\u2010up time points to predict the PNI at the fifth time point (PNI5) and evaluated the impact of different missing data imputation strategies on predictive performance. Missing PNI values were imputed using mean imputation, multiple imputation (MI), Kalman filtering, k\u2010nearest neighbors (KNN), and XGBoost imputation. Predictive models were developed with two machine learning algorithms: random forest (RF) and XGBoost. The RF model demonstrated better overall performance, and MI combined with RF achieved the best predictive accuracy (MAE: 2.952; RMSE: 3.727). Predicted PNI values were further translated into risk categories using a predefined threshold (PNI\u2009=\u200945), with overall accuracy of 93.33%, PPV 95.24%, and NPV 92.59%. Importantly, the model enabled risk assessment in patients with incomplete laboratory data, covering previously unassessable cases and reducing monitoring gaps. This study highlights the importance of appropriate imputation strategies in and provides a practical tool for continuous nutritional risk assessment and early identification of high\u2010risk patients in SCLC.<\/jats:p>","DOI":"10.1111\/1759-7714.70381","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T08:59:46Z","timestamp":1787648386000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decision\u2010Support Framework for Nutritional Risk in Small Cell Lung Cancer: A Time\u2010Series Model Using Imputation Strategies for Incomplete Clinical Data"],"prefix":"10.1111","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-2045","authenticated-orcid":false,"given":"Ruili","family":"Pan","sequence":"first","affiliation":[{"name":"Department of Nursing Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College  Beijing 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