ArticleAnnals of nuclear medicine2026
Development and validation of a machine learning-based hierarchical classification model for predicting the early therapeutic response to initial
Article in Annals of nuclear medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivePersonalized tools for accurately predicting papillary thyroid carcinoma (PTC) patients' response to initial
methodsThis study retrospectively enrolled 702 PTC patients undergoing initial
resultsFor the first-layer classification, the Random Forest model optimally identified Non-ER. For the second-layer classification, the Logistic Regression + Extreme Gradient Boosting fusion model excelled. The final model, built on 17 features, achieved a hierarchical F1 score of 0.849 and an overall accuracy of 69.7%. Both the model's superior predictive accuracy over the benchmark model and its strong association with progression-free survival confirmed the value of this hierarchical model.
conclusionsThis study developed an ML-based hierarchical model capable of forecasting early response to initial
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