Journal of the American Medical Informatics Association : JAMIA · 2026
Adapting machine learning models to temporal drift: oral mucositis prediction in head and neck cancer patients receiving proton and carbon ion therapy.
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Wang Yiqiao, Zhang Zhihong, Zhu Yu, Wang Ziying, Ning Renli, Zhang Lijuan, Wan Hongwei
Аннотация
AIM: Temporal drift, defined as changes over time in underlying data distributions, can degrade the performance of clinical prediction models. In head and neck cancer (HNC) radiotherapy, evolving proton and carbon ion therapies may shift the risk of oral mucositis over time. This study aimed to compare machine learning (ML) strategies for mitigating temporal drift in predicting grade ≥2 oral mucositis among patients treated with particle therapy.
METHODS: This retrospective cohort included 1751 adults with HNC treated with particle therapy between May 2015 and December 2022 at a single proton and heavy-ion center. Acute oral mucositis was graded twice weekly using Radiation Therapy Oncology Group criteria. Thirty-five demographic, clinical, and laboratory variables were extracted from electronic health records. Three complementary strategies were examined, including standard ML with inclusion of recent data, temporal modeling, and transfer learning, and each benchmarked using 14 machine-learning algorithms. Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC), F1-score, accuracy, precision, recall, and SHAP-based interpretability.
RESULTS: The incidence of grade ≥2 oral mucositis increased from 27.3% in 2015 to 60.4% in 2022, paralleling evolving dose and modality patterns. Models trained on 2015-2020 data declined in AUC from 0.81 internally to 0.74 and 0.68 on 2021 and 2022 data. A Extras Trees transfer-learning ensemble achieved the best temporal robustness (AUC 0.87, F1 0.82) on 2022 data, demonstrating improved adaptability to drift.
CONCLUSIONS: Temporal drift significantly reduced oral mucositis prediction accuracy over time. Transfer-learning ensembles improved adaptability and maintained reliable, clinically relevant performance for particle-therapy toxicity prediction.
METHODS: This retrospective cohort included 1751 adults with HNC treated with particle therapy between May 2015 and December 2022 at a single proton and heavy-ion center. Acute oral mucositis was graded twice weekly using Radiation Therapy Oncology Group criteria. Thirty-five demographic, clinical, and laboratory variables were extracted from electronic health records. Three complementary strategies were examined, including standard ML with inclusion of recent data, temporal modeling, and transfer learning, and each benchmarked using 14 machine-learning algorithms. Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC), F1-score, accuracy, precision, recall, and SHAP-based interpretability.
RESULTS: The incidence of grade ≥2 oral mucositis increased from 27.3% in 2015 to 60.4% in 2022, paralleling evolving dose and modality patterns. Models trained on 2015-2020 data declined in AUC from 0.81 internally to 0.74 and 0.68 on 2021 and 2022 data. A Extras Trees transfer-learning ensemble achieved the best temporal robustness (AUC 0.87, F1 0.82) on 2022 data, demonstrating improved adaptability to drift.
CONCLUSIONS: Temporal drift significantly reduced oral mucositis prediction accuracy over time. Transfer-learning ensembles improved adaptability and maintained reliable, clinically relevant performance for particle-therapy toxicity prediction.