American Dental Association Science and Research Institute, 211 E Chicago Ave., Chicago, IL, 60611, USA.
Medical Physics; BC Cancer, Surrey, BC, Canada.
Radiat Oncol. 2023 May 8;18(1):77. doi: 10.1186/s13014-023-02274-9.
This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict radiation induced hyposalivation from dose-volume histograms of the parotid glands.
The pre and post-radiotherapy salivary flow rates of 510 head and neck cancer patients were used to fit three predictive models of salivary hypofunction, (1) the Lyman-Kutcher-Burman (LKB) model, (2) a spline-based model, (3) a neural network. A fourth LKB-type model using literature reported parameter values was included for reference. Predictive performance was evaluated using a cut-off dependent AUC analysis.
The neural network model dominated the LKB models demonstrating better predictive performance at every cutoff with AUCs ranging from 0.75 to 0.83 depending on the cutoff selected. The spline-based model nearly dominated the LKB models with the fitted LKB model only performing better at the 0.55 cutoff. The AUCs for the spline model ranged from 0.75 to 0.84 depending on the cutoff chosen. The LKB models had the lowest predictive ability with AUCs ranging from 0.70 to 0.80 (fitted) and 0.67 to 0.77 (literature reported).
Our neural network model showed improved performance over the LKB and alternative machine learning approaches and provided clinically useful predictions of salivary hypofunction without relying on summary measures.
本研究利用了大量头颈部癌症患者的回顾性队列，旨在开发机器学习模型，从腮腺的剂量-体积直方图预测放射性唾液减少。
510 名头颈部癌症患者放疗前后的唾液流量用于拟合三种唾液功能减退的预测模型，（1）Lyman-Kutcher-Burman（LKB）模型，（2）基于样条的模型，（3）神经网络。还包括一个使用文献报道的参数值的第四种 LKB 型模型作为参考。通过基于截断的 AUC 分析评估预测性能。
神经网络模型优于 LKB 模型，在每个截断处的预测性能都更好，AUC 范围为 0.75 至 0.83，具体取决于所选的截断值。基于样条的模型几乎主导了 LKB 模型，拟合的 LKB 模型仅在 0.55 的截断值表现更好。基于样条的模型的 AUC 范围为 0.75 至 0.84，具体取决于所选的截断值。LKB 模型的预测能力最低，AUC 范围为 0.70 至 0.80（拟合）和 0.67 至 0.77（文献报道）。
我们的神经网络模型在 LKB 模型和替代机器学习方法的基础上表现出了更好的性能，并提供了有用的临床预测，无需依赖汇总指标。
