Cai Yongkang, Xie Yutong, Zhang Shulian, Wang Yuepeng, Wang Yan, Chen Jian, Huang Zhiquan
Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Guangzhou, China.
Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia.
Head Neck. 2023 Dec;45(12):3053-3066. doi: 10.1002/hed.27533. Epub 2023 Oct 3.
BACKGROUND: Postoperative recurrence of oral cancer is an important factor affecting the prognosis of patients. Artificial intelligence is used to establish a machine learning model to predict the risk of postoperative recurrence of oral cancer. METHODS: The information of 387 patients with postoperative oral cancer were collected to establish the multilayer perceptron (MLP) model. The comprehensive variable model was compared with the characteristic variable model, and the MLP model was compared with other models to evaluate the sensitivity of different models in the prediction of postoperative recurrence of oral cancer. RESULTS: The overall performance of the MLP model under comprehensive variable input was the best. CONCLUSION: The MLP model has good sensitivity to predict postoperative recurrence of oral cancer, and the predictive model with variable input training is better than that with characteristic variable input.
背景：口腔癌术后复发是影响患者预后的重要因素。利用人工智能建立机器学习模型以预测口腔癌术后复发风险。 方法：收集387例口腔癌术后患者的信息，建立多层感知器（MLP）模型。将综合变量模型与特征变量模型进行比较，将MLP模型与其他模型进行比较，以评估不同模型在预测口腔癌术后复发方面的敏感性。 结果：综合变量输入下MLP模型的整体性能最佳。 结论：MLP模型在预测口腔癌术后复发方面具有良好的敏感性，且变量输入训练的预测模型优于特征变量输入的预测模型。
Zhonghua Wei Chang Wai Ke Za Zhi. 2022-4-25
J Oral Pathol Med. 2021-5
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