Li Jing Wen, Zhang Meng Jing, Zhou Ya Fang, Adeoye John, Pu Jing Ya Jane, Thomson Peter, McGrath Colman Patrick, Zhang Dian, Zheng Li Wu
Division of Oral & Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
Department of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
iScience. 2025 Feb 18;28(3):112062. doi: 10.1016/j.isci.2025.112062. eCollection 2025 Mar 21.
This study addresses the challenge of accurately predicting malignant transformation risk in patients with oral potentially malignant disorders (OPMDs). Using data from 1,094 patients across three institutions (2004-2023), the researchers compared traditional statistical methods, including a Cox proportional hazards (Cox-PH) nomogram, with machine learning (ML) algorithms. A novel Self Attention Artificial Neural Network (SANN) model was developed, trained, and validated alongside other ML models including ANN, RF, and DeepSurv. The SANN model outperformed all other approaches, achieving an AUC of 0.9877, with sensitivity, specificity, accuracy, and precision exceeding 0.96. In comparison, the Cox-PH nomogram achieved AUCs of 0.880-0.902. Comprehensive evaluations using Receiver Operating Characteristic, calibration curves, and decision curve analysis demonstrated SANN's superior predictive efficacy, robustness, and generalizability. These findings highlight the potential of customized ML models, particularly SANN, to enhance early identification and management of high-risk OPMD patients, outperforming conventional statistical methods.
本研究应对了准确预测口腔潜在恶性疾病（OPMD）患者恶性转化风险这一挑战。研究人员利用来自三个机构的1094例患者的数据（2004年至2023年），将包括Cox比例风险（Cox-PH）列线图在内的传统统计方法与机器学习（ML）算法进行了比较。开发、训练并验证了一种新型自注意力人工神经网络（SANN）模型以及包括人工神经网络（ANN）、随机森林（RF）和深度生存模型（DeepSurv）在内的其他ML模型。SANN模型的表现优于所有其他方法，曲线下面积（AUC）达到0.9877，灵敏度、特异性、准确度和精确率均超过0.96。相比之下，Cox-PH列线图的AUC为0.880至0.902。使用受试者工作特征曲线、校准曲线和决策曲线分析进行的综合评估表明，SANN具有卓越的预测效能、稳健性和通用性。这些发现凸显了定制ML模型，尤其是SANN，在加强对高危OPMD患者的早期识别和管理方面的潜力，其表现优于传统统计方法。
