Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.

IF 2.9 3区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Oral diseases Pub Date : 2025-02-01 Epub Date: 2024-10-27 DOI:10.1111/odi.15159

Xueying Mei, Wenhao Luo, Wan Duan, Zhuming Guo, Xiaomei Lao, Sien Zhang, Le Yang, Bin Zeng, Jianbin Gong, Wei Deng, Guiqing Liao, Yujie Liang

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Abstract

Objectives: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.

Patients and methods: This predictive modeling study was conducted of 1163 patients with OSCC from Hospital of Stomatology, SYSU and SYSU Cancer Center from March 2009 to October 2021. Clinical, pathological, and hematological features of the patients were collected. Six ML algorithms were explored, and model performance was assessed by accuracy, sensitivity, specificity, f1 score, and AUC. SHAP values were used to identify the variables with the greatest contribution to the model.

Results: Among the 1163 patients (mean [SD] age, 55.36 [12.91] years), 563 are from development cohort and 600 are from validation cohort. The Logistic Regression algorithm outperformed all other models, with a sensitivity of 94.7% (68.2%), a specificity of 55.3% (63.7%), and the AUC of 0.76 ± 0.09 (0.723) in the development (validation) cohort. The most predictive feature was neutrophil count.

Conclusion: This study demonstrated ML models can improve clinical prediction of oral squamous cell carcinoma progression through basic information of patients. These tools could be used to provide individual risk estimation and may help direct intervention.

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用于预测 OSCC 肿瘤进展的机器学习模型的开发与验证

目标：利用机器学习（ML）方法开发口腔鳞状细胞癌（OSCC）患者肿瘤进展预测模型，为患者个体预后提供风险评估：利用机器学习（ML）方法建立口腔鳞状细胞癌（OSCC）患者肿瘤进展预测模型，为患者个体预后提供风险评估：这项预测模型研究的对象是2009年3月至2021年10月期间，来自上海协和医学院附属口腔医院和上海协和医学院肿瘤中心的1163名OSCC患者。研究收集了患者的临床、病理和血液学特征。探讨了六种 ML 算法，并通过准确性、灵敏度、特异性、f1 分数和 AUC 评估了模型性能。SHAP值用于确定对模型贡献最大的变量：在 1163 名患者（平均 [SD] 年龄 55.36 [12.91] 岁）中，563 人来自开发队列，600 人来自验证队列。逻辑回归算法的灵敏度为 94.7%（68.2%），特异性为 55.3%（63.7%），AUC 为 0.76 ± 0.09 (0.723)。最具预测性的特征是中性粒细胞计数：本研究表明，ML 模型可以通过患者的基本信息改善口腔鳞状细胞癌进展的临床预测。这些工具可用于提供个体风险估计，并有助于指导干预。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Oral diseases 医学-牙科与口腔外科

CiteScore

7.60

自引率

5.30%

发文量

325

审稿时长

4-8 weeks

期刊介绍： Oral Diseases is a multidisciplinary and international journal with a focus on head and neck disorders, edited by leaders in the field, Professor Giovanni Lodi (Editor-in-Chief, Milan, Italy), Professor Stefano Petti (Deputy Editor, Rome, Italy) and Associate Professor Gulshan Sunavala-Dossabhoy (Deputy Editor, Shreveport, LA, USA). The journal is pre-eminent in oral medicine. Oral Diseases specifically strives to link often-isolated areas of dentistry and medicine through broad-based scholarship that includes well-designed and controlled clinical research, analytical epidemiology, and the translation of basic science in pre-clinical studies. The journal typically publishes articles relevant to many related medical specialties including especially dermatology, gastroenterology, hematology, immunology, infectious diseases, neuropsychiatry, oncology and otolaryngology. The essential requirement is that all submitted research is hypothesis-driven, with significant positive and negative results both welcomed. Equal publication emphasis is placed on etiology, pathogenesis, diagnosis, prevention and treatment.