Kauark-Fontes Elisa, Araújo Anna Luiza Damaceno, Andrade Danilo Oliveira, Faria Karina Morais, Prado-Ribeiro Ana Carolina, Laheij Alexa, Rios Ricardo Araújo, Ramalho Luciana Maria Pedreira, Brandão Thais Bianca, Santos-Silva Alan Roger
Department of Propaedeutic and Integrated Clinic, Universidade Federal da Bahia (UFBA), Salvador, Bahia, Brazil.
Head and Neck Surgery Department, Paulo Medical School (FMUSP), University of São, São Paulo, São Paulo, Brazil.
Support Care Cancer. 2025 Jan 14;33(2):96. doi: 10.1007/s00520-025-09158-6.
Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
Clinical data were collected from 157 patients with oral and oropharyngeal squamous cell carcinoma submitted to radiotherapy. Grade 2 OM or higher was considered (NCI). Two dataset versions were used; in the first version, all data were considered, and in the second version, a feature selection was added. Age, smoking status, surgery, radiotherapy prescription dose, treatment modality, histopathological differentiation, tumor stage, presence of oral cancer lesion, and tumor location were selected as key features. The training process used a fivefold cross-validation strategy with 10 repetitions. A total of 4 algorithms and 3 scaling methods were trained (12 models), without using data augmentation.
A comparative assessment was performed. Accuracy greater than 55% was considered. No relevant results were achieved with the first version, closest performance was Decision Trees with 52% of accuracy, 42% of sensitivity, and 60% of specificity. For the second version, relevant results were achieved, K-Nearest Neighbors outperformed with 64% accuracy, 58% sensitivity, and 68% specificity.
ML demonstrated promising results in OM risk prediction. Model improvement was observed after feature selection. Best result was achieved with the KNN model. This is the first study to test ML for OM risk prediction using clinical data.
口腔黏膜炎（OM）反映了多种风险因素之间的复杂相互作用。机器学习（ML）是科学领域一个很有前景的前沿技术，能够处理密集信息。本研究旨在评估ML在预测接受头颈部放疗患者的OM风险方面的性能。
收集了157例接受放疗的口腔和口咽鳞状细胞癌患者的临床数据。采用美国国立癌症研究所（NCI）的标准，将2级及以上的OM视为观察对象。使用了两个数据集版本；在第一个版本中，考虑了所有数据，在第二个版本中，增加了特征选择。选择年龄、吸烟状况、手术、放疗处方剂量、治疗方式、组织病理学分化、肿瘤分期、口腔癌病变的存在情况以及肿瘤位置作为关键特征。训练过程采用五折交叉验证策略，重复10次。共训练了4种算法和3种缩放方法（12个模型），未使用数据增强。
进行了对比评估。将准确率大于55%视为有效结果。第一个版本未取得相关结果，最接近的性能是决策树，准确率为52%，灵敏度为42%，特异性为60%。对于第二个版本，取得了相关结果，K近邻算法表现最佳，准确率为64%，灵敏度为58%，特异性为68%。
ML在OM风险预测方面显示出有前景的结果。特征选择后观察到模型有所改进。KNN模型取得了最佳结果。这是第一项使用临床数据测试ML用于OM风险预测的研究。
