Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
Oral Pathology and Oral Medicine, Dentistry School, Western Parana State University, Cascavel, PR, Brazil.
Virchows Arch. 2019 Oct;475(4):489-497. doi: 10.1007/s00428-019-02642-5. Epub 2019 Aug 17.
Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accuracy of the neural network was 92.7%, which was higher than that of the logistic regression model (86.5%). Our online tool provided 88.2% accuracy, 71.2% sensitivity, and 98.9% specificity. In conclusion, ANN seems to offer a unique decision-making support predicting recurrences and thus adding value for the management of early OTSCC. To the best of our knowledge, this is the first study that applied ANN for prediction of recurrence in early OTSCC and provided a Web-based tool.
早期口腔舌鳞状细胞癌（OTSCC）的复发风险评估仍然是头颈肿瘤学领域的一个挑战。我们研究了使用人工神经网络（ANNs）预测早期 OTSCC 复发的情况。还开发了一个可供公众使用的基于网络的工具。我们为早期 OTSCC 的局部区域复发预测训练了一个前馈神经网络。使用训练好的网络评估了几个预后参数（年龄、性别、T 分期、世界卫生组织组织学分级、浸润深度、肿瘤芽、侵袭最严重模式、神经周围浸润和淋巴细胞宿主反应）。我们的神经网络模型确定肿瘤芽和浸润深度是预测局部区域复发的最重要的预后因素。神经网络的准确率为 92.7%，高于逻辑回归模型（86.5%）。我们的在线工具准确率为 88.2%，敏感性为 71.2%，特异性为 98.9%。总之，ANN 似乎为预测复发提供了独特的决策支持，从而为早期 OTSCC 的管理增加了价值。据我们所知，这是首次应用 ANN 预测早期 OTSCC 复发并提供基于网络的工具的研究。
