Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
Einstein Center for Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany.
Sci Rep. 2023 Feb 9;13(1):2296. doi: 10.1038/s41598-023-29204-9.
Oral squamous cell carcinoma (OSCC) is amongst the most common malignancies, with an estimated incidence of 377,000 and 177,000 deaths worldwide. The interval between the onset of symptoms and the start of adequate treatment is directly related to tumor stage and 5-year-survival rates of patients. Early detection is therefore crucial for efficient cancer therapy. This study aims to detect OSCC on clinical photographs (CP) automatically. 1406 CP(s) were manually annotated and labeled as a reference. A deep-learning approach based on Swin-Transformer was trained and validated on 1265 CP(s). Subsequently, the trained algorithm was applied to a test set consisting of 141 CP(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved a classification accuracy of 0.986 and an AUC of 0.99 for classifying OSCC on clinical photographs. Deep learning-based assistance of clinicians may raise the rate of early detection of oral cancer and hence the survival rate and quality of life of patients.
口腔鳞状细胞癌 (OSCC) 是最常见的恶性肿瘤之一，全球估计发病率为 37.7 万例，死亡人数为 17.7 万例。症状出现到开始进行充分治疗之间的时间间隔与肿瘤分期和患者 5 年生存率直接相关。因此，早期发现对于有效的癌症治疗至关重要。本研究旨在自动检测临床照片 (CP) 中的 OSCC。对 1406 张 CP 进行了手动注释和标记作为参考。基于 Swin-Transformer 的深度学习方法在 1265 张 CP 上进行了训练和验证。随后，将训练好的算法应用于由 141 张 CP 组成的测试集。计算了分类准确率和曲线下面积 (AUC)。该方法在分类 CP 上的 OSCC 方面达到了 0.986 的分类准确率和 0.99 的 AUC。基于深度学习的临床医生辅助工具可能会提高口腔癌的早期发现率，从而提高患者的生存率和生活质量。
