Oral squamous cell carcinoma (OSCC) accounts for more than 90% of oral malignancies. Despite numerous advancements in understanding its biology, the mean five-year survival rate of OSCC is still very poor at about 50%, with even lower rates when the disease is detected at later stages. We investigate the use of clinical photographic images taken by common smartphones for the automated detection of OSCC cases and for the identification of suspicious cases mimicking cancer that require an urgent biopsy. We perform a retrospective study on a cohort of 1470 patients drawn from both hospital records and online academic sources. We examine various deep learning methods for the early detection of OSCC cases as well as for the detection of suspicious cases. Our results demonstrate the efficacy of these methods in both tasks, providing a comprehensive understanding of the patient’s condition. When evaluated on holdout data, the model to predict OSCC achieved an AUC of 0.96 (CI: 0.91, 0.98), with a sensitivity of 0.91 and specificity of 0.81. When the data are stratified based on lesion location, we find that our models can provide enhanced accuracy (AUC 1.00) in differentiating specific groups of patients that have lesions in the lingual mucosa, floor of mouth, or posterior tongue. These results underscore the potential of leveraging clinical photos for the timely and accurate identification of OSCC.
口腔鳞状细胞癌(OSCC)占口腔恶性肿瘤的90%以上。尽管对其生物学特性的认识已取得诸多进展，但OSCC的五年平均生存率仍处于约50%的较低水平，晚期发现时生存率更低。本研究探讨利用普通智能手机拍摄的临床照片实现OSCC病例的自动化检测，并识别需要紧急活检的疑似癌变病例。通过对医院记录和在线学术资料中提取的1470例患者队列进行回顾性研究，我们检验了多种深度学习模型在OSCC早期检测及可疑病例识别中的应用效果。研究结果表明这些方法在两项任务中均表现出显著效能，可为患者病情提供全面评估。在独立验证数据集中，OSCC预测模型的曲线下面积达0.96（置信区间：0.91-0.98），敏感度为0.91，特异度为0.81。按病变部位分层分析发现，针对舌黏膜、口底或舌后部病变的患者亚组，模型能提供更高的鉴别准确度（AUC达1.00）。这些发现凸显了利用临床照片实现OSCC及时准确识别的巨大潜力。
肿瘤(癌症)患者之家