Hu Ruifang, Liu Xianping, Zhang Yong, Arthur Clement, Qin Dongguang
Endoscopy Center, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
Head and Neck Surgery, First Shanxi Hospital of Shanxi Medical University, Taiyuan, China.
Front Oncol. 2025 Jun 10;15:1582011. doi: 10.3389/fonc.2025.1582011. eCollection 2025.
Laryngeal cancer accounts for approximately 2% of all cancers globally and is considered one of the most aggressive types of head and neck cancer. Prompt diagnosis is crucial to improving survival and function. Direct laryngoscopy and imaging modalities are conventional diagnostic methods. However, laryngeal cancer diagnosis can be delayed, and early subtle mucosal changes can be missed. Flexible nasal endoscopy, particularly when integrated with artificial intelligence and optical biopsy methods, has shown promise in the early detection of laryngeal cancer. Yet, there is little literature on the combined experiences of these modalities.
This prospective observational study involved 142 patients with suspected laryngeal cancer. All included patients underwent flexible nasal endoscopy with topical anesthesia. The patients were assessed using one or more optical biopsy techniques (Narrow Band Imaging [NBI], SPIES, or ISCAN), depending on available equipment and whether the lesions were visible. AI algorithms were retrospectively applied to endoscopic images to categorize lesions as cancerous or non-cancerous depending on vascular, textural, and color characteristics. The AI model was trained on a different pre-annotated dataset, and the images from the study cohort were not used to train the AI model - this methodologically ensures no bias has been introduced into the evaluation. Histopathology was used as the reference standard. Diagnostic performance was calculated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
The study revealed superior sensitivity (95.2%) and specificity (96.5%) with AI-enhanced endoscopy compared to conventional endoscopy (89.6%, 92.4%), respectively. Optical biopsy methods provided better visualization of lesions; however, not all patients had all three modalities in a single procedure. Diagnostic delay was shortened with a median time of 15 to 7 days (<0.001). Inter-rater agreement was strong overall (κ=0.84), with hoarseness having the most reliability, most likely due to better exposure of the glottis.
AI and selectively applied optical biopsy methods improved diagnostic accuracy in nasal endoscopy and reduced time delays for the early detection and management of laryngeal cancer. Further study in multicenters will allow for further validation of this work.
喉癌约占全球所有癌症的2%，被认为是头颈癌中侵袭性最强的类型之一。及时诊断对于提高生存率和功能至关重要。直接喉镜检查和影像学检查是传统的诊断方法。然而，喉癌诊断可能会延迟，早期细微的黏膜变化可能会被遗漏。柔性鼻内镜检查，特别是与人工智能和光学活检方法相结合时，在喉癌的早期检测中显示出了前景。然而，关于这些方法联合应用的经验的文献很少。
这项前瞻性观察性研究纳入了142例疑似喉癌患者。所有纳入的患者均接受了局部麻醉下的柔性鼻内镜检查。根据可用设备和病变是否可见，使用一种或多种光学活检技术（窄带成像[NBI]、SPIES或ISCAN）对患者进行评估。人工智能算法被回顾性应用于内镜图像，根据血管、纹理和颜色特征将病变分类为癌性或非癌性。人工智能模型是在一个不同的预先标注的数据集上进行训练的，研究队列的图像未用于训练人工智能模型——这种方法学确保在评估中没有引入偏差。组织病理学被用作参考标准。使用灵敏度、特异度、阳性预测值（PPV）和阴性预测值（NPV）计算诊断性能。
研究显示，与传统内镜检查（分别为89.6%、92.4%）相比，人工智能增强内镜检查的灵敏度（95.2%）和特异度（96.5%）更高。光学活检方法能更好地观察病变；然而，并非所有患者在一次检查中都能接受所有三种方法。诊断延迟缩短，中位时间从15天缩短至7天（<0.001）。总体而言，评分者间一致性较强（κ=0.84），声音嘶哑的可靠性最高，最可能是由于声门暴露更好。
人工智能和选择性应用的光学活检方法提高了鼻内镜检查的诊断准确性，并减少了喉癌早期检测和管理的时间延迟。多中心的进一步研究将有助于对这项工作进行进一步验证。
