Objective:Esophageal carcinoma (EC) is the eighth most prevalent cancer and the sixth leading cause of cancer-related mortality worldwide. Early detection is vital for improving prognosis, particularly for dysplasia and squamous cell carcinoma (SCC).Methods:This study evaluates a hyperspectral imaging conversion method, the Spectrum-Aided Vision Enhancer (SAVE), for its efficacy in enhancing esophageal cancer detection compared to conventional white-light imaging (WLI). Five deep learning models (YOLOv9, YOLOv10, YOLO-NAS, RT-DETR, and Roboflow 3.0) were trained and evaluated on a dataset comprising labeled endoscopic images, including normal, dysplasia, and SCC classes.Results: Across all five evaluated deep learning models, the SAVE consistently outperformed conventional WLI in detecting esophageal cancer lesions. For SCC, the F1 score improved from 84.3% to 90.4% in regard to the YOLOv9 model and from 87.3% to 90.3% in regard to the Roboflow 3.0 model when using the SAVE. Dysplasia detection also improved, with the precision increasing from 72.4% (WLI) to 76.5% (SAVE) in regard to the YOLOv9 model. Roboflow 3.0 achieved the highest F1 score for dysplasia of 64.7%. YOLO-NAS exhibited balanced performance across all lesion types, with the dysplasia precision rising from 75.1% to 79.8%. Roboflow 3.0 also recorded the highest SCC sensitivity of 85.7%. In regard to SCC detection with YOLOv9, the WLI F1 score was 84.3% (95% CI: 71.7–96.9%) compared to 90.4% (95% CI: 80.2–100%) with the SAVE (p= 0.03). For dysplasia detection, the F1 score increased from 60.3% (95% CI: 51.5–69.1%) using WLI to 65.5% (95% CI: 57.0–73.8%) with SAVE (p= 0.04). These findings demonstrate that the SAVE enhances lesion detectability and diagnostic performance across different deep learning models.Conclusions:The amalgamation of the SAVE with deep learning algorithms markedly enhances the detection of esophageal cancer lesions, especially squamous cell carcinoma and dysplasia, in contrast to traditional white-light imaging. This underscores the SAVE’s potential as an essential clinical instrument for the early detection and diagnosis of cancer.
目的：食管癌是全球第八大常见癌症和第六大癌症相关死亡原因。早期检测对于改善预后至关重要，尤其对于异型增生和鳞状细胞癌。 方法：本研究评估了一种高光谱成像转换方法——光谱辅助视觉增强器，与传统白光成像相比，其在增强食管癌检测方面的效能。使用包含正常、异型增生和鳞状细胞癌类别的标记内镜图像数据集，对五种深度学习模型（YOLOv9、YOLOv10、YOLO-NAS、RT-DETR和Roboflow 3.0）进行了训练和评估。 结果：在所有五种评估的深度学习模型中，SAVE在检测食管癌病变方面均持续优于传统白光成像。对于鳞状细胞癌，使用SAVE时，YOLOv9模型的F1分数从84.3%提升至90.4%，Roboflow 3.0模型从87.3%提升至90.3%。异型增生检测也有所改善，YOLOv9模型的精确度从72.4%（白光成像）提高至76.5%（SAVE）。Roboflow 3.0在异型增生检测中取得了最高的F1分数，为64.7%。YOLO-NAS在所有病变类型中表现出均衡的性能，异型增生的精确度从75.1%上升至79.8%。Roboflow 3.0还记录了最高的鳞状细胞癌灵敏度，为85.7%。在使用YOLOv9进行鳞状细胞癌检测时，白光成像的F1分数为84.3%（95% CI：71.7–96.9%），而SAVE为90.4%（95% CI：80.2–100%）（p=0.03）。对于异型增生检测，F1分数从使用白光成像的60.3%（95% CI：51.5–69.1%）提升至使用SAVE的65.5%（95% CI：57.0–73.8%）（p=0.04）。这些发现表明，SAVE在不同深度学习模型中均能增强病变的可检测性和诊断性能。 结论：与传统白光成像相比，SAVE与深度学习算法的结合显著提高了食管癌病变（尤其是鳞状细胞癌和异型增生）的检测能力。这凸显了SAVE作为癌症早期检测和诊断关键临床工具的潜力。
Evaluation of Spectral Imaging for Early Esophageal Cancer Detection
肿瘤(癌症)患者之家