Background/objectives: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by assisting pathologists in efficiently assessing serial sections for metastases, reducing workload and costs while enhancing accuracy. This proof-of-principle study evaluated the effectiveness of a deep learning algorithm for SLN metastasis detection in early-stage cervical cancer.Methods: We retrospectively analyzed whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained SLNs from early-stage cervical cancer patients diagnosed with an SLN metastasis with either H&E or IHC. A CE-IVD certified commercially available deep learning algorithm, initially developed for detection of breast and colon cancer lymph node metastases, was employed off-label to assess its sensitivity in cervical cancer.Results: This study included 21 patients with early-stage cervical cancer, comprising 15 with squamous cell carcinoma, five with adenocarcinoma, and one with clear cell carcinoma. Among these patients, 10 had macrometastases and 11 had micrometastases in at least one SLN. The algorithm was applied to evaluate H&E WSIs of 47 SLN specimens, including 22 that were negative for metastasis, 13 with macrometastases, and 12 with micrometastases in the H&E slides. The algorithm detected all H&E macro- and micrometastases with 100% sensitivity.Conclusions: This proof-of-principle study demonstrated high sensitivity of a deep learning algorithm for detection of clinically relevant SLN metastasis in early-stage cervical cancer, despite being originally developed for adenocarcinomas of the breast and colon. Our findings highlight the potential of leveraging an existing algorithm for use in cervical cancer, warranting further prospective validation in a larger population.
背景/目的：病理超分期作为前哨淋巴结（SLN）定位的关键环节，通过连续切片和免疫组化（IHC）染色以可靠检测具有临床意义的转移灶。然而，超分期技术存在工作强度大、耗时久且成本高昂的问题。深度学习算法为这一难题提供了潜在解决方案，可辅助病理学家高效评估连续切片中的转移灶，在提升准确性的同时减轻工作负担并降低成本。本原理验证研究旨在评估深度学习算法在早期宫颈癌前哨淋巴结转移检测中的效能。 方法：我们回顾性分析了早期宫颈癌患者的苏木精-伊红（H&E）染色前哨淋巴结全切片图像，这些患者均经H&E或IHC确诊存在前哨淋巴结转移。研究采用一款经CE-IVD认证、原用于检测乳腺癌和结肠癌淋巴结转移的商用深度学习算法，通过超适应症应用评估其在宫颈癌检测中的敏感性。 结果：本研究纳入21例早期宫颈癌患者，包括15例鳞状细胞癌、5例腺癌和1例透明细胞癌。其中10例患者至少存在一个前哨淋巴结宏转移，11例存在微转移。通过对47例前哨淋巴结标本的H&E全切片图像进行分析（包括H&E切片中22例阴性、13例宏转移及12例微转移样本），该算法对H&E切片中所有宏转移和微转移的检测灵敏度达到100%。 结论：本原理验证研究表明，尽管该深度学习算法原为乳腺和结肠腺癌开发，但对早期宫颈癌具有临床意义的前哨淋巴结转移检测表现出高灵敏度。我们的发现凸显了现有算法在宫颈癌领域应用的潜力，值得在更大规模人群中进行前瞻性验证。
Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer
肿瘤(癌症)患者之家