■前哨淋巴结活检(SNB)中的淋巴结转移(NM)对于黑色素瘤分期至关重要。然而,结节内痣(INN)通常可能被错误分类为NM,导致潜在的误诊和不正确的分期。病理学家在评估SNB阳性时存在高度不一致,这可能会导致错误的分期。数字全载玻片成像为在数字病理学中实施人工智能(AI)提供了潜力。在这项研究中,我们评估了AI在SNB中检测NM和INN的能力。
■总共485张苏木精和曙红全幻灯片图像(WSI),包括来自196个SNB的NM和INN,收集并分为培训(279个WSI),验证(89个WSI),和测试集(117WSI)。深度学习模型使用5,956个手动像素注释进行了训练。AI和三名失明的皮肤病理学家评估了测试集,免疫组织化学作为参考标准。
■AI模型显示出优异的性能,用于检测NM的曲线下面积为0.965接收器工作特性(AUC)。相比之下,在皮肤病理学家中,NM检测的AUC在0.94~0.98之间.对于INN的检测,AI(0.781)和皮肤病理学家的AUC均较低(范围为0.63-0.79).
■总而言之,深度学习AI模型在检测NM方面表现出出色的准确性,在检测NM和INN方面实现皮肤病理学水平的表现。重要的是,AI模型显示出区分这两个实体的潜力。然而,进一步验证是必要的。
UNASSIGNED: Nodal metastasis (NM) in sentinel node biopsies (SNB) is crucial for melanoma staging. However, an intra-nodal nevus (INN) may often be misclassified as NM, leading to potential misdiagnosis and incorrect staging. There is high discordance among pathologists in assessing SNB positivity, which may lead to false staging. Digital whole slide imaging offers the potential for implementing artificial intelligence (AI) in digital pathology. In this study, we assessed the capability of AI to detect NM and INN in SNBs.
UNASSIGNED: A total of 485 hematoxylin and eosin whole slide images (WSIs), including NM and INN from 196 SNBs, were collected and divided into training (279 WSIs), validation (89 WSIs), and test sets (117 WSIs). A deep learning model was trained with 5,956 manual pixel-wise annotations. The AI and three blinded dermatopathologists assessed the test set, with immunohistochemistry serving as the reference standard.
UNASSIGNED: The AI model showed excellent performance with an area under the curve receiver operating characteristic (AUC) of 0.965 for detecting NM. In comparison, the AUC for NM detection among dermatopathologists ranged between 0.94 and 0.98. For the detection of INN, the AUC was lower for both AI (0.781) and dermatopathologists (range of 0.63-0.79).
UNASSIGNED: In conclusion, the deep learning AI model showed excellent accuracy in detecting NM, achieving dermatopathologist-level performance in detecting both NM and INN. Importantly, the AI model showed the potential to differentiate between these two entities. However, further validation is warranted.