Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

Abstract

Background: Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological evaluation, supported by immunohistochemical staining, is the gold standard for detecting LN metastases. However, the process is labor-intensive, requiring the analysis of multiple tissue levels, which increases both time and cost. With the growing integration of artificial intelligence (AI) into clinical workflows, there is potential to streamline this process, enhancing efficiency and accuracy.

Method: This study analyzed 53 WSIs (12 negative samples). Images are all derived from hematoxylin-eosin (H&E) stains and were uploaded to the Orca AI cloud-based platform (SpatialX Diagnostics, Inc.), a commercially available platform specifically designed for AI pathology. An AI-driven model was developed and trained to detect melanoma metastases directly from H&E histopathological images, bypassing additional immunohistochemical staining. The algorithm was designed to identify the presence of metastases and classify tumor deposits based on size, specifically distinguishing deposits greater than 0.1 mm and less than 1.0 mm in diameter, which are critical thresholds for prognostic evaluation. The model was validated on 9 WSIs (2 negative) that were not seen by the model. For every WSI in the validation set, one of the following 4 classes was assigned: normal, metastasis (<0.1 mm), metastasis (0.1-1 mm), and metastasis (>1 mm). The class corresponding to the highest metastasis size range was assigned to the whole sample. The results were then examined by a board-certified pathologist.

Results: The AI algorithm demonstrated high accuracy in detecting LN metastases in patients with melanoma. It effectively identified and classified tumor deposits with a specificity of 0.91 and a sensitivity of 0.74. The model also distinguishes between smaller deposits (>0.1 mm) and larger deposits (>1.0 mm). This stratification is essential for accurate staging, prognosis determination, and treatment planning, highlighting the algorithm's potential clinical value. When evaluating the model performance in the WSI classification tasks, the model showed high agreement with the pathologist's classification, correctly identifying 7 WSIs as metastasis (>1 mm) and labeling 1 normal WSI as metastasis (0.7 mm).

Conclusion: The study's findings underscore the potential of AI in revolutionizing the detection of melanoma metastasis in lymph nodes. By significantly reducing the reliance on time-consuming and costly immunohistochemical staining, AI-driven tools can streamline diagnostic workflows, improve accuracy, and potentially enhance patient outcomes. As AI technology continues to evolve, its application in melanoma management could become a cornerstone of modern pathology, offering a powerful adjunct to traditional diagnostic methods.