Detection of lung cancer lymph node metastases from whole-slide histopathological images using a two-step deep learning approach.

In this study, a novel two-step deep learning algorithm was developed to address the issue of false-positive prediction while maintaining accurate cancer detection. Three-hundred and forty-nine whole-slide lung cancer lymph node images, including 233 slides for algorithm training, 10 slides for validation, and 106 slides for evaluation, were collected. In the first step, a deep learning algorithm was used to eliminate frequently misclassified noncancerous regions (lymphoid follicles). In the second step, a deep learning classifier was developed to detect cancer cells. Using this two-step approach, errors were reduced by 36.4% on average and up to 89% in slides with reactive lymphoid follicles. Furthermore, 100% sensitivity was reached in cases of macro-metastases, micro-metastases, and isolated tumor cells. To reduce the small number of remaining false-positives, an ROC curve was created using foci size thresholds of 0.6 mm and 0.7 mm, achieving sensitivity and specificity of 79.6% and 96.5%, and 75.5% and 98.2%, respectively. A two-step approach can be used to detect lung cancer metastases in lymph node tissue effectively and with few false-positives. PMID: 31541645 [PubMed - as supplied by publisher]
Source: The American Journal of Pathology - Category: Pathology Authors: Tags: Am J Pathol Source Type: research