Deep learning detection of melanoma metastases in lymph nodes

• Verfasst von: Jansen, Philipp [VerfasserIn]
• Verfasst von: Baguer, Daniel Otero [VerfasserIn]
• Verfasst von: Duschner, Nicole [VerfasserIn]
• Verfasst von: Le'Clerc Arrastia, Jean [VerfasserIn]
• Verfasst von: Schmidt, Maximillian [VerfasserIn]
• Verfasst von: Landsberg, Jennifer Caroline [VerfasserIn]
• Verfasst von: Wenzel, Jörg [VerfasserIn]
• Verfasst von: Schadendorf, Dirk [VerfasserIn]
• Verfasst von: Hadaschik, Eva [VerfasserIn]
• Verfasst von: Maass, Peter [VerfasserIn]
• Verfasst von: Schaller, Jörg [VerfasserIn]
• Verfasst von: Griewank, Klaus [VerfasserIn]
• Titel: Deep learning detection of melanoma metastases in lymph nodes
• Verf.angabe: Philipp Jansen, Daniel Otero Baguer, Nicole Duschner, Jean Le’Clerc Arrastia,Maximilian Schmidt, Jennifer Landsberg, Jörg Wenzel, Dirk Schadendorf, Eva Hadaschik, Peter Maass, Jörg Schaller, Klaus Georg Griewank
• E-Jahr: 2023
• Jahr: July 2023
• Umfang: 10 S.
• Illustrationen: Illustrationen, Diagramme
• Fussnoten: Online verfügbar: 29. April 2023, Artikelversion: 23. Mai 2023 ; Gesehen am 26.02.2024 ; Available online: 29 April 2023
• Titel Quelle: Enthalten in: European journal of cancer
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1992
• Jahr Quelle: 2023
• Band/Heft Quelle: 188(2023) vom: Juli, Seite 161-170
• ISSN Quelle: 1879-0852
• DOI: doi:10.1016/j.ejca.2023.04.023
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1881617696

Abstract

Background - In melanoma patients, surgical excision of the first draining lymph node, the sentinel lymph node (SLN), is a routine procedure to evaluate lymphogenic metastases. Metastasis detection by histopathological analysis assesses multiple tissue levels with hematoxylin and eosin and immunohistochemically stained glass slides. Considering the amount of tissue to analyze, the detection of metastasis can be highly time-consuming for pathologists. The application of artificial intelligence in the clinical routine has constantly increased over the past few years. Methods - IIn this multi-center study, a deep learning method was established on histological tissue sections of sentinel lymph nodes collected from the clinical routine. The algorithm was trained to highlight potential melanoma metastases for further review by pathologists, without relying on supplementary immunohistochemical stainings (e.g. anti-S100, anti-MelanA). Results - The established method was able to detect the existence of metastasis on individual tissue cuts with an area under the curve of 0.9630 and 0.9856 respectively on two test cohorts from different laboratories. The method was able to accurately identify tumour deposits>0.1 mm and, by automatic tumour diameter measurement, classify these into 0.1 mm to −1.0 mm and>1.0 mm groups, thus identifying and classifying metastasis currently relevant for assessing prognosis and stratifying treatment. Conclusions - Our results demonstrate that AI-based SLN melanoma metastasis detection has great potential and could become a routinely applied aid for pathologists. Our current study focused on assessing established parameters; however, larger future AI-based studies could identify novel biomarkers potentially further improving SLN-based prognostic and therapeutic predictions for affected patients.