Multimodal integration of image, epigenetic and clinical data to predict BRAF mutation status in melanoma

• Verfasst von: Schneider, Lucas [VerfasserIn]
• Verfasst von: Wies, Christoph [VerfasserIn]
• Verfasst von: Krieghoff-Henning, Eva [VerfasserIn]
• Verfasst von: Bucher, Tabea-Clara [VerfasserIn]
• Verfasst von: Utikal, Jochen [VerfasserIn]
• Verfasst von: Schadendorf, Dirk [VerfasserIn]
• Verfasst von: Brinker, Titus Josef [VerfasserIn]
• Titel: Multimodal integration of image, epigenetic and clinical data to predict BRAF mutation status in melanoma
• Verf.angabe: Lucas Schneider, Christoph Wies, Eva I. Krieghoff-Henning, Tabea-Clara Bucher, Jochen S. Utikal, Dirk Schadendorf, Titus J. Brinker
• E-Jahr: 2023
• Jahr: April 2023
• Umfang: 8 S.
• Fussnoten: Gesehen am 25.04.2023
• Titel Quelle: Enthalten in: European journal of cancer
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 1992
• Jahr Quelle: 2023
• Band/Heft Quelle: 183(2023), Seite 131-138
• ISSN Quelle: 1879-0852
• DOI: doi:10.1016/j.ejca.2023.01.021
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: BRAF-V600 mutation
• Sach-SW: Deep learning
• Sach-SW: Melanoma
• Sach-SW: Multimodal classifier
• Sach-SW: Mutation prediction
• K10plus-PPN: 1843477297

Abstract

Background - In machine learning, multimodal classifiers can provide more generalised performance than unimodal classifiers. In clinical practice, physicians usually also rely on a range of information from different examinations for diagnosis. In this study, we used BRAF mutation status prediction in melanoma as a model system to analyse the contribution of different data types in a combined classifier because BRAF status can be determined accurately by sequencing as the current gold standard, thus nearly eliminating label noise. - Methods - We trained a deep learning-based classifier by combining individually trained random forests of image, clinical and methylation data to predict BRAF-V600 mutation status in primary and metastatic melanomas of The Cancer Genome Atlas cohort. - Results - With our multimodal approach, we achieved an area under the receiver operating characteristic curve of 0.80, whereas the individual classifiers yielded areas under the receiver operating characteristic curve of 0.63 (histopathologic image data), 0.66 (clinical data) and 0.66 (methylation data) on an independent data set. - Conclusions - Our combined approach can predict BRAF status to some extent by identifying BRAF-V600 specific patterns at the histologic, clinical and epigenetic levels. The multimodal classifiers have improved generalisability in predicting BRAF mutation status.