Pan-cancer integrative histology-genomic analysis via multimodal deep learning

• Published in: Cancer cell Vol. 40; no. 8; pp. 865 - 878.e6
• Main Authors: Chen, Richard J., Lu, Ming Y., Williamson, Drew F.K., Chen, Tiffany Y., Lipkova, Jana, Noor, Zahra, Shaban, Muhammad, Shady, Maha, Williams, Mane, Joo, Bumjin, Mahmood, Faisal
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 08.08.2022
• Subjects: Algorithms; artificial intelligence; biomarker discovery; cancer prognosis; computational pathology; data fusion; Deep Learning; digital pathology; Genomics - methods; Humans; multimodal integration; multimodal prognostic models; Neoplasms - genetics; Neoplasms - pathology; pan-cancer; Prognosis; data fusion; deep learning; pan-cancer; computational pathology; digital pathology; multimodal prognostic models; multimodal integration; cancer prognosis; biomarker discovery; artificial intelligence

The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how these data sources can be integrated to develop joint...