Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.
Artem ShmatkoNarmin Ghaffari LalehMoritz GerstungJakob Nikolas KatherPublished in: Nature cancer (2022)
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative information from digital histopathology images. AI is expected to reduce workload for human experts, improve the objectivity and consistency of pathology reports, and have a clinical impact by extracting hidden information from routinely available data. Here, we describe how AI can be used to predict cancer outcome, treatment response, genetic alterations and gene expression from digitized histopathology slides. We summarize the underlying technologies and emerging approaches, noting limitations, including the need for data sharing and standards. Finally, we discuss the broader implications of AI in cancer research and oncology.
Keyphrases
- artificial intelligence
- big data
- deep learning
- machine learning
- papillary thyroid
- gene expression
- squamous cell
- palliative care
- endothelial cells
- emergency department
- convolutional neural network
- lymph node metastasis
- electronic health record
- oxidative stress
- childhood cancer
- dna methylation
- young adults
- mass spectrometry
- genome wide
- optical coherence tomography
- data analysis
- induced pluripotent stem cells
- adverse drug