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In recent years, machine learning and deep learning-based approaches, two sub-fields of artificial intelligence, have emerged as key components in biomedical data analyses (1-5). They can be applied to image segmentation, identifying insertion/deletion mutations, protein alignments, and so on. Several studies have integrated pathological image data with genomics data. Yuan et al. have quantitatively analyzed image data to better describe and validate the independent prognostic factors in estrogen receptor-negative breast cancer (6). Another study by Copper et al. also used histopathology images and genomics data to identify prognostic factors in breast cancer (7). Other types of cancers such as prostate cancer (8), renal cell carcinoma (9), low grade glioma (10), and non-small cell lung cancer (11), just to name a few, have also been studied by approaches integrating (multi-) omics data with pathology images.

Role of artificial intelligence in integrated analysis of multi-omics and imaging data in cancer research