Fig. 5: Potential future real-time tracking of whole tumour volume, spatial and temporal phenotypic heterogeneity with multi-omics data integration for precision oncology.
From: Artificial intelligence and machine learning in cancer imaging
This schema would allow the processing of multi-institutional data, where each medical centre acquires and stores (in local PACS) its own medical imaging data. To execute quantitative analyses, a radiomics gateway is used to communicate outside the institution by requesting an automated, real-time tumour segmentation from a trusted and specialised AI/ML centre, which allows for continuous learning. The medical images leaving the hospital are anonymised to deal with cyber-security and privacy issues. The segmentation results are used for radiomic feature extraction and analysis, acting as virtual biopsies. The quantitative imaging results are integrated with other biomedical data streams to determine associations with clinical and multi-omics information. Such an approach may develop reliable diagnostic and prognostic tools for multidisciplinary team meetings to improve cancer care in clinical practice; and the evolution of precision oncology. PACS Picture Archiving and Communication System, ML Machine Learning.
