![]() The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Liquid biopsy in the era of immuno-oncology: is it ready for prime-time use for cancer patients? Annals of oncology : official journal of the European Society for Medical Oncology 30, 1448–1459, doi: (2019).īenjamens S, Dhunnoo P & Meskó B. Hofman P, Heeke S, Alix-Panabières C et al. Artificial Intelligence - Opportunities in Cancer Research, (2020). Cancer Detection and Diagnosis Research, (2020). We highlight the critical role that the convergence of artificial intelligence and liquid biopsies holds for the future of therapy response and survival prediction in precision oncology. This chapter presents methodological challenges and advances in handling missingness and sparsity, integrating multidimensional clinical and multi-omic liquid biopsy data for improving the accuracy and robustness of machine and deep learning survival prediction models. Methods for integrating multimodal oncology data are paramount for building robust models for outcomes prediction. In liquid biopsies where specimens are oftentimes samples of convenience, datasets are often of limited scale and lacking paired clinical data. However, clinical datasets are frequently sparse, inconsistent, and incomplete, due to the lack of standardization and interoperability across cancer centers and healthcare systems. Further, comprehensive liquid biopsy analysis with single-cell profiling provides multiscale data on the morphology, genomics, and proteomics of circulating tumor cells (CTCs) and tumor microenvironment cells with deeper resolution on spatiotemporal tumor biology. The complex multimodality in clinical and liquid biopsy data generated by multiparametric diagnostic and tumor profiling technologies presents an exciting opportunity for developing innovative predictive mathematical models by harnessing large datasets across studies and institutional data repositories. ![]()
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