The progress of high throughput DNA/RNA sequencing and the emergence of an image-based environment, have paved the way towards a variety of applications in precision oncology. The field of Artificial Intelligence (AI) has gained unprecedented momentum by providing opportunities for using the plethora of complex high dimensional datasets that are generated from those domains to revolutionize cancer research and care. Thus, AI-based systems applying modern computational algorithms are particularly well suited for exploring genotype–phenotype relationships aiming to improve the therapeutic decision-making process.
This Research Topic focuses on the integration of multi-omics and clinical data, coupled with medical imaging using state-of-art in AI for improving cancer diagnosis, prognosis and therapy.
?This issue will highlight the integration of multi-modal genomics data and imaging data coupled with the state of the art of AI algorithms and their applications to enhance the delivery of precision oncology.
We welcome manuscripts in the following areas:
- AI-based algorithms for the identification of both diagnostic and prognostic markers
- Applications of machine learning and deep learning systems on genomic data linked to either CT/MRI/PET or pathologic images
- AI-based approaches for support clinical decision-making process
- Explainable AI systems for use in precision oncology
- AI tools for pre-screening or definitive testing
The progress of high throughput DNA/RNA sequencing and the emergence of an image-based environment, have paved the way towards a variety of applications in precision oncology. The field of Artificial Intelligence (AI) has gained unprecedented momentum by providing opportunities for using the plethora of complex high dimensional datasets that are generated from those domains to revolutionize cancer research and care. Thus, AI-based systems applying modern computational algorithms are particularly well suited for exploring genotype–phenotype relationships aiming to improve the therapeutic decision-making process.
This Research Topic focuses on the integration of multi-omics and clinical data, coupled with medical imaging using state-of-art in AI for improving cancer diagnosis, prognosis and therapy.
?This issue will highlight the integration of multi-modal genomics data and imaging data coupled with the state of the art of AI algorithms and their applications to enhance the delivery of precision oncology.
We welcome manuscripts in the following areas:
- AI-based algorithms for the identification of both diagnostic and prognostic markers
- Applications of machine learning and deep learning systems on genomic data linked to either CT/MRI/PET or pathologic images
- AI-based approaches for support clinical decision-making process
- Explainable AI systems for use in precision oncology
- AI tools for pre-screening or definitive testing
