About this Research Topic
Since its inception by NASA in 2010, the digital twin concept has transitioned from aerospace to various sectors, including healthcare, where it promises revolutionary changes in managing complex health conditions like cancer. The adaptation of digital twins in oncology is relatively recent, facing fundamental challenges in simulating complex, dynamical, multi-scale biological processes that are often poorly understood or purely hypothetical. Moreover, the multimodal patient-specific datasets necessary for constructing these digital twins are often siloed and inaccessible for comprehensive AI/ML analysis. These issues are compounded by biases in data collection, small datasets, and algorithm development, exacerbating existing health disparities.
This research topic seeks to propel forward the state-of-the-art digital twin technologies tailored for cancer patients. By examining recent advances in data curation and processing, multiscale modeling, and computational techniques, the aim is to integrate digital twin technologies into clinical workflow, thereby enhancing diagnosis, monitoring, treatment, and prevention for individual cancer patients. The overarching goal is to transform digital twin research into a global collaborative initiative, improving the treatment outcomes and quality of life for cancer patients.
To advance the integration of digital twins into oncology, this call for papers outlines a series of key topics. We prioritize contributions that push the boundaries of multimodal data processing and analysis, multiscale modeling, and ethical AI usage, such as:
• High-throughput and high-fidelity data acquisition, engineering and analysis across modalities
• Standards and protocols for AI/ML data handling and labeling
• Effective integration and management of diverse real-world datasets and data augmentation
• Innovations in multi-omics integration for targeted cancer treatments
• Advances in system and organ modeling to predict disease progression and treatment response
• Novel AI/ML technologies for precision cancer diagnosis and treatment
• Ethical considerations and frameworks for trustworthy AI in healthcare
These topics align with the intricacies of deploying digital twin technologies in predictive oncology, guiding contributions that address both the technical challenges and the ethical implications of this rapidly evolving field.
This research topic seeks to propel forward the state-of-the-art digital twin technologies tailored for cancer patients. By examining recent advances in data curation and processing, multiscale modeling, and computational techniques, the aim is to integrate digital twin technologies into clinical workflow, thereby enhancing diagnosis, monitoring, treatment, and prevention for individual cancer patients. The overarching goal is to transform digital twin research into a global collaborative initiative, improving the treatment outcomes and quality of life for cancer patients.
To advance the integration of digital twins into oncology, this call for papers outlines a series of key topics. We prioritize contributions that push the boundaries of multimodal data processing and analysis, multiscale modeling, and ethical AI usage, such as:
• High-throughput and high-fidelity data acquisition, engineering and analysis across modalities
• Standards and protocols for AI/ML data handling and labeling
• Effective integration and management of diverse real-world datasets and data augmentation
• Innovations in multi-omics integration for targeted cancer treatments
• Advances in system and organ modeling to predict disease progression and treatment response
• Novel AI/ML technologies for precision cancer diagnosis and treatment
• Ethical considerations and frameworks for trustworthy AI in healthcare
These topics align with the intricacies of deploying digital twin technologies in predictive oncology, guiding contributions that address both the technical challenges and the ethical implications of this rapidly evolving field.
Keywords: Digital Twin, predictive oncology, oncology, artificial intelligence, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
