About this Research Topic
A digital twin is a digital model of any physical thing, from an engine to a process to a person with cancer disease. Ideally, the digital model updates and changes alongside the real-time data as well as factoring historical data. The concept of digital twin has been largely applied to the engineering and aviation industries. For example, in the aviation industry, digital twins are used to simulate fluid dynamics and materials processing to improve engine and aircraft designs. However, digital twins in healthcare are relatively new. As we are in the era of artificial intelligence which involves both computer science and physical engineering spaces, we are observing a trend in the healthcare space extending digital twin solutions in various areas including precision medicine care, surgical simulation, or drug discovery.
More recently, a framework for Cancer Patient Digital Twins (CPDTs) has been proposed. This framework of CPDTs is the virtual presentations of cancer patients - the combination of real-time patient-level data, such as multi-omics, clinical characteristics, computing modelling, simulation, and model inference to make treatment prediction and individual healthcare decisions for cancer patients. Such a framework could apply to a relapsed or treatment resistance cancer patient who is seeking the best treatment plan. The patient’s multi-omic, image, and pathological data collected can be used to build a digital twin to simulate various clinical scenarios, including drug combinations, doses, side effects, and durations for treatment plans and preferences. The outcome will then be presented in a human-understandable format to the patient and clinician. CPDTs will continuously account for the evolving cancer state, thereby improving outcomes and patient-clinician interaction.
With the advances in new technologies to profile each individual patient, especially big data and digital twins, we believe this will provide cancer research around the world, an ideal platform to share their exciting findings.
We invite authors to submit perspective, original research, methodologies, short report, and review articles that will help improve diagnosis, treatment plans, clinical outcome, and management strategies in cancers through the use of CPDT. Potential topics can include, but are not limited to:
1. the potentials of CPDT in oncology and healthcare applications
2. methodology approaches including integration of multimodal and multiscale data for the use of CPDT and mechanistic modelling in cancers
3. reviews on the development challenges of using CPDT and its open research directions
More recently, a framework for Cancer Patient Digital Twins (CPDTs) has been proposed. This framework of CPDTs is the virtual presentations of cancer patients - the combination of real-time patient-level data, such as multi-omics, clinical characteristics, computing modelling, simulation, and model inference to make treatment prediction and individual healthcare decisions for cancer patients. Such a framework could apply to a relapsed or treatment resistance cancer patient who is seeking the best treatment plan. The patient’s multi-omic, image, and pathological data collected can be used to build a digital twin to simulate various clinical scenarios, including drug combinations, doses, side effects, and durations for treatment plans and preferences. The outcome will then be presented in a human-understandable format to the patient and clinician. CPDTs will continuously account for the evolving cancer state, thereby improving outcomes and patient-clinician interaction.
With the advances in new technologies to profile each individual patient, especially big data and digital twins, we believe this will provide cancer research around the world, an ideal platform to share their exciting findings.
We invite authors to submit perspective, original research, methodologies, short report, and review articles that will help improve diagnosis, treatment plans, clinical outcome, and management strategies in cancers through the use of CPDT. Potential topics can include, but are not limited to:
1. the potentials of CPDT in oncology and healthcare applications
2. methodology approaches including integration of multimodal and multiscale data for the use of CPDT and mechanistic modelling in cancers
3. reviews on the development challenges of using CPDT and its open research directions
Keywords: precision medicine, predictive medicine, oncology, digital twins, machine learning, artificial intelligence, dynamical system modelling, mathematical modelling, statistical modelling
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.
