Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Oncology

Background:

• Digital twins (DTHs) and virtual twins (VTHs) are emerging technologies for precision medicine in healthcare.
• These patient-centric tools hold promise for revolutionizing cancer care.

Purpose of the Study:

• To map current DTH and VTH technologies in oncology.
• To summarize their technical solutions and assess credibility.
• To identify challenges and future directions for clinical implementation.

Main Methods:

• A systematic scoping review was conducted.
• Searched major bibliographic databases for relevant studies.
• Included 30 studies from 441 records, covering various cancers and applications.

Main Results:

• DTH and VTH applications span diagnosis, therapy, and monitoring across diverse cancers.
• Significant heterogeneity exists in technical approaches and outcomes.
• Most solutions rely on synthetic or limited real-world data, impacting reliability.
• Limited integration of real-time data and machine learning for predictive modeling was observed.
• Technical challenges include data integration, scalability, and ethical concerns (privacy, security).
• Clinical validation is often insufficient, leading to partial credibility.

Conclusions:

• Multidisciplinary collaboration is crucial to address technical and ethical hurdles.
• Substantial advancements are needed to transition DTH and VTH from experimental to clinical settings in personalized oncology.