About this Research Topic
In silico oncology is a new scientific, technological, and gradually clinical discipline that aims to support cancer prevention, diagnosis, prognosis and patient-specific optimization of eventual clinical interventions or mimic clinical studies through the conduct of in silico experiments, i.e., experiments on a computer. Such experiments make use of models or digital (virtual) twins of parts of - or the entirety of – the human body, and eventually its environment, as well as these models’ natural behavior and/or interactions with candidate intervention(s). The models utilized can be based on mechanistic multiscale modeling and simulation and/or artificial intelligence (AI) modeling and/or advanced statistical modeling. All models must be strictly clinically validated and certified before being used in an actual clinical setting. Virtual (digital) twins for predictive oncology are posing a paradigm shift for precision and personalized cancer care. Cancer patient digital (virtual) twins are expected to reach their full potential to support personalized cancer prevention, diagnosis and prognosis and predict cancer treatment response and resilience after treatment.
In this Research Topic, we welcome contributions in the form of original research, review, mini review, hypothesis and theory, perspective, that cover, but are not limited to, following themes:
- Mechanistic multiscale and/or AI based modeling of clinical tumors and their response to treatment;
- AI and/or advanced statistical modeling of the resilience of cancer survivors;
- Technologically integrated digital (virtual) twins of specific cancers and their response to treatment and clinical validation;
- Certification and translation of digital (virtual) twins.
Topic Editors declare no conflict of interest.
In this Research Topic, we welcome contributions in the form of original research, review, mini review, hypothesis and theory, perspective, that cover, but are not limited to, following themes:
- Mechanistic multiscale and/or AI based modeling of clinical tumors and their response to treatment;
- AI and/or advanced statistical modeling of the resilience of cancer survivors;
- Technologically integrated digital (virtual) twins of specific cancers and their response to treatment and clinical validation;
- Certification and translation of digital (virtual) twins.
Topic Editors declare no conflict of interest.
Keywords: multiscale cancer modeling, artificial intelligence, AI, in silico oncology, in silico psycho-oncology, digital twin, virtual twin cancer
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.
