A Vision for Leveraging the Concept of Digital Twins to Support the Provision of Personalized Cancer Care

Exploring the opportunity for applying digital twins in the healthcare context is an emerging research area that has the potential to support more personalized care. A recognized aspect in cancer care is the need for more personalized treatment planning to complement the recent advances in precision medicine. In this article, we present a classification of digital twins into Grey Box, Surrogate, and Black Box models using systems and mathematical modeling theory. We then explore one possible approach, namely a Black Box classification for incorporating the use of digital twins in the context of personalized uterine cancer care. This article presents one of the first attempts to use digital twins in this capacity and represents an amalgamation of three key domains: clinical, digital health, and computer science, respectively.

Digital Twins—precise, virtual copies of living or nonliving real-world entities—are revolutionizing every industry. While digital twins have seen faster growth in some industries such as manufacturing, other industries especially in the service sector, such as healthcare, have been slow to embrace the possibilities of digital twins. Advances in precision medicine, supported to a great extent by increased computational capabilities and developments in analytics,^, have served to provide significant benefits to clinical care, notably in cancer care. However, to date these advances and applications of genomic analytics have generally failed to provide overall superior, personalized, individualized focus around care delivery for most patients. Moreover, cancer patients differ in terms of their personal preferences regarding quality versus quantity of life and thus how best their ensuing cancer treatment should be planned and structured. To address this key and arguably growing patient concern, as well as bringing into the decision making process important considerations from psychosocial data, patient reported outcome measures (PROMS), in addition to cultural and ethnic considerations, we contend that incorporating aspects of digital twins holds the key in providing superior, precise, yet more personalized cancer care. In this article, we explore the application of the digital twin concept in the context of cancer patients, specifically uterine cancer, and begin to answer the research question: