Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.

Autor:	Stahlberg EA; Cancer Data Science Initiatives, Frederick National Laboratory for Cancer Research, Frederick, MD, United States., Abdel-Rahman M; Department of Ophthalmology and Visual Sciences, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States., Aguilar B; Institute for Systems Biology, Seattle, WA, United States., Asadpoure A; Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, MA, United States., Beckman RA; Innovation Center for Biomedical Informatics, Georgetown University, Washington DC, United States., Borkon LL; Cancer Data Science Initiatives, Frederick National Laboratory for Cancer Research, Frederick, MD, United States., Bryan JN; Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, United States., Cebulla CM; Department of Ophthalmology and Visual Sciences, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States., Chang YH; Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, OR, United States., Chatterjee A; School of Statistics, University of Minnesota, Minneapolis, MN, United States., Deng J; Department of Therapeutic Radiology, Yale University School of Medicine, Yale University, New Haven, CT, United States., Dolatshahi S; Department of Biomedical Engineering, University of Virginia, Charlottesville VA, United States., Gevaert O; Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine and Department of Biomedical Data Science, Stanford University, Stanford, CA, United States., Greenspan EJ; Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States., Hao W; Department of Mathematics, The Pennsylvania State University, University Park, PA, United States., Hernandez-Boussard T; Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine and Department of Biomedical Data Science, Stanford University, Stanford, CA, United States., Jackson PR; Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, Phoenix, AZ, United States., Kuijjer M; Computational Biology and Systems Medicine Group, Centre for Molecular Medicine Norway University of Oslo, Oslo, Norway., Lee A; Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, United States., Macklin P; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States., Madhavan S; Innovation Center for Biomedical Informatics, Georgetown University, Washington DC, United States., McCoy MD; Innovation Center for Biomedical Informatics, Georgetown University, Washington DC, United States., Mohammad Mirzaei N; Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, United States., Razzaghi T; School of Industrial and Systems Engineering, The University of Oklahoma, Norman, OK, United States., Rocha HL; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, United States., Shahriyari L; Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, United States., Shmulevich I; Institute for Systems Biology, Seattle, WA, United States., Stover DG; Division of Medical Oncology and Department of Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, United States., Sun Y; Department of Mathematics, University of South Carolina, Columbia, SC, United States., Syeda-Mahmood T; Almaden Research Center, IBM Research, San Jose, CA, United States., Wang J; Institute for Health Informatics and the Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States., Wang Q; Department of Mathematics, University of South Carolina, Columbia, SC, United States., Zervantonakis I; Department of Bioengineering, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2022 Oct 06; Vol. 4, pp. 1007784. Date of Electronic Publication: 2022 Oct 06 (Print Publication: 2022).
DOI:	10.3389/fdgth.2022.1007784
Abstrakt:	We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community. Competing Interests: Author TS is/was employed by IBM. SM currently employed by Pfizer Worldwide R and D. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (© 2022 Stahlberg, Abdel-Rahman, Aguilar, Asadpoure, Beckman, Borkon, Bryan, Cebulla, Chang, Chatterjee, Deng, Dolatshahi, Gevaert, Greenspan, Hao, Hernandez-Boussard, Jackson, Kuijjer, Lee, Macklin, Madhavan, McCoy, Mohammed Mirzaei, Razzaghi, Rocha, Shahriyari, Shmulevich, Stover, Sun, Syeda-Mahmood, Wang, Wang and Zervantonakis.)
Databáze:	MEDLINE
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