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Digital twins: a new paradigm in oncology in the era of big data

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ESMO Real World Data and Digital Oncology

ESMO Real World Data and Digital Oncology 2024年 5卷 100056-100056页

作者： Mollica, L. Leli, C. Sottotetti, F. Quaglini, S. Locati, L.D. Marceglia, S. Medical Oncology Unit Istituti Clinici Scientifici Maugeri IRCCS Pavia Italy Department of Internal Medicine and Medical Therapy University of Pavia Pavia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Engineering and Architecture University of Trieste Trieste Italy

Recent advancements in health care digitalization opened the collection and availability of big data, whose analysis requires artificial intelligence-based technologies to facilitate the development of predictive tools supporting decision making in clinical practice. In this context, the idea of constructing ‘digital worlds’ to evaluate the performance of such novel tools becomes more attractive. Digital twins (DTs) are ‘digital objects’ characterized by a bi-directional interaction with their ‘real-world counterparts’. DTs aim to enhance predictions further by leveraging both the predictive capabilities of digital simulations and the continuous updating of real-life data—ideally incorporating clinical records, multiomics data, and patient-reported outcomes. DTs can potentially integrate these diverse data into virtual models applicable across pre-clinical to clinical studies. Running simulations in silico on cancer cells or cancer patients’ DTs can provide valuable insights into cancer biology, clinical practice, and health care education, with the added value of reducing costs and overcoming many common limitations of current studies (limited number of variables, challenges in recruiting patients with rare tumors, lack of real-life feedback). Despite their significant potential, DTs are still in their infancy, facing numerous unsolved technical and ethical challenges that hinder their application in clinical practice.

关键词： digital twins clinical oncology big data artificial intelligence cancer patient

Aerogel from sustainably grown bacterial cellulose pellicle as thermally insulative film for building envelope

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arXiv 2022年

作者： Fleury, Blaise Abraham, Eldho De La Cruz, Joshua A. Chandrasekar, Varun S. Senyuk, Bohdan Liu, Qingkun Cherpak, Vladyslav Park, Sungoh ten Hove, Jan Bart Smalyukh, Ivan I. Department of Physics University of Colorado Boulder CO80309 United States Materials Science and Engineering Program University of Colorado Boulder CO80309 United States Soft Materials Research Center and Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder CO80303 United States

Improving building energy performance requires the development of new highly insulative materials. An affordable retrofitting solution comprising a thin film could improve the resistance to heat flow in both residential and commercial buildings and reduce overall energy consumption. Here we propose cellulose aerogel films formed from pellicles produced by the bacteria Gluconacetobacter hansenii as insulation materials. We studied the impact of density and nanostructure on the aerogels' thermal properties. Thermal conductivity as low as 13 mW/(K·m) was measured for native pellicle-based aerogels dried as-is with minimal post-treatment. The use of waste from the beer brewing industry as a solution to grow the pellicle maintained the cellulose yield obtained with standard Hestrin-Schramm medium, making our product more affordable and sustainable. In the future, our work can be extended through further diversification of the sources of substrate among food wastes, facilitating larger potential production and applications. © 2022, CC BY-NC-ND.

关键词： Aerogels

Automating the Practice of Science – Opportunities, Challenges, and Implications

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Ramsey envelope modulation in NV diamond magnetometry

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Physical Review B 2022年第5期106卷 054110-054110页

作者： Jner Tzern Oon Jiashen Tang Connor A. Hart Kevin S. Olsson Matthew J. Turner Jennifer M. Schloss Ronald L. Walsworth Department of Physics University of Maryland College Park Maryland 20742 USA Quantum Technology Center University of Maryland College Park Maryland 20742 USA Department of Electrical Engineering and Computer Science University of Maryland College Park Maryland 20742 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts 02421 USA

Nitrogen-vacancy (NV) spin ensembles in diamond provide an advanced magnetic sensing platform, with applications in both the physical and life sciences. The development of isotopically engineered NV15 diamond offers advantages over naturally occurring NV14 for magnetometry, due to its simpler hyperfine structure. However, for sensing modalities requiring a bias magnetic field not aligned with the sensing NV axis, the absence of a quadrupole moment in the N15 nuclear spin leads to pronounced envelope modulation effects in time-dependent measurements of NV15 spin evolution. While such behavior in spin echo experiments are well studied, analogous effects in Ramsey measurements and the implications for magnetometry remain underexplored. Here, we derive the modulated NV15 Ramsey response to a misaligned bias field, using a simple vector description of the effective magnetic field on the nuclear spin. The predicted modulation properties are then compared to experimental results, revealing significant magnetic sensitivity loss if unaddressed. We demonstrate that double-quantum coherences of the NV S=1 electronic spin states dramatically suppress these envelope modulations, while additionally proving resilient to other parasitic effects such as strain heterogeneity and temperature shifts.

关键词： NV centers Quantum metrology Quantum sensing Nitrogen vacancy centers in diamond Magnetization measurements Numerical approximation & analysis Perturbation theory

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

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arXiv 2022年

作者： Li, Lei Wu, Fuping Wang, Sihan Luo, Xinzhe Martín-Isla, Carlos Zhai, Shuwei Zhang, Jianpeng Liu, Yanfei Zhang, Zhen Ankenbrand, Markus J. Jiang, Haochuan Zhang, Xiaoran Wang, Linhong Arega, Tewodros Weldebirhan Altunok, Elif Zhao, Zhou Li, Feiyan Ma, Jun Yang, Xiaoping Puybareau, Elodie Oksuz, Ilkay Bricq, Stephanie Li, Weisheng Punithakumar, Kumaradevan Tsaftaris, Sotirios A. Schreiber, Laura M. Yang, Mingjing Liu, Guocai Xia, Yong Wang, Guotai Escalera, Sergio Zhuang, Xiahai School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Departament de Matemàtiques & Informàtica Universitat de Barcelona Barcelona Spain Computer Vision Center Universitat Autònoma de Barcelona Spain School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China College of Physics and Information Engineering Fuzhou University Fuzhou China Comprehensive Heart Failure Center Wuerzburg University Hospitals Wuerzburg Germany School of Engineering University of Edinburgh Edinburgh United Kingdom School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecomm-Unications Chongqing China ImViA Laboratory Université Bourgogne Franche-Comté Dijon France Computer Engineering Department Istanbul Technical University Istanbul Turkey Le Kremlin-Bicêtre France Department of Mathematics Nanjing University of Science and Technology Nanjing China Department of Mathematics Nanjing University Nanjing China

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (***/zhuangxiahai/0/myops20/). © 2022, CC BY-NC-SA.

关键词： Magnetic resonance imaging

Estimating Potential Outcome Distributions with Collaborating Causal Networks

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arXiv 2021年

作者： Zhou, Tianhui Carson, William E. Carlson, David Department of Biostatistics and Bioinformatics Duke University DurhamNC27705 United States Department of Biomedical Engineering Duke University DurhamNC27705 United States Department of Civil and Environmental Engineering Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27705 United States Department of Electrical and Computer Engineering Duke University DurhamNC27705 United States

Traditional causal inference approaches leverage observational study data to estimate the difference in observed (factual) and unobserved (counterfactual) outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or overly-simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE alone by learning the full potential outcome distributions. Estimation of outcome distributions via the CCN framework does not require restrictive assumptions of the underlying data generating process (e.g. Gaussian errors). Additionally, our proposed method facilitates estimation of the utility of each possible treatment and permits individual-specific variation through utility functions (e.g. risk tolerance variability). CCN not only extends outcome estimation beyond traditional risk difference, but also enables a more comprehensive decision making process through definition of flexible comparisons. Under assumptions commonly made in the causal inference literature, we show that CCN learns distributions that asymptotically capture the correct potential outcome distributions. Furthermore, we propose an adjustment approach that is empirically effective in alleviating sample imbalance between treatment groups in observational studies. Finally, we evaluate the performance of CCN in multiple experiments on both synthetic and semi-synthetic data. We demonstrate that CCN learns improved distribution estimates compared to existing Bayesian and deep generative methods as well as improved de

关键词： Risk perception

Cellulose-Based Reflective Liquid Crystal Films as Optical Filters and Solar Gain Regulators

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arXiv 2022年

作者： De La Cruz, Joshua A. Liu, Qingkun Senyuk, Bohdan Frazier, Allister W. Peddireddy, Karthik Smalyukh, Ivan I. Materials Science and Engineering Program University of Colorado Boulder CO80309 United States Department of Physics University of Colorado Boulder CO80309 United States Soft Materials Research Center Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder CO80303 United States

Many promising approaches for designing interactions of synthetic materials with light involve solid optical monocrystals and nanofabricated photonic crystal structures with spatially periodic variations of refractive index. Although their high costs limit current technological applications, remarkably, such photonic and optically anisotropic materials have also evolved throughout nature and enable narrow or broad-band spectral reflection of light. Here we use self-assembly of biomaterial cellulose nanocrystals to obtain three-layer films with helicoidal and nematic-like organization of the cellulose nanoparticles, which mimics naturally occurring polarization-insensitive reflectors found in the wings of Plusiotis resplendens beetles. These films were characterized with polarized optical microscopy and circular dichroism spectrometry, as well as scanning and transmission electron microscopies. These films exhibit high reflectivity tunable within the visible and near-infrared regions of the optical spectrum and may find applications ranging from color filters to smart cloth designs and in solar-gain-regulating building technologies. © 2022, CC BY-NC-ND.

关键词： Reflection

Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias

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The journal of prevention of Alzheimer's disease 2025年 100169页

作者： Sonia Akter Zhandi Liu Eduardo J Simoes Praveen Rao Institute for Data Science and Informatics University of Missouri USA. Department of Electrical Engineering and Computer Science University of Missouri USA. Department of Biomedical Informatics Biostatics and Medical Epidemiology University of Missouri USA. Institute for Data Science and Informatics University of Missouri USA Department of Electrical Engineering and Computer Science University of Missouri USA. Electronic address: praveen.rao@missouri.edu.

BACKGROUND:Over 6 million patients in the United States are affected by Alzheimer's Disease and Related Dementias (ADRD). Early detection of ADRD can significantly improve patient outcomes through timely treatment. OBJECTIVE:To develop and validate machine learning (ML) models for early ADRD diagnosis and prediction using de-identified EHR data from the University of Missouri (MU) Healthcare. DESIGN:Retrospective case-control study. SETTING:The study used de-identified EHR data provided by the MU NextGen biomedical Informatics, modeled with the PCORnet Common Data Model (CDM). PARTICIPANTS:An initial cohort of 380,269 patients aged 40 or older with at least two healthcare encounters was narrowed to a final dataset of 4,012 ADRD cases and 119,723 controls. METHODS:Six ML classifier models: Gradient-Boosted Trees (GBT), Light Gradient-Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient-Boosting (XGBoost), Logistic Regression (LR), and Adaptive Boosting (AdaBoost) were evaluated using Area Under the Receiver Operating Characteristic Curve (AUC-ROC), accuracy, sensitivity, specificity, and F1 score. SHAP (SHapley Additive exPlanations) analysis was applied to interpret predictions. RESULTS:The GBT model achieved the best AUC-ROC scores of 0.809-0.833 across 1- to 5-year prediction windows. SHAP analysis identified depressive disorder, age groups 80-90 yrs and 70-80 yrs, heart disease, anxiety, and the novel risk factors of sleep apnea, and headache. CONCLUSION:This study underscores the potential of ML models for leveraging EHR data to enable early ADRD prediction, supporting timely interventions, and improving patient outcomes. By identifying both established and novel risk factors, these findings offer new opportunities for personalized screening and management strategies, advancing both clinical and informatics science.

关键词： Alzheimer's disease Dementias Early prediction Electronic health record data Machine learning (ML)

MONAI: An open-source framework for deep learning in healthcare

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arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research