Digital Twins in Radiology

Pesapane, Filippo; Rotili, Anna; Penco, Silvia; Nicosia, Luca; Cassano, Enrico

doi:10.3390/jcm11216553

Cited by 38 publications

(12 citation statements)

References 13 publications

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“…Moreover, currently unknown correlations between observed phenotypes and genotypes may be discovered through the mappings between imaging data and genomic data, providing possibilities to improve early detection and better management of the disease [ 8 , 21 , 22 , 23 ].…”

Section: The Role Of Artificial Intelligence and Big Data In Radiomicsmentioning

confidence: 99%

“…This is pertinent in the current era of personalised medicine, which relies on early diagnosis of disease, in a patient with specific characteristics, and the subsequent individually tailored treatments, aiming to deliver the right treatment to the right patient at the right time. Accordingly, the ultimate goals of the modern breast imaging are to detect BC as early as possible, then to classify the lesion and predict its clinical course and its biological aggressiveness to optimise treatment in a specific patient [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Pesapane

Marco

Rapino

et al. 2023

JCM

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Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical “how-to” guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.

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Section: The Role Of Artificial Intelligence and Big Data In Radiomicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Pesapane

Marco

Rapino

et al. 2023

JCM

Self Cite

View full text Add to dashboard Cite

show abstract

“…These models, including digital twins, can be continuously updated from multimodal medical data if the characteristics change over time 4 . Digital twins are of interest in the context of evaluating and selecting optimal medical treatments [27] or imaging procedures [29] within clinical practice, and can also be incorporated into other in silico applications [30]. For instance, Wang et al [31] suggested three applications in the areas of medical imaging: optimal selection of scanning techniques (so called 'virtual comparative scanning'), data sharing from in silico scanning of the digital replica to the open source community, and improvement of the regulatory process of image reconstruction algorithms.…”

Section: Personalized Models or Digital Twinsmentioning

confidence: 99%

The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts

Badano,

Lago,

Sizikova

et al. 2023

Prog. Biomed. Eng.

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Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy status and for generating diseased cases and discuss briefly the role of augmentation methods. Finally, we discuss approaches for sampling digital cohorts and understanding the trade-offs and potential for study bias associated with selecting specific patient distributions.

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“…Human digital twins (HDTs) for healthcare are emerging as a cornerstone for personalizing treatments [ 45 , 46 ]. The prevalence of the IoT devices that can connect to user devices to collect, store, process, and notify in real-time through even mobile applications or embedded sensors is becoming a norm.…”

Section: Introductionmentioning

confidence: 99%

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Padmapritha

Srinivasan

Kesavadev

et al. 2023

JCM

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Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

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Digital Twins in Radiology

Cited by 38 publications

References 13 publications

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

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