Agent-based modeling reveals impacts of cell adhesion and matrix remodeling on cancer collective cell migration phenotypes DOI Open Access

Temitope O. Benson,

Mohammad Aminul Islam,

K.C. Liu

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 23, 2024

Abstract Understanding the phenotypic transitions of cancer cells is crucial for elucidating tumor progression mechanisms, particularly transition from a non-invasive spheroid phenotype to an invasive network phenotype. We developed agent-based model (ABM) using Compucell3D, open-source biological simulation software, investigate how varying biophysical and biochemical parameters influence emerging properties cellular communities, including cell growth, division, migration. Our focus was on cell-cell contact adhesion matrix remodeling effects simplified enzymatic extracellular subsequent enhancements chemotaxis or durotaxis as combined effect localized secretion chemoattractant. By chemoattractant rate energy, we simulated their behavior driving The serves digital twin 3D culture, simulating invasion over 1 week, validated against published data. simulations track emergent morphological collective changes key metrics such circularity invasion. findings indicate that increased enhances invasiveness cells, promoting Additionally, changing energy strong weak affects compactness spheroids, resulting in lower work advances understanding by providing insights into mechanisms behind transitions.

Language: Английский

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation DOI Creative Commons
Nicolò Cogno, Cristian Axenie, Roman Bauer

et al.

Cancer Biology & Therapy, Journal Year: 2024, Volume and Issue: 25(1)

Published: April 28, 2024

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial temporal scales, but also integrate information from well-established

Language: Английский

Citations

12
From shallow to deep: the evolution of machine learning and mechanistic model integration in cancer research DOI Creative Commons

Lan Yunduo,

Sung‐Young Shin, Lan K. Nguyen

et al.

Current Opinion in Systems Biology, Journal Year: 2025, Volume and Issue: unknown, P. 100541 - 100541

Published: Feb. 1, 2025

Language: Английский

Citations

2
From virtual to reality: innovative practices of digital twins in tumor therapy DOI Creative Commons
Shiying Shen, Wenhao Qi, Xin Liu

et al.

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: March 19, 2025

As global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for treatment. This study aims to systematically analyze the current applications, research trends, challenges of tumor therapy, while exploring future directions. Relevant literature up 2024 was retrieved from PubMed, Web Science, other databases. Data visualization performed using R VOSviewer software. The analysis includes initiation funding models, distribution, sample size analysis, data processing artificial intelligence applications. Furthermore, investigates specific applications effectiveness diagnosis, treatment decision-making, prognosis prediction, personalized management. Since 2020, on oncology has surged, with significant contributions United States, Germany, Switzerland, China. Funding primarily comes government agencies, particularly National Institutes Health U.S. Sample reveals that large-sample studies have greater clinical reliability, small-sample emphasize validation. In integration medical imaging, multi-omics data, AI algorithms is key. By combining multimodal dynamic modeling, accuracy models been significantly improved. However, different types still faces related tool interoperability limited standardization. Specific shown advantages surgical planning. Digital holds substantial promise therapy by optimizing plans through integrated modeling. factors such as language restrictions, potential selection bias, relatively small number published this emerging field, which may affect comprehensiveness generalizability our findings. Moreover, issues heterogeneity, technical integration, privacy ethics continue impede its broader application. Future should promote international collaboration, establish unified interdisciplinary standards, strengthen ethical regulations accelerate translation

Language: Английский

Citations

2
Virtual cells for predictive immunotherapy DOI
Daniel Bergman, Elana J. Fertig

Nature Biotechnology, Journal Year: 2025, Volume and Issue: 43(4), P. 464 - 465

Published: April 1, 2025

Language: Английский

Citations

1
Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer DOI
Constantinos Harkos, Andreas G. Hadjigeorgiou, Chrysovalantis Voutouri

et al.

Nature reviews. Cancer, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Language: Английский

Citations

1
Synergistic Integration of Digital Twins and Neural Networks for Advancing Optimization in the Construction Industry: A Comprehensive Review DOI
Alexey Borovkov, Khristina Maksudovna Vafaeva, Nikolai Vatin

et al.

Construction Materials and Products, Journal Year: 2024, Volume and Issue: 7(4), P. 7 - 7

Published: Aug. 9, 2024

The object of research is the potential application digital twins and neural network modeling for optimizing construction processes. Method. Adopting a perspective approach, conducts an extensive review existing literature delineates theoretical framework integrating technologies. Insights from inform development methodologies, while case studies practical applications are explored to deepen understanding these integrated approaches system optimization. Results. yields following key findings: Digital Twins: Offer capability create high-fidelity virtual representations physical systems, enabling real-time data collection, analysis, visualization throughout project lifecycle. This allows proactive decision-making, improved constructability enhanced coordination between design field operations. Neural Network Modeling: Possesses power learn complex relationships vast datasets, predictive optimization behavior. networks can be employed forecast timelines, identify risks, optimize scheduling resource allocation. Integration Twins Networks: Presents transformative avenue processes by facilitating data-driven design, maintenance equipment infrastructure, performance monitoring. synergistic approach lead significant improvements in efficiency, reduced costs, overall quality.

Language: Английский

Citations

5
Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities. DOI Creative Commons
Tyler Meadows, Erik R. Coats, Solana Narum

et al.

Water Research, Journal Year: 2024, Volume and Issue: 268, P. 122671 - 122671

Published: Oct. 20, 2024

Language: Английский

Citations

3
Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications DOI Creative Commons
Miguel Rujas,

Rodrigo Martín Gómez del Moral Herranz,

Giuseppe Fico

et al.

International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 195, P. 105763 - 105763

Published: Dec. 17, 2024

Language: Английский

Citations

3
Deep Learning Approaches for the Classification of Keloid Images in the Context of Malignant and Benign Skin Disorders DOI Creative Commons

O. E. Adebayo,

B. Chatelain, Dumitru Trucu

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 710 - 710

Published: March 12, 2025

Background/Objectives: Misdiagnosing skin disorders leads to the administration of wrong treatments, sometimes with life-impacting consequences. Deep learning algorithms are becoming more and used for diagnosis. While many cancer/lesion image classification studies focus on datasets containing dermatoscopic images do not include keloid images, in this study, we diagnosing amongst other lesions combine two publicly available non-dermatoscopic images: one dataset various benign malignant (melanoma, basal cell carcinoma, squamous actinic keratosis, seborrheic nevus). Methods: Different Convolution Neural Network (CNN) models classify these as either or benign, differentiate keloids different disorders, furthermore among similar-looking lesions. To end, use transfer technique applied nine base models: VGG16, MobileNet, InceptionV3, DenseNet121, EfficientNetB0, Xception, InceptionRNV2, EfficientNetV2L, NASNetLarge. We explore compare results using performance metrics such accuracy, precision, recall, F1score, AUC-ROC. Results: show that VGG16 model (after fine-tuning) performs best classifying lesion following class performance: an accuracy 0.985, precision 1.0, recall 0.857, F1 score 0.922 AUC-ROC value 0.996. also has overall average (over all classes) terms metrics. Using model, further attempt predict identification three new anonymised clinical them malignant, keloid, process, identify some issues related collection processing images. Finally, DenseNet121 when differentiating from have similar presentations. Conclusions: The study emphasised potential deep (and their drawbacks), keloids, which usually investigated via approaches (as opposed cancers), mainly due lack data.

Language: Английский

Citations

0
A critical assessment of artificial intelligence in magnetic resonance imaging of cancer DOI Creative Commons
Chengyue Wu, Meryem Abbad Andaloussi, David A. Hormuth

et al.

npj Imaging, Journal Year: 2025, Volume and Issue: 3(1)

Published: April 9, 2025

Given the enormous output and pace of development artificial intelligence (AI) methods in medical imaging, it can be challenging to identify true success stories determine state-of-the-art field. This report seeks provide magnetic resonance imaging (MRI) community with an initial guide into major areas which AI are contributing MRI oncology. After a general introduction intelligence, we proceed discuss successes current limitations when used for image acquisition, reconstruction, registration, segmentation, as well its utility assisting diagnostic prognostic settings. Within each section, attempt present balanced summary by first presenting common techniques, state readiness, clinical needs, barriers practical deployment setting. We conclude new advances must realized address questions regarding generalizability, quality assurance control, uncertainty quantification applying cancer maintain patient safety utility.

Language: Английский

Citations

0