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Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data
Despite the remarkable advances in cancer diagnosis, treatment, and management over the
past decade, malignant tumors remain a major public health problem. Further progress in …
past decade, malignant tumors remain a major public health problem. Further progress in …
Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology
Digital twins employ mathematical and computational models to virtually represent a
physical object (eg, planes and human organs), predict the behavior of the object, and …
physical object (eg, planes and human organs), predict the behavior of the object, and …
Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas
We develop a methodology to create data-driven predictive digital twins for optimal risk-
aware clinical decision-making. We illustrate the methodology as an enabler for an …
aware clinical decision-making. We illustrate the methodology as an enabler for an …
Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when
combined with radiation therapy, may improve patient outcomes and reduce the …
combined with radiation therapy, may improve patient outcomes and reduce the …
[HTML][HTML] Biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data
Simple Summary The recruitment of new vasculature via angiogenesis is a critical
component of tumor development, which fundamentally influences tumor growth and …
component of tumor development, which fundamentally influences tumor growth and …
TGM-Nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling
Prediction and uncertainty quantification of tumor progression are vital in clinical practice, ie,
disease prognosis and decision-making on treatment strategies. In this work, we propose …
disease prognosis and decision-making on treatment strategies. In this work, we propose …
Designing clinical trials for patients who are not average
The heterogeneity inherent in cancer means that even a successful clinical trial merely
results in a therapeutic regimen that achieves, on average, a positive result only in a subset …
results in a therapeutic regimen that achieves, on average, a positive result only in a subset …
A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth
Forecasting tumor progression and assessing the uncertainty of predictions play a crucial
role in clinical settings, especially for determining disease outlook and making informed …
role in clinical settings, especially for determining disease outlook and making informed …
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could
significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing …
significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing …
Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks
Blood coagulation, which involves a group of complex biochemical reactions, is a crucial
step in hemostasis to stop bleeding at the injury site of a blood vessel. Coagulation …
step in hemostasis to stop bleeding at the injury site of a blood vessel. Coagulation …