ArticleComputer methods in applied mechanics and engineering2017
Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.
Article in Computer methods in applied mechanics and engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 61 citations in OpenAlex.
- Computational reactive-diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib.Scientific reports · 2023Trial
- Volumetric CT Assessment of In Situ Induced Hepatic Lesions in a Transgenic Swine Model.Life (Basel, Switzerland) · 2024Article
- A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data.Engineering with computers · 2024Article
- Image-Based Polygonal Lattices for Mechanical Modeling of Biological Materials: 2D Demonstrations.ACS biomaterials science & engineering · 2023Article
- Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling.Clinical biomechanics (Bristol, Avon) · 2023Article
- Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform.Breast cancer research and treatment · 2022Article
- Dynamic characterization of breast cancer response to neoadjuvant therapy using biophysical metrics of spatial proliferation.Scientific reports · 2022Article
- Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.Frontiers in oncology · 2022Article
- Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging.IEEE transactions on bio-medical engineering · 2021Article
- Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.Nature protocols · 2021Review
- Atorvastatin-mediated rescue of cancer-related cognitive changes in combined anticancer therapies.PLoS computational biology · 2021Article
- Mathematical model of STAT signalling pathways in cancer development and optimal control approaches.Royal Society open science · 2021Article
- Optimal solution of the fractional order breast cancer competition model.Scientific reports · 2021Article
- Towards integration ofScientific reports · 2020Article
- A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database.Magnetic resonance imaging · 2020Article
- Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.Annual review of biomedical engineering · 2020Review
- A Coupled Mass Transport and Deformation Theory of Multi-constituent Tumor Growth.Journal of the mechanics and physics of solids · 2020Article
- Accounting for intraoperative brain shift ascribable to cavity collapse during intracranial tumor resection.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.Breast (Edinburgh, Scotland) · 2020Review
- Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.Journal of digital imaging · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor burden following neoadjuvant therapy in breast cancer that uses a biophysical mathematical model combining reaction-diffusion growth/therapy dynamics and biomechanical effects driven by early time point imaging data. Whereas early work had been based on a limited dimensionality reduction (two-dimensional planar modeling analysis) to simplify the numerical implementation, in this work, we extend our framework to a fully volumetric, three-dimensional biophysical mathematical modeling approach in which parameter estimates are generated by an inverse problem based on the adjoint state method for numerical efficiency. In an
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.