ReviewNature protocols2021
Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.
Review in Nature protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Characterizing Tumor Cell Movement in Partial Differential Equation Models of Triple-Negative Breast Cancer Receiving Neoadjuvant Therapy.Annals of biomedical engineering · 2026Article
- TumorTwin: a Python framework for patient-specific digital twins in oncology.BMC medical informatics and decision making · 2026Article
- Bio-magnetic nanomedicine for targeted drug delivery of breast cancer: green synthesis, functional design, and translational challenges.Breast cancer research : BCR · 2026Review
- Minibeam radiation therapy remodels tumor microenvironment and suppresses HIF-1α/VEGFR axis to overcome radioresistance in triple-negative breast cancer.NPJ precision oncology · 2025Article
- Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling.International journal of radiation oncology, biology, physics · 2025Article
- Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis.Scientific reports · 2025Article
- Clinically feasible liver tumour cell size measurement through histology-informed in vivo diffusion MRI.Communications medicine · 2025Article
- Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy.bioRxiv : the preprint server for biology · 2025Article
- Employing nullclines to balance treatment efficacy and neurotoxicity for sustained tumor control.Mathematical biosciences · 2025Article
- BMagnetic resonance in medicine · 2025Article
- Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin.NPJ systems biology and applications · 2025Article
- Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology.ArXiv · 2025Article
- MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens.NPJ digital medicine · 2025Article
- Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.Radiology. Artificial intelligence · 2025Article
- Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition.Journal of computational science · 2024Article
- Physics Informed Neural Networks for Estimation of Tissue Properties from Multi-echo Configuration State MRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.NPJ digital medicine · 2024Review
- A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model.Cancer research communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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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.