ArticleCancers2020
Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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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.
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Who cites it
35 citing papers in PubMed, 51 citations in OpenAlex.
- A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.BioMed research international · 2022Trial
- Predicting head and neck cancer response to radiotherapy using mathematical modeling of MRI-based habitats.NPJ precision oncology · 2026Article
- Development and Validation of an Explainable MRI-Based Habitat Radiomics Model for Predicting p53-Abnormal Endometrial Cancer: A Multicentre Feasibility Study.Journal of imaging informatics in medicine · 2026Article
- MRI-Based Habitat Radiomics for Differentiating Early-Stage Endometrial Carcinoma from Submucous Leiomyoma: A Multicenter Validation Study.Journal of Cancer · 2026Article
- Fractal analysis of tumor subregions differentiates benign from malignant breast lesions.Polish journal of radiology · 2026Article
- A study on the prognostic assessment of triple-negative breast cancer using habitat analysis of preoperative DCE-MRI subtraction maps.Frontiers in oncology · 2026Article
- Minibeam radiation therapy remodels tumor microenvironment and suppresses HIF-1α/VEGFR axis to overcome radioresistance in triple-negative breast cancer.NPJ precision oncology · 2025Article
- Tumor shrinkage patterns and optimal timing of response assessment during neoadjuvant therapy for breast cancer: a study based on multiparametric MRI.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis.Quantitative imaging in medicine and surgery · 2025Article
- Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis.Scientific reports · 2025Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- Review
- Development and application of a novel tumor habitat analysis technique based on dynamical modeling.Medical physics · 2025Article
- Article
- Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors.NMR in biomedicine · 2025Article
- Attention mechanism-based habitat analysis for predicting pleural invasion and prognosis of pulmonary nodules.Translational lung cancer research · 2025Article
- Study on heterogeneity of vascularity and cellularity via multiparametric MRI habitat imaging in breast cancer.BMC medical imaging · 2025Article
- A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.Cancer biology & therapy · 2024Article
- Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: a retrospective study.Quantitative imaging in medicine and surgery · 2024Article
- MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (
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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.