ArticleCancer research2019
Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models.
Article in Cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 2 of them syntheses that pooled it.
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
66 citing papers in PubMed, 2 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- Assessing Treatment Response in Soft Tissue Sarcoma Using Dynamic Contrast-Enhanced MRI: A Systematic Review.Korean journal of radiology · 2026Pooled it
- Visualising spatial heterogeneity in glioblastoma using imaging habitats.Frontiers in oncology · 2022Pooled it
- Sodium accumulation in breast cancer predicts malignancy and treatment response.British journal of cancer · 2022Trial
- Predicting early recurrence of craniopharyngioma using multi-omics radiomic modeling: a retrospective cohort study.Neurosurgical review · 2026Article
- Biphasic CT clustering-based habitat radiomics predicts WHO/ISUP nuclear grade in clear cell renal cell carcinoma.Abdominal radiology (New York) · 2026Article
- Habitat analysis and other AI technologies for Ki-67 prediction in hepatocellular carcinoma: a multi-center study.BMC cancer · 2026Article
- Diffusion weighted imaging-based tumor growth rate for predicting long-term survival in nasopharyngeal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- The prognostic value of intra- and peritumoral habitat analysis in nasopharyngeal carcinoma: combining IVIM and ASL.European radiology · 2026Article
- Predicting head and neck cancer response to radiotherapy using mathematical modeling of MRI-based habitats.NPJ precision oncology · 2026Article
- Mesenteric and antimesenteric border subregionalization using MR enterography: advancing fibrosis evaluation in Crohn disease.European radiology experimental · 2026Article
- An interpretable machine learning model combining MRI-DKI habitat radiomic features and clinical biomarkers for noninvasive prediction of lymphatic metastasis in rectal cancer: a prospective study.Insights into imaging · 2026Article
- Multicenter study provides radiomic and biological insights into neoadjuvant chemotherapy response and prognosis in luminal breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Mri-based habitat and peritumoral radiomics for predicting the proliferative activity of stromal cells in giant cell tumor of bone.Journal of bone oncology · 2026Article
- Pathobiological Dictionary Defining Pathomics and Texture Features: Addressing Understandable AI Issues in Personalized Liver Cancer; Dictionary Version LCP1.0.Journal of imaging informatics in medicine · 2026Article
- Multi-parametric MRI habitat radiomics with interpretable machine learning for early prediction of axillary lymph node metastasis in triple-negative breast cancer.Frontiers in medicine · 2026Article
- Predicting the response of triple negative breast cancer to neoadjuvant systemic therapy via biology-based modeling and habitat analysis.Scientific reports · 2025Article
- MRI-based Intra- and Peritumoral Heterogeneity in Hepatocellular Carcinoma for Microvascular Invasion Prediction and Prognostic Risk Stratification.Radiology. Imaging cancer · 2025Article
- Hypoxia Imaging as a Radiomics Signature in Tumors Using NovelbioRxiv : the preprint server for biology · 2025Article
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6 more citing papers are in PubMed but not listed here.
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 at 2 institutions in 3 countries.
Funding
Abstract
It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. SIGNIFICANCE: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation.
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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.