ReviewNature reviews. Clinical oncology2024
Multiparametric MRI for characterization of the tumour microenvironment.
Review in Nature reviews. Clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06999174 (Multiparametric MRI for Optimized Imaging-based Characterization and Therapy Management of Hepatocellular Carcinoma), which is not on this map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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.
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.
Multiparametric MRI for Optimized Imaging-based Characterization and Therapy Management of Hepatocellular Carcinoma
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Innovative technologies and their clinical prospects for early lung cancer screening.Clinical and experimental medicine · 2025Pooled it
- MR-based techniques for imaging prostate cancer metabolism: current status and future perspectives.Abdominal radiology (New York) · 2026Review
- Intravoxel incoherent motion diffusion-weighted imaging for early response stratification of neoadjuvant immune checkpoint inhibitor plus tyrosine kinase inhibitor in renal cell carcinoma: an exploratory study.Japanese journal of radiology · 2026Article
- Review
- MRI-based perfusion-diffusion habitat analysis for characterizing intratumoral heterogeneity in rectal adenocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.Nature communications · 2026Article
- Biology-informed risk stratification of glioblastoma by integrating MRI-based intratumoral heterogeneity with clinical features: a multicenter validation study.Experimental hematology & oncology · 2026Article
- Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.NPJ breast cancer · 2026Article
- Hybrid membrane-camouflaged photothermal immunomodulatory nanoparticle inhibits colorectal cancer growth and metastasis.International journal of pharmaceutics: X · 2026Article
- Advancing the understanding of tumor microenvironment through medical imaging based on bibliometric analysis.Translational cancer research · 2026Article
- Habitat Analysis for Risk Prediction of Nasopharyngeal Carcinoma: A Comparative Study of Different MRI Sequences and Regional Combinations.Bioengineering (Basel, Switzerland) · 2026Article
- Multiparameter concept-based interpretable model for early breast cancer diagnosis and structured reporting: a multi-center, multi-reader, radiologist-in-the-loop study.BMC medicine · 2026Article
- Evaluating the utility of enhanced T1 mapping MR imaging in assessing depth of myometrial invasion and detecting DNA mismatch repair status in endometrial cancer: a pilot study.Abdominal radiology (New York) · 2026Article
- Spatiotemporal Mapping of Tumor Microenvironment Remodeling During Fiber-Optic Photothermal Therapy: A Multiparametric MRI Study in 4T1 Breast Cancer Xenografts.Chemical & biomedical imaging · 2026Article
- Article
- Diagnostic performance of multiparametric imaging markers in differentiating local recurrence from post-treatment change in head and neck cancer surveillance.Neuroradiology · 2026Article
- Prediction of ISS and R-ISS Stratification in Newly Diagnosed Multiple Myeloma Using Lumbar Spine MRI Radiomics Model: A Two-Center Multimodal Study.Journal of imaging informatics in medicine · 2026Article
- A radiomics-deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.Frontiers in oncology · 2026Article
- MRI Monitored Mn-THPPmPEG12-Guided Phototherapy Elicits Abscopal Immunity and Synergizes with PD-1 Blockade in Triple-Negative Breast Cancer in Mice Model.International journal of nanomedicine · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.
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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.