ReviewFrontiers in oncology2020
Progress and Future Trends in PET/CT and PET/MRI Molecular Imaging Approaches for Breast Cancer.
Review in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 4 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
58 citing papers in PubMed, 4 syntheses or guidelines pooled it, 121 citations in OpenAlex.
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Joint EANM-SNMMI guideline on the role of 2-[European journal of nuclear medicine and molecular imaging · 2024Guideline
- [Frontiers in medicine · 2024Pooled it
- Diagnostic performance of whole-body [European radiology · 2024Pooled it
- Clinicopathological and molecular predictors of [European journal of nuclear medicine and molecular imaging · 2024Trial
- The proportion, clinical predictors, and prognostic impact of hypometabolic estrogen receptor-positive primary breast cancer on baseline [ 18 F] fluorodeoxyglucose PET.Nuclear medicine communications · 2026Article
- Design and first experimental results of an edgeless PET prototype for breast imaging: DeepBreast.EJNMMI physics · 2026Article
- The gastrin-releasing peptide receptor in oncology: a comprehensive review of recent advances in targeted radionuclide theranostics.Frontiers in oncology · 2026Review
- Advances in nanotechnology for early cancer diagnosis: emerging nanoplatforms and multimodal imaging approaches.American journal of cancer research · 2026Review
- Advancements in Targeted Radiopharmaceuticals: Innovations in Diagnosis and Therapy for Enhanced Cancer Management.Chembiochem : a European journal of chemical biology · 2026Review
- Gene expression profiling and predictive modeling of cancer biomarkers using machine learning and IoT-Enabled biosensors.Scientific reports · 2025Article
- Comparison of Magnetic Resonance Spectra Acquired With Hybrid PET/MR and Standalone MR Scanners.Journal of magnetic resonance imaging : JMRI · 2025Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025Review
- Inflammatory microenvironment-responsive MR imaging contrast agents.Nanomedicine (London, England) · 2025Review
- Review
- Article
- Personalized mutation tracking in circulating-tumor DNA predicts recurrence in patients with high-risk early breast cancer.NPJ breast cancer · 2025Article
- Robust whole-body PET image denoising using 3D diffusion models: evaluation across various scanners, tracers, and dose levels.European journal of nuclear medicine and molecular imaging · 2025Article
- Targeting breast cancer: the promise of phage-based nanomedicines.Breast cancer research and treatment · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a major disease with high morbidity and mortality in women worldwide. Increased use of imaging biomarkers has been shown to add more information with clinical utility in the detection and evaluation of breast cancer. To date, numerous studies related to PET-based imaging in breast cancer have been published. Here, we review available studies on the clinical utility of different PET-based molecular imaging methods in breast cancer diagnosis, staging, distant-metastasis detection, therapeutic and prognostic prediction, and evaluation of therapeutic responses. For primary breast cancer, PET/MRI performed similarly to MRI but better than PET/CT. PET/CT and PET/MRI both have higher sensitivity than MRI in the detection of axillary and extra-axillary nodal metastases. For distant metastases, PET/CT has better performance in the detection of lung metastasis, while PET/MRI performs better in the liver and bone. Additionally, PET/CT is superior in terms of monitoring local recurrence. The progress in novel radiotracers and PET radiomics presents opportunities to reclassify tumors by combining their fine anatomical features with molecular characteristics and develop a beneficial pathway from bench to bedside to predict the treatment response and prognosis of breast cancer. However, further investigation is still needed before application of these modalities in clinical practice. In conclusion, PET-based imaging is not suitable for early-stage breast cancer, but it adds value in identifying regional nodal disease and distant metastases as an adjuvant to standard diagnostic imaging. Recent advances in imaging techniques would further widen the comprehensive and convergent applications of PET approaches in the clinical management of breast cancer.
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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.