ArticleEuropean journal of nuclear medicine and molecular imaging2022
AI-enhanced simultaneous multiparametric
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06370234 (The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.), which is not on this map. Cited by 30 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.
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.
The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.
Who cites it
30 citing papers in PubMed.
- 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
- Multimodal artificial intelligence in breast cancer: towards integrated prediction and personalized care.European radiology · 2026Article
- Uptake Patterns of [Cancers · 2026Article
- Radiomics-based Machine Learning Models for Classifying Breast Cancer on Dynamic-Contrast Enhanced MRI through Multi-Observer AnalysisCurrent medical imaging · 2026Article
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- Clinician-driven automated data preprocessing in nuclear medicine AI environments.European journal of nuclear medicine and molecular imaging · 2025Article
- Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.Scientific reports · 2025Article
- Article
- Performance of low- and high-temporal-resolution DCE-MRI texture analysis in distinguishing breast lesions from background enhancement.American journal of translational research · 2025Article
- PET Molecular Imaging in Breast Cancer: Current Applications and Future Perspectives.Journal of clinical medicine · 2024Review
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- PET/MRI and Novel Targets for Breast Cancer.Biomedicines · 2024Review
- Article
- Application of Machine Learning Analyses Using Clinical and [Molecular imaging and biology · 2023Article
- AI-Enhanced PET and MR Imaging for Patients with Breast Cancer.PET clinics · 2023Review
- Prospective validation of 18F-Fluoroethylcholine as a tracer in PET/MRI for the evaluation of breast lesions and prediction of lymph node status.La Radiologia medica · 2023Article
- Breast PET/MRI Hybrid Imaging and Targeted Tracers.Journal of magnetic resonance imaging : JMRI · 2023Review
- Role of LGMN in tumor development and its progression and connection with the tumor microenvironment.Frontiers in molecular biosciences · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeTo assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric
methodsA population of 102 patients with 120 breast lesions (101 malignant and 19 benign) detected on ultrasound and/or mammography was prospectively enrolled. All patients underwent hybrid
resultsEight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983), although its accuracy was not significantly higher than that of expert reader review (AUC 0.868) (p = 0.508).
conclusionA radiomics and ML model combining quantitative parameters and radiomics features extracted from simultaneous multiparametric
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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.