ReviewDiagnostics (Basel, Switzerland)2021
Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML).
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of radiological prediction of ki 67 proliferation index of meningioma.Neurosurgical review · 2024Pooled it
- Article
- ChatGPT Versus DeepSeek for Breast Cancer Information Retrieval: Quantitative Comparative Study.JMIR cancer · 2026Article
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.International journal of colorectal disease · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- A Dual Nano-Signal Probe-Based Electrochemical Immunosensor for the Simultaneous Detection of Two Biomarkers in Gastric Cancer.Biosensors · 2025Article
- Training machine learning-based spirometry reference equations: a comparison with GAMLSS and GLI reference equations.European clinical respiratory journal · 2025Article
- Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning.Scientific reports · 2024Article
- TBiophysical reports · 2024Article
- A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions.International journal of nanomedicine · 2024Article
- Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2023Review
- A Review of the Recent Advances in Alzheimer's Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics.Diagnostics (Basel, Switzerland) · 2022Review
- Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers.Diagnostics (Basel, Switzerland) · 2022Review
- Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers.Cancers · 2022Review
- Transcriptome-based stemness indices analysis reveals platinum-based chemo-theraputic response indicators in advanced-stage serous ovarian cancer.Bioengineered · 2021Article
- Attention-based Vision Transformer Enables Early Detection of Radiotherapy-Induced Toxicity in Magnetic Resonance Images of a Preclinical Model.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.
Indexed as
Identifiers
What OpenQuestion holds
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.