ReviewMagnetic resonance imaging2019
Precision diagnostics based on machine learning-derived imaging signatures.
Review in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes.JAMA network open · 2023Trial
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders.Nature biomedical engineering · 2025Article
- Towards precision MRI biomarkers in epilepsy with normative modelling.Brain : a journal of neurology · 2025Review
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model.European radiology · 2023Article
- Review
- The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.Scientific data · 2022Article
- Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses.Frontiers in neurology · 2022Review
- Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.Cancers · 2021Article
- Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas.Neurosurgery · 2021Article
- Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples.Current opinion in neurology · 2021Review
- Multiple Subtypes of Alzheimer's Disease Base on Brain Atrophy Pattern.Brain sciences · 2021Article
- Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.IEEE transactions on bio-medical engineering · 2020Article
- Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.Annual review of biomedical engineering · 2020Review
- AI in MRI: A case for grassroots deep learning.Magnetic resonance imaging · 2019Article
- Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.EBioMedicine · 2019Article
- Multi-Disease Segmentation of Gliomas and White Matter Hyperintensities in the BraTS Data Using a 3D Convolutional Neural Network.Frontiers in computational neuroscience · 2019Article
- Radiomics and radiogenomics in pediatric neuro-oncology: A review.Neuro-oncology advancesReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.
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.