ArticleCancers2023
Within-Modality Synthesis and Novel Radiomic Evaluation of Brain MRI Scans.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses 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.
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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.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2024Pooled it
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research.Scientific reports · 2024Trial
- A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study.Journal of orthopaedic surgery and research · 2024Trial
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.BMC medical imaging · 2025Article
- Development and validation of a robust MRI-based nomogram incorporating radiomics and deep features for preoperative glioma grading: a multi-center study.Quantitative imaging in medicine and surgery · 2025Article
- Reproducibility of MRI-derived radiomic features in prostate cancer detection: a methodological approach.Polish journal of radiology · 2025Article
- Lightweight CNN for accurate brain tumor detection from MRI with limited training data.Frontiers in medicine · 2025Article
- Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image.Scientific reports · 2024Article
- CT-based surrogate parameters for MRI-based disc height and endplate degeneration in the lumbar spine.BMC medical imaging · 2024Article
- Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.BMC medical imaging · 2024Article
- Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.Radiation oncology (London, England) · 2024Article
- Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma.BMC cancer · 2024Article
- Radiomics and Deep Features: Robust Classification of Brain Hemorrhages and Reproducibility Analysis Using a 3D Autoencoder Neural Network.Bioengineering (Basel, Switzerland) · 2024Article
- Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer.Scientific reports · 2024Article
- Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.BMC cancer · 2024Article
- Diagnostic value of one-stop CT energy spectrum and perfusion for angiogenesis in colon and rectum cancer.BMC medical imaging · 2024Article
- Clinical application of computed tomographic volumetric imaging in postoperative lung function assessment in patients with lung cancer.BMC medical imaging · 2024Article
- Construction of a nomogram for predicting compensated cirrhosis with Wilson's disease based on non-invasive indicators.BMC medical imaging · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC
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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.