Observational studyJournal of magnetic resonance imaging : JMRI2018
Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy.
Observational study in Journal of magnetic resonance imaging : JMRI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
63 citing papers in PubMed, 116 citations in OpenAlex.
- Pretreatment Staging FDG PET-Derived Radiomic Score Predicts Progression-Free Survival in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy.Diagnostics (Basel, Switzerland) · 2026Article
- Development of multiphasic CT-based delta-radiomics model for predicting postoperative recurrence risk in bladder cancer.BMC medical imaging · 2026Article
- Construction and validation of a novel prognostic risk model in cervical cancer: integrating lactylation-related genes linked to radiotherapy‑associated transcriptional changes.Discover oncology · 2026Article
- Which is the best timing to assess response after chemoradiation in locally advanced cervical cancer (BRILACC)?Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Article
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- Alpha and Beta Emitters in Translational Nuclear Medicine: Clinical Advances, Challenges, and Future Direction.International journal of molecular sciences · 2026Review
- Multimodality and temporal analysis of cervical cancer treatment response.Quantitative imaging in medicine and surgery · 2026Article
- Graph neural network model using radiomics for lung CT image segmentation.Scientific reports · 2025Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- DCE-MRI quantitative analysis and MRI-based radiomics for predicting the early efficacy of microwave ablation in lung cancers.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- MRI delta radiomics during chemoradiotherapy for prognostication in locally advanced cervical cancer.BMC cancer · 2025Article
- Article
- Pre-treatment T2-weighted magnetic resonance radiomics for prediction of loco-regional recurrence after image-guided adaptive brachytherapy for locally advanced cervical cancer.Journal of contemporary brachytherapy · 2024Article
- New Perspectives for Estimating Body Composition From Computed Tomography: Clothing Associated Artifacts.Academic radiology · 2024Article
- Equivalent uniform aerobic dose in radiotherapy for hypoxic tumors.Physics in medicine and biology · 2024Article
- Current Status and Future Directions of Image-Guided Adaptive Brachytherapy for Locally Advanced Cervical Cancer.Cancers · 2024Review
- Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network andInternational journal of molecular sciences · 2024Article
- Magnetic Resonance Image Radiomic Reproducibility: The Impact of Preprocessing on Extracted Features from Gross and High-Risk Clinical Tumor Volumes in Cervical Cancer Patients before Brachytherapy.Journal of medical signals and sensors · 2024Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance.Molecular imaging and biology · 2023Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundRobust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy. PURPOSE: To conduct a technical exploration of longitudinal changes in tumor heterogeneity patterns on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI) and FDG positron emission tomography / computed tomography (PET/CT), and their association to radiation therapy (RT) response in cervical cancer. STUDY TYPE: Prospective observational study with longitudinal MRI and PET/CT pre-RT, early-RT (2 weeks), and mid-RT (5 weeks). POPULATION: Twenty-one FIGO IB FIELD STRENGTH/SEQUENCE: 1.5T, precontrast axial T ASSESSMENT: Response assessment 1 month after completion of treatment by a board-certified radiation oncologist from manually delineated tumor volume changes. STATISTICAL TESTS: Intensity histogram (IH) quantiles (DCE SI
resultsTumor IH means and quantiles varied significantly during RT (SUV DATA
conclusionPreliminary findings show tumor heterogeneity was variable between patients, modalities, and timepoints. Radiomic assessment of changing tumor heterogeneity has the potential to personalize treatment and power outcome prediction. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1388-1396.
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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.