ArticleEuropean radiology2022
Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer: A Systematic Review and Meta-analysis.AJNR. American journal of neuroradiology · 2022Pooled it
- Non-Contrast Cine Cardiac Magnetic Resonance Derived-Radiomics for the Prediction of Left Ventricular Adverse Remodeling in Patients With ST-Segment Elevation Myocardial Infarction.Korean journal of radiology · 2023Trial
- An interpretable MRI radiomics approach for preoperative prediction of lymphovascular space invasion in cervical cancer using optimal peritumoral region.Frontiers in oncology · 2026Article
- Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer.Cancers · 2025Article
- Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.International journal of legal medicine · 2025Article
- Initial Recurrence Risk Stratification of Papillary Thyroid Cancer based on Intratumoral and Peritumoral Dual Energy CT Radiomics.Current medical imaging · 2025Article
- Multi-parametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in early-stage cervical adenocarcinoma.Frontiers in oncology · 2025Article
- [A predictive model for survival outcomes of glioma patients based on multi-parametric, multi-regional MRI radiomics features and clinical features].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.Insights into imaging · 2024Article
- Integrating a deep neural network and Transformer architecture for the automatic segmentation and survival prediction in cervical cancer.Quantitative imaging in medicine and surgery · 2024Article
- Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer.Journal of imaging informatics in medicine · 2024Article
- Development and validation of aBMC cancer · 2024Article
- MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.Cancer medicine · 2023Article
- What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?Military Medical Research · 2023Review
- The Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review.Life (Basel, Switzerland) · 2023Review
- Reduced field-of-view DWI‑derived clinical-radiomics model for the prediction of stage in cervical cancer.Insights into imaging · 2023Article
- MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.The British journal of radiology · 2022Article
- Article
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6 authors.
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Abstract
objectiveTo conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage squamous cervical cancer (ESSCC).
methodsA total of 191 ESSCC patients (training cohort, n = 135; validation cohort, n = 56) from March 2016 to September 2019 were retrospectively recruited. Radiomics features were derived from the T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CET1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) map for each patient. DFS-related radiomics features were selected in 3 target tumor volumes (VOI
resultsThree radiomics scores all showed favorable performances in DFS prediction. Rad-score (VOI
conclusionMultiparametric MRI-derived radiomics based on multi-scale tumor region can aid in the prediction of DFS for ESSCC patients, thereby facilitating clinical decision-making. KEY POINTS: • Three radiomics scores based on multi-scale tumor region all showed favorable performances in DFS prediction. Rad-score (VOI
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