ArticleGynecologic oncology2020
Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy.
Article in Gynecologic oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 45 citations in OpenAlex.
- The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [European journal of nuclear medicine and molecular imaging · 2026Article
- A 20-feature radiomic signature of triple-negative breast cancer identifies patients at high risk of death.NPJ breast cancer · 2025Article
- A T2 weighted imaging-based radiomics nomogram for the classification of hepatic blood-rich lesions: hepatocellular carcinoma and benign liver lesions.Discover oncology · 2025Article
- Multi-parametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in early-stage cervical adenocarcinoma.Frontiers in oncology · 2025Article
- 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
- Endovaginal coil for pelvic high-resolution magnetic resonance imaging of cervical cancer: a preliminary parameter optimization study.Quantitative imaging in medicine and surgery · 2024Article
- Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.Scientific reports · 2024Article
- An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators.Journal of ovarian research · 2024Article
- Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer.Frontiers in medicine · 2024Article
- An Update on the Role of MRI in Treatment Stratification of Patients with Cervical Cancer.Cancers · 2023Review
- Review
- MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Article
- MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer.Insights into imaging · 2022Article
- Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm.Frontiers in oncology · 2022Article
- Multimodal MRI-Based Radiomics-Clinical Model for Preoperatively Differentiating Concurrent Endometrial Carcinoma From Atypical Endometrial Hyperplasia.Frontiers in oncology · 2022Article
- Radiomics in surgical oncology: applications and challenges.Computer assisted surgery (Abingdon, England) · 2021Review
- Radiomics in cervical and endometrial cancer.The British journal of radiology · 2021Review
- Review
- Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.European radiology · 2021Article
- Early Metabolic Response Assessed Using 18F-FDG-PET/CT for Image-Guided Intracavitary Brachytherapy Can Better Predict Treatment Outcomes in Patients with Cervical Cancer.Cancer research and treatment · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundTextural features extracted from MRI potentially provide prognostic information additional to volume for influencing surgical management of cervical cancer. PURPOSE: To identify textural features that differ between cervical tumors above and below the volume threshold of eligibility for trachelectomy and determine their value in predicting recurrence in patients with low-volume tumors.
methodsOf 378 patients with Stage1-2 cervical cancer imaged prospectively (3T, endovaginal coil), 125 had well-defined, histologically-confirmed squamous or adenocarcinomas with >100 voxels (>0.07 cm
resultsTextural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm
conclusionTextural features from ADC maps and T2-W images differ between high- and low-volume tumors and potentially predict recurrence in low-volume tumors.
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