ArticleThe British journal of radiology2022
MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Clinical translation of a DWI-radiomics nomogram integrating serum biomarkers for pretreatment staging of cervical cancer.BMC medical imaging · 2026Article
- MRI radiomics and nutritional-inflammatory biomarkers: a powerful combination for predicting progression-free survival in cervical cancer patients undergoing concurrent chemoradiotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- 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
- Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.Scientific reports · 2024Article
- Development and validation of aBMC cancer · 2024Article
- MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.Cancer medicine · 2023Article
- The Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review.Life (Basel, Switzerland) · 2023Review
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Authors and funding
6 authors.
Funding
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Abstract
objectiveTo establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer.
methodsA total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (
resultsT1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773).
conclusionThe combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up. ADVANCES IN KNOWLEDGE: A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.
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