ArticleRadiation oncology (London, England)2024
Longitudinal dynamic MRI radiomic models for early prediction of prognosis in locally advanced cervical cancer treated with concurrent chemoradiotherapy.
Article in Radiation oncology (London, England), 2024. 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.
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- MR-DELTAnet: A Longitudinal MRI-Transformer Model Predicting Pathological Complete Response and Revealing Immune Microenvironment via scRNA-seq in Locally Advanced Rectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Radiomics and artificial intelligence in precision radiotherapy for cervical cancer: a narrative review.Frontiers in oncology · 2026Review
- Review
- Development, validation, and visualization of a novel nomogram for predicting clinical outcomes of radiotherapy combined with chemotherapy in locally advanced cervical cancer.Frontiers in oncology · 2025Article
- Article
- A Multimodal Graph Neural Network Based on MRI Radi-omics for Prognostic Prediction in Locally Advanced Cervical Squamous Cell Carcinoma.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the early predictive value of dynamic magnetic resonance imaging (MRI)-based radiomics for progression and prognosis in locally advanced cervical cancer (LACC) patients treated with concurrent chemoradiotherapy (CCRT). METHODS AND MATERIALS: A total of 111 LACC patients (training set: 88; test set: 23) were retrospectively enrolled. Dynamic MR images were acquired at baseline (MRI
resultsCompared with single-sequence models, multisequence models exhibited superior performance. MRI
conclusionWe built machine learning models from dynamic features in longitudinal images and found that the ΔMRI
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Registered trials
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