ArticleEuropean journal of nuclear medicine and molecular imaging2019
External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy.
Article in European journal of nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- External validation improves generalizability, replicability and reproducibility in predictive models for neuroimaging.Nature methods · 2026Review
- Cervical cancer in the modern era: cutting-edge strategies for diagnosis and treatment.Japanese journal of radiology · 2026Review
- Evaluating Transferability of ComBat Harmonization of Diffusion Tensor Magnetic Resonance Imaging Data.Annals of biomedical engineering · 2026Article
- A Hybrid Model-Based Clinicopathological Features and Radiomics Based on Conventional MRI for Predicting Lymph Node Metastasis and DFS in Cervical Cancer.Journal of imaging informatics in medicine · 2026Article
- Multimodality and temporal analysis of cervical cancer treatment response.Quantitative imaging in medicine and surgery · 2026Article
- An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.European journal of nuclear medicine and molecular imaging · 2025Article
- Machine Learning-Based Prognostic Modelling Using MRI Radiomic Data in Cervical Cancer Treated with Definitive Chemoradiotherapy and Brachytherapy.Current oncology (Toronto, Ont.) · 2025Article
- Habitat-based transformer model in pretreatmentBMC cancer · 2025Article
- Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer.Cancers · 2025Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Automatic cervical tumors segmentation in PET/MRI by parallel encoder U-net.Radiation oncology (London, England) · 2025Article
- Association of long-term treatment outcomes with changes in PET/MRI characteristics and the type of early treatment response during concurrent radiochemotherapy in patients with locally advanced cervical cancer.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Computed tomography-based radiomics modeling to predict patient overall survival in cervical cancer with intensity-modulated radiotherapy combined with concurrent chemotherapy.The Journal of international medical research · 2025Observational
- Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.Scientific reports · 2025Article
- Article
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- [Diagnostics (Basel, Switzerland) · 2024Review
- Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.Journal of applied clinical medical physics · 2024Article
- Artificial intelligence in female pelvic oncology: tailoring applications to clinical needs.European radiology · 2024Article
42 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
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Abstract
purposeThe aim of this study was to validate previously developed radiomics models relying on just two radiomics features from
methodsPatients with LACC receiving chemoradiotherapy were enrolled in two French and one Canadian center. Pre-treatment imaging was performed for each patient. Multicentric harmonization of the two radiomics features was performed with the ComBat method. The models for DFS (using the feature from apparent diffusion coefficient (ADC) MRI) and LRC (adding one PET feature to the DFS model) were tuned using one of the French cohorts (n = 112) and applied to the other French (n = 50) and the Canadian (n = 28) external validation cohorts.
resultsThe DFS model reached an accuracy of 90% (95% CI [79-98%]) (sensitivity 92-93%, specificity 87-89%) in both the French and the Canadian cohorts. The LRC model reached an accuracy of 98% (95% CI [90-99%]) (sensitivity 86%, specificity 100%) in the French cohort and 96% (95% CI [80-99%]) (sensitivity 83%, specificity 100%) in the Canadian cohort. Accuracy was significantly lower without ComBat harmonization (82-85% and 71-86% for DFS and LRC, respectively). The best prediction using standard clinical variables was 56-60% only.
conclusionsThe previously developed PET/MRI radiomics predictive models were successfully validated in two independent external cohorts. A proposed flowchart for improved management of patients based on these models should now be confirmed in future larger prospective studies.
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