ArticleEuropean journal of nuclear medicine and molecular imaging2023
Multicentric development and evaluation of
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- PET-CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Pooled it
- Imaging based artificial intelligence for predicting lymph node metastasis in cervical cancer patients: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Preoperative non-invasive prediction of lymph node metastasis in cervical cancer using a multiparametric radiomics model based on transvaginal ultrasound.Abdominal radiology (New York) · 2026Article
- Construction of a predictive model for radiation proctitis after radical radiotherapy for cervical cancer based on MRI radiomics.Abdominal radiology (New York) · 2026Article
- Prediction of myelosuppression in cervical cancer after concurrent chemoradiotherapy by CT radiomics-based model.BMC medical imaging · 2026Article
- Article
- Predicting the biological behavior of cervical squamous cell carcinoma: a machine learning approach using apparent transverse relaxation rate (R2* maps) radiomics nomogram.BMC medical imaging · 2026Article
- Development and validation of the risk stratification based on deep learning and radiomics to predict survival of advanced cervical cancer.Scientific reports · 2025Article
- Habitat-based transformer model in pretreatmentBMC cancer · 2025Article
- Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer.Cancers · 2025Article
- Artificial Intelligence in Obstetric and Gynecological MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Harnessing unsupervised machine learning with [Frontiers in oncology · 2025Article
- 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
- [Diagnostics (Basel, Switzerland) · 2024Review
- Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients.Scientific reports · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Lymph node assessment in cervical cancer: current approaches.Frontiers in oncology · 2024Review
- Review
Corrections and comments
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Authors and funding
20 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using
methodsWe retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital
resultsIn the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively.
conclusionsRadiomic features extracted from pre-CRT analog and digital
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.