ArticleFrontiers in neuroscience2022
Prediction of pre-eclampsia by using radiomics nomogram from gestational hypertension patients.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Analysis of risk factors and construction of nomograph model for critical condition of patients with hypertension during pregnancy.BMC pregnancy and childbirth · 2023Trial
- Artificial Intelligence for the Prediction of Preeclampsia: Current Evidence, Comparison with Conventional Screening Models, and Future Perspectives.Diagnostics (Basel, Switzerland) · 2026Review
- The clinical value of nomogram combined with machine learning models in predicting the progression of hypertensive disorders in pregnancy to severe preeclampsia.Frontiers in global women's health · 2026Article
- Current Understanding on Hypertensive Disorders in Pregnancy.International journal of general medicine · 2026Review
- Detection of placental stiffness using virtual magnetic resonance elastography in pregnancies complicated by preeclampsia.Archives of gynecology and obstetrics · 2024Article
- Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI.Frontiers in neurology · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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Abstract
Background: Pre-eclampsia (PE) is the main cause of death in maternal and prenatal morbidity. No effective clinical tools could be used for the prediction of PE. A radiomics nomogram based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps was established to predict PE from gestational hypertension (GH). Materials and methods: A total of 138 patients with hypertensive disorders of pregnancy were continuously enrolled in the study prospectively, namely, 58 patients with PE and 80 patients with GH. The patients were randomly divided into a training cohort ( Results: The LASSO analysis finally included 11 radiomics features, which were defined as the radiomics signature. The individualized prediction nomogram was constructed by integrating the radiomics signature, maternal age, and body mass index (BMI). The nomogram exhibited a good performance both in the training cohort [AUC of 0.89 (95% CI, 0.82-0.95)] and test cohort [AUC of 0.85 (95% CI, 0.73-0.97)] for predicting PE from GH. The DCA indicated that clinicians and patients could benefit from the use of radiomics nomogram. Conclusion: The radiomics nomogram could individually predict PE from GH. The nomogram could be conveniently used to facilitate the treatment decision for clinicians and patients.
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