AUTHOR=Liu Xue-Fei , Lu Jing-Jing , Li Meng-Die , Li Ying , Zeng An-Rong , Qiang Jin-Wei 

TITLE=Prediction of pre-eclampsia by using radiomics nomogram from gestational hypertension patients

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.961348

DOI=10.3389/fnins.2022.961348

ISSN=1662-453X

ABSTRACT=Background: Pre-eclampsia (PE) is the main causes of death of 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).
Methods: A total of 138 patients with hypertensive disorders of pregnancy were continuously enrolled in the study prospectively including 58 PE patients and 80 GH patients. The patients were randomly divided into a training cohort (n = 97) and a test cohort (n = 41). Radiomics features were extracted from DWI and ADC maps. The radiomics signature was constructed using a least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. A radiomics nomogram was developed by combining the radiomics signature with the selected clinical risk factors. The area under the receiver operating characteristic (ROC) curves (AUC), specificity, sensitivity, accuracy, positive predictive value, and negative predictive values of the radiomics signature, clinical risk factors and radiomics nomogram were calculated. Decision curve analysis (DCA) was performed to determine the clinical usefulness of the radiomics nomogram.
Results: 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 (0.89 [95% CI, 0.82-0.95]) and test cohort (0.85 [95% CI, 0.73-0.97]) for predicting PE from GH. The DCA indicated that clinician and patients could be 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 clinician and patients.