Utility of In Vivo Magnetic Resonance Imaging Is Predictive of Gestational Diabetes Mellitus During Early Pregnancy

Lee, Brian; Janzen, Carla; Wu, Holden H.; Vangala, Sitaram; Devaskar, Sherin U.; Sung, Kyunghyun

doi:10.1210/clinem/dgac602

Cited by 1 publication

(1 citation statement)

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“…In addition to laboratory tests, various imaging technology techniques are available to assess placental function in early pregnancy, including Uterine Artery Pulsatility Index (UtA-PI), free-breathing Magnetic Resonance Imaging (MRI), and three-dimensional placental imaging. However, the assessment of placental function in the context of placental changes in GDM has not been extensively studied ( 6 – 8 ).…”

Section: Introductionmentioning

confidence: 99%

Predictive value of ultrasonic artificial intelligence in placental characteristics of early pregnancy for gestational diabetes mellitus

Zhou,

Chen,

Chen

et al. 2024

Front. Endocrinol.

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BackgroundTo explore the predictive value of placental features in early pregnancy for gestational diabetes mellitus (GDM) using deep and radiomics-based machine learning (ML) applied to ultrasound imaging (USI), and to develop a nomogram in conjunction with clinical features.MethodsThis retrospective multicenter study included 415 pregnant women at 11-13 weeks of gestation from two institutions: the discovery group from center 1 (n=305, control group n=166, GDM group n=139), and the independent validation cohort (n=110, control group n=57, GDM group n=53) from center 2. The 2D USI underwent pre-processed involving normalization and resampling. Subsequently, the study performed screening of radiomics features with Person correlation and mutual information methods. An RBF-SVM model based on radiomics features was constructed using the five-fold cross-validation method. Resnet-50 as the backbone network was employed to learn the region of interest and constructed a deep convolutional neural network (DLCNN) from scratch learning. Clinical variables were screened using one-way logistic regression, with P<0.05 being the threshold for statistical significance, and included in the construction of the clinical model. Nomogram was built based on ML model, DLCNN and clinical models. The performance of nomogram was assessed by calibration curves, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).ResultsThe AUCs for the ML model in the discovery cohort and independent validation cohort were 0.91 (0.88-0.94) and 0.86 (0.79-0.93), respectively. And 0.65 (0.59-0.71), 0.69 (0.59-0.79) for the DLCNN, 0.66 (0.59-0.72), 0.66 (0.55-0.76) for the clinical model, respectively. The nomogram exhibited the highest performance with AUCs of 0.93 (0.90-0.95) and 0.88 (0.81-0.94) The receiver operating characteristic curve (ROC) proved the superiority of the nomogram of clinical utility, and calibration curve showed the goodness of fit of the model. The DCA curve indicated that the nomogram outperformed other models in terms of net patient benefit.ConclusionsThe study emphasized the intrinsic relationship between early pregnancy placental USI and the development of GDM. The use of nomogram holds potential for clinical applications in predicting the development of GDM.

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Section: Introductionmentioning

confidence: 99%