Dataset Information

Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images.

ABSTRACT:

Importance

Accurate screening of trisomy 21 in the first trimester can provide an early opportunity for decision-making regarding reproductive choices.

Objective

To develop and validate a deep learning model for screening fetuses with trisomy 21 based on ultrasonographic images.

Design, setting, and participants

This diagnostic study used data from all available cases and controls enrolled at 2 hospitals in China between January 2009 and September 2020. Two-dimensional images of the midsagittal plane of the fetal face in singleton pregnancies with gestational age more than 11 weeks and less than 14 weeks were examined. Observers were blinded to subjective fetus nuchal translucency (NT) marker measurements. A convolutional neural network was developed to construct a deep learning model. Data augmentation was applied to generate more data. Different groups were randomly selected as training and validation sets to assess the robustness of the deep learning model. The fetal NT was shown and measured. Each detection of trisomy 21 was confirmed by chorionic villus sampling or amniocentesis. Data were analyzed from March 1, 2021, to January 3, 2022.

Main outcomes and measures

The primary outcome was detection of fetuses with trisomy 21. The receiver operating characteristic curve, metrics of accuracy, area under the curve (AUC), sensitivity, and specificity were used for model performance evaluation.

Results

A total of 822 case and control participants (mean [SD] age, 31.9 [4.6] years) were enrolled in the study, including 550 participants (mean [SD] age, 31.7 [4.7] years) in the training set and 272 participants (mean [SD] age, 32.3 [4.7] years) in the validation set. The deep learning model showed good performance for trisomy 21 screening in the training (AUC, 0.98; 95% CI, 0.97-0.99) and validation (AUC, 0.95; 95% CI, 0.93-0.98) sets. The deep learning model had better detective performance for fetuses with trisomy 21 than the model with NT marker and maternal age (training: AUC, 0.82; 95% CI, 0.77-0.86; validation: AUC, 0.73; 95% CI, 0.66-0.80).

Conclusions and relevance

These findings suggest that this deep learning model accurately screened fetuses with trisomy 21, which indicates that the model is a potential tool to facilitate universal primary screening for trisomy 21.

SUBMITTER: Zhang L

PROVIDER: S-EPMC9214589 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesTo develop and validate a nomogram based on fetal nuchal translucency thickness (NT) and ultrasonographic facial markers for screening for trisomy 21 in the first trimester of pregnancy.MethodsThis was a retrospective case-control study using stored two-dimensional midsagittal fetal profile images captured at 11 + 0 to 13 + 6 weeks' gestation in singleton pregnancies. We included images from 302 trisomy-21 pregnancies and 322 euploid pregnancies. Cases were divided into a training set (200 euploid + 200 with trisomy 21) and a validation set (122 euploid + 102 with trisomy 21) at a ratio of approximately 2:1. For each, the maternal age, gestational age, fetal NT and karyotype were noted, and 12 ultrasonographic fetal facial markers were measured. The least absolute shrinkage and selection operator (LASSO) method and multivariable analysis were used to select automatically the discriminative markers. Logistic regression was used to develop a LASSO model, based on the selected markers, to screen for trisomy 21 in the first trimester of pregnancy. Furthermore, 60 of the 624 images were selected randomly as a retest set to evaluate the model's robustness. The predictive performance of screening for trisomy 21 of a model based on fetal NT and maternal age and of the LASSO model was assessed using the area under the receiver-operating-characteristics curve (AUC). A nomogram was developed as an individualized tool to predict patient-specific probability for trisomy 21, which is a more visual presentation of the LASSO model. The performance of the nomogram was assessed using the C-index and calibration curve.ResultsInto the LASSO model were incorporated eight markers, including fetal NT, prenasal-thickness-to-nasal-bone-length ratio, facial profile line, frontomaxillary facial angle, frontonasal facial angle, mandibulomaxillary facial angle, maxilla-nasion-mandible angle and d2 (distance between the anterior edge of the prefrontal skin and the mandibulomaxillary line) (all P < 0.05). The AUCs of the LASSO model for screening for trisomy 21 were 0.983 (95% CI, 0.971-0.994) in the training set and 0.979 (95% CI, 0.966-0.993) in the validation set, and these were higher than the AUCs of all eight individual ultrasonographic markers included in the model. The AUC of the LASSO model in the retest set was 0.997 (95% CI, 0.990-1.000), indicating good robustness of the LASSO model. The AUC of the LASSO model was significantly higher than that of the model based on fetal NT and maternal age in both training and validation sets (P < 0.001 for both). The nomogram of the LASSO model showed good discrimination of trisomy 21, with C-indices of 0.983 in the training set and 0.981 in the validation set.ConclusionsWe present an individualized nomogram which incorporates fetal NT and a series of ultrasonographic facial profile markers selected by the LASSO method and multivariable analysis. This nomogram can potentially be utilized as a convenient and effective tool in screening for trisomy 21 in the first trimester of pregnancy. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Project description:BackgroundDown syndrome (DS) is the most common known aneuploidy, caused by an extra copy of all or part of chromosome 21. Fetal-specific epigenetic markers have been investigated for non-invasive prenatal detection of fetal DS. The phosphodiesterases gene, PDE9A, located on chromosome 21q22.3, is completely methylated in blood (M-PDE9A) and unmethylated in the placenta (U-PDE9A). Therefore, we estimated the accuracy of non-invasive fetal DS detection during the first trimester of pregnancy using this tissue-specific epigenetic characteristic of PDE9A.Methodology/principal findingsA nested, case-control study was conducted using maternal plasma samples collected from 108 pregnant women carrying 18 DS and 90 normal fetuses (each case was matched with 5 controls according to gestational weeks at blood sampling). All pregnancies were singletons at or before 12 weeks of gestation between October 2008 and May 2009. The maternal plasma levels of M-PDE9A and U-PDE9A were measured by quantitative methylation-specific polymerase chain reaction. M-PDE9A and U-PDE9A levels were obtained in all samples and did not differ between male and female fetuses. M-PDE9A levels did not differ between the DS cases and controls (1854.3 vs 2004.5 copies/mL; P?=?0.928). U-PDE9A levels were significantly elevated in women with DS fetuses compared with controls (356.8 vs 194.7 copies/mL, P<0.001). The sensitivities of U-PDE9A level and the unmethylation index of PDE9A for non-invasive fetal DS detection were 77.8% and 83.3%, respectively, with a 5% false-positive rate. In the risk assessment for fetal DS, the adjusted odds ratios of U-PDE9A level and UI were 46.2 [95% confidence interval: 7.8-151.6] and 63.7 [95% confidence interval: 23.2-206.7], respectively.ConclusionsOur findings suggest that U-PDE9A level and the unmethylation index of PDE9A may be useful biomarkers for non-invasive fetal DS detection during the first trimester of pregnancy, regardless of fetal gender.