The added value of radiomic analysis for predicting spontaneous preterm birth in the first trimester.

BACKGROUND AND OBJECTIVE: Preterm birth (PTB) is a public health problem. Researchers have worked to identify ways to detect women at risk for PTB early in pregnancy. Although existing biomarkers allow some women to be detected in the second trimester, detection in the first trimester is warranted to initiate earlier interventions. This study aims to explore the added value of radiomic analysis of transvaginal ultrasound (TVUS) images in the construction of first-trimester risk assessment models for predicting spontaneous preterm birth (sPTB). METHODS: A retrospective cohort study was conducted including pregnant women who attended their screening ultrasound examination between 11+0 and 13+6 weeks. Data on medical history (MH), cervical length (CL), and cervical consistency index (CCI) were collected, along with TVUS images. These images were subjected to a computerized radiomic analysis to extract features, which were used to train different machine learning models incorporating a feature selection process. The area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI) was used to evaluate the models' performance in predicting sPTB. RESULTS: A total of 253 pregnant women were included in the study, where 225 had a term birth and 28 had a sPTB. In sPTB prediction, MH, CL, and CCI obtained AUCs of 0.68 (95% CI, 0.56-0.79), 0.61 (95% CI, 0.48-0.73) and 0.67 (95% CI, 0.55-0.79), respectively. Among the evaluated machine learning models, logistic regression achieved the highest AUC and was therefore used to build the radiomic feature-based model (RF), which reached an AUC of 0.67 (95% CI, 0.56-0.76). When combining RF with MH and CCI, AUCs of 0.73 (95% CI, 0.63-0.82) and 0.74 (95% CI, 0.64-0.83) were achieved, respectively. The best performance was obtained by combining RF with both MH and CCI, reaching an AUC of 0.76 (95% CI, 0.68-0.84). CONCLUSION: The performance of the models improved by adding radiomic features, highlighting the potential of radiomic analysis for sPTB prediction in the first trimester.