Deep Learning Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta

• Published in: Journal of magnetic resonance imaging Vol. 59; no. 2; pp. 510 - 521
• Main Authors: Zong, Ming, Pei, Xinlong, Yan, Kun, Luo, Deng, Zhao, Yangyu, Wang, Ping, Chen, Lian
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2024; Wiley Subscription Services, Inc
• Subjects: adverse outcomes; Deep Learning; Female; Field strength; Humans; Hysterectomy; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical models; Medical imaging; multisequence MRI; PAS; Placenta; Placenta Accreta - diagnostic imaging; Population studies; Pregnancy; Pregnancy Outcome; Reclassification; Retrospective Studies; Risk assessment; ROI; Statistical analysis; Statistical tests; deep learning; PAS; adverse outcomes; ROI; multisequence MRI
• DOI: 10.1002/jmri.29023
• ISSN: 1053-1807; 1522-2586

Background Preoperative assessment of adverse outcomes risk in placenta accreta spectrum (PAS) disorders is of high clinical relevance for perioperative management and prognosis. Purpose To investigate the association of preoperative MRI multisequence images and adverse pregnancy outcomes by establishing a deep learning model in patients with PAS. Study Type Retrospective. Population 323 pregnant women (age from 20 to 46, the median age is 33), suspected of PAS, underwent MRI to assess the PAS, divided into the training (N = 227) and validation datasets (N = 96). Field Strength/Sequence 1.5T scanner/fast imaging employing steady‐state acquisition sequence and single shot fast spin echo sequence. Assessment Different deep learning models (i.e., with single MRI input sequence/two sequences/multisequence) were compared to assess the risk of adverse pregnancy outcomes, which defined as intraoperative bleeding ≥1500 mL and/or hysterectomy. Net reclassification improvement (NRI) was used for quantitative comparison of assessing adverse pregnancy outcome between different models. Statistical Tests The AUC, sensitivity, specificity, and accuracy were used for evaluation. The Shapiro–Wilk test and t‐test were used. A P value of <0.05 was considered statistically significant. Results 215 cases were invasive placenta accreta (67.44% of them with adverse outcomes) and 108 cases were non‐invasive placenta accreta (9.25% of them with adverse outcomes). The model with four sequences assessed adverse pregnancy outcomes with AUC of 0.8792 (95% CI, 0.8645–0.8939), with ACC of 85.93% (95%, 84.43%–87.43%), with SEN of 86.24% (95% CI, 82.46%–90.02%), and with SPC of 85.62% (95%, 82.00%–89.23%) on the test cohort. The performance of model with four sequences improved above 0.10 comparing with that of model with two sequences and above 0.20 comparing with that of model with single sequence in terms of NRI. Data Conclusion The proposed model showed good diagnostic performance for assessing adverse pregnancy outcomes. Level of Evidence 3 Technical Efficacy Stage 2