MRI-Based Radiomics Analysis for Intraoperative Risk Assessment in Gravid Patients at High Risk with Placenta Accreta Spectrum

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2022 Feb 25

PMID 35204575

Authors

Caiting Chu

Ming Liu

Yuzhen Zhang

Shuhui Zhao

Yaqiong Ge

Wenhua Li

Chengjin Gao

Affiliations

Soon will be listed here.

Abstract

Background: Gravid patients at high risk with placenta accreta spectrum (PAS) face life-threatening risk at delivery. Intraoperative risk assessment for patients is currently insufficient. We aimed to develop an assessment system of intraoperative risks through MRI-based radiomics.

Methods: A total of 131 patients enrolled were randomly grouped according to a ratio of 7:3. Clinical data were analyzed retrospectively. Radiomic features were extracted from sagittal Fast Imaging Employing State-sate Acquisition images. Univariate and multivariate regression analyses were performed to build models using R software. A receiver operating characteristic curve and decision curve analysis (DCA) were performed to determine the predictive performance of models.

Results: Six radiomic features and two clinical variables were used to construct the combined model for selection of removal protocols of the placenta, with an area under the curve (AUC) of 0.90 and 0.91 in the training and test cohorts, respectively. Nine radiomic features and two clinical variables were obtained to establish the combined model for prediction of intraoperative blood loss, with an AUC of 0.90 and 0.88 in the both cohorts, respectively. The DCA confirmed the clinical utility of the combined model.

Conclusion: The analysis of combined MRI-based radiomics with clinics could be clinically beneficial for patients.

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References

Alamo L, Anaye A, Rey J, Denys A, Bongartz G, Terraz S . Detection of suspected placental invasion by MRI: do the results depend on observer' experience?. Eur J Radiol. 2012; 82(2):e51-7. DOI: 10.1016/j.ejrad.2012.08.022. View

Zhang J, Xu H, Xin Y, Zhang C, Liu Z, Han X . Assessment of the massive hemorrhage in placenta accreta spectrum with magnetic resonance imaging. Exp Ther Med. 2020; 19(3):2367-2376. PMC: 7027333. DOI: 10.3892/etm.2020.8457. View

Sentilhes L, Kayem G, Silver R . Conservative Management of Placenta Accreta Spectrum. Clin Obstet Gynecol. 2018; 61(4):783-794. DOI: 10.1097/GRF.0000000000000395. View

Silver R, Branch D . Placenta Accreta Spectrum. N Engl J Med. 2018; 378(16):1529-1536. DOI: 10.1056/NEJMcp1709324. View

Wu Q, Yao K, Liu Z, Li L, Zhao X, Wang S . Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study. EBioMedicine. 2019; 50:355-365. PMC: 6921361. DOI: 10.1016/j.ebiom.2019.11.010. View

Carusi D . The Placenta Accreta Spectrum: Epidemiology and Risk Factors. Clin Obstet Gynecol. 2018; 61(4):733-742. DOI: 10.1097/GRF.0000000000000391. View

Liu H, Zhang C, Wang L, Luo R, Li J, Zheng H . MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol. 2018; 29(8):4418-4426. DOI: 10.1007/s00330-018-5802-7. View

Bowman Z, Eller A, Bardsley T, Greene T, Varner M, Silver R . Risk factors for placenta accreta: a large prospective cohort. Am J Perinatol. 2013; 31(9):799-804. DOI: 10.1055/s-0033-1361833. View

Hobson S, Kingdom J, Murji A, Windrim R, Carvalho J, Singh S . No. 383-Screening, Diagnosis, and Management of Placenta Accreta Spectrum Disorders. J Obstet Gynaecol Can. 2019; 41(7):1035-1049. DOI: 10.1016/j.jogc.2018.12.004. View

10.

Romeo V, Ricciardi C, Cuocolo R, Stanzione A, Verde F, Sarno L . Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa. Magn Reson Imaging. 2019; 64:71-76. DOI: 10.1016/j.mri.2019.05.017. View

11.

Chen X, Shan R, Zhao L, Song Q, Zuo C, Zhang X . Invasive placenta previa: Placental bulge with distorted uterine outline and uterine serosal hypervascularity at 1.5T MRI - useful features for differentiating placenta percreta from placenta accreta. Eur Radiol. 2017; 28(2):708-717. DOI: 10.1007/s00330-017-4980-z. View

12.

Do Q, Lewis M, Xi Y, Madhuranthakam A, Happe S, Dashe J . MRI of the Placenta Accreta Spectrum (PAS) Disorder: Radiomics Analysis Correlates With Surgical and Pathological Outcome. J Magn Reson Imaging. 2019; 51(3):936-946. DOI: 10.1002/jmri.26883. View

13.

Romeo V, Maurea S . The new era of advanced placental tissue characterization using MRI texture analysis: Clinical implications. EBioMedicine. 2020; 51:102588. PMC: 6940719. DOI: 10.1016/j.ebiom.2019.11.049. View

14.

Wu H, Wu C, Zheng H, Wang L, Guan W, Duan S . Radiogenomics of neuroblastoma in pediatric patients: CT-based radiomics signature in predicting MYCN amplification. Eur Radiol. 2020; 31(5):3080-3089. DOI: 10.1007/s00330-020-07246-1. View

15.

Chen E, Mar W, Horowitz J, Allen A, Jha P, Cantrell D . Texture analysis of placental MRI: can it aid in the prenatal diagnosis of placenta accreta spectrum?. Abdom Radiol (NY). 2019; 44(9):3175-3184. DOI: 10.1007/s00261-019-02104-1. View

16.

Jauniaux E, Hussein A, Fox K, Collins S . New evidence-based diagnostic and management strategies for placenta accreta spectrum disorders. Best Pract Res Clin Obstet Gynaecol. 2019; 61:75-88. PMC: 6929563. DOI: 10.1016/j.bpobgyn.2019.04.006. View

17.

Jha P, Poder L, Bourgioti C, Bharwani N, Lewis S, Kamath A . Society of Abdominal Radiology (SAR) and European Society of Urogenital Radiology (ESUR) joint consensus statement for MR imaging of placenta accreta spectrum disorders. Eur Radiol. 2020; 30(5):2604-2615. DOI: 10.1007/s00330-019-06617-7. View

18.

Tanimura K, Morizane M, Deguchi M, Ebina Y, Tanaka U, Ueno Y . A novel scoring system for predicting adherent placenta in women with placenta previa. Placenta. 2018; 64:27-33. DOI: 10.1016/j.placenta.2018.02.005. View

19.

Sun H, Qu H, Chen L, Wang W, Liao Y, Zou L . Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning. Eur Radiol. 2019; 29(11):6152-6162. DOI: 10.1007/s00330-019-06372-9. View

20.

Li Z, Zhang J, Song Y, Yin X, Chen A, Tang N . Utilization of radiomics to predict long-term outcome of magnetic resonance-guided focused ultrasound ablation therapy in adenomyosis. Eur Radiol. 2020; 31(1):392-402. DOI: 10.1007/s00330-020-07076-1. View