Share this content in WeChat
Research progress of MRI in placenta accrete spectrum disorders
WANG Yingchao  HUANG Gang 

Cite this article as: WANG Y C, HUANG G. Research progress of MRI in placenta accrete spectrum disorders[J]. Chin J Magn Reson Imaging, 2023, 14(1): 194-197, 202. DOI:10.12015/issn.1674-8034.2023.01.036.

[Abstract] Placenta accrete spectrum disorders (PAS) is one of the serious complications of pregnant women in the world, which can lead to postpartum hemorrhage, increase the risk of perioperative hysterectomy, and cause adverse pregnancy outcomes. MRI is an excellent tool for evaluating PAS, which can provide additional information for patients with PAS suspected by ultrasound, such as the scope and extent of the invasion, whether there is extrauterine involvement, and predict the emergency during surgery (such as blood loss, blood transfusion, and hysterectomy). This article aims to discuss the research progress of new MRI technologies, such as diffusion-weighted imaging (DWI), blood oxygen level-dependent (BOLD) imaging, MRI based rediomics and deep learning, in PAS evaluation.
[Keywords] placenta accrete spectrum disorders;magnetic resonance imaging;diffusion-weighted imaging;intravoxel incoherent motion;blood oxygen level-dependent imaging;radiomics;deep learning

WANG Yingchao1, 2   HUANG Gang3*  

1 Gansu University of Chinese Medicine, Lanzhou 730000, China

2 Department of Medical Imaging, Zhangye People's Hospital Affiliated to Hexi University Zhangye 734000, China

3 Department of Radiology, Gansu Province Hospital, Lanzhou 730000, China

Corresponding author: Huang G, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS University Innovation Fund Project of Education Department of Gansu Province (No. 2021B-232); Research Fund Project for Young Teachers of Hexi University (No. QN2020005).
Received  2022-04-06
Accepted  2022-12-21
DOI: 10.12015/issn.1674-8034.2023.01.036
Cite this article as: WANG Y C, HUANG G. Research progress of MRI in placenta accrete spectrum disorders[J]. Chin J Magn Reson Imaging, 2023, 14(1): 194-197, 202. DOI:10.12015/issn.1674-8034.2023.01.036.

DONOVAN B M, SHAINKER S A. Placenta Accreta Spectrum[J/OL]. Neoreviews, 2021, 22(11): e722-e733 [2022-04-06]. DOI: 10.1542/neo.22-11-e722.
TANTBIROJN P, CRUM C P, PARAST M M. Pathophysiology of placenta creta: the role of decidua and extravillous trophoblast[J]. Placenta, 2008, 29(7): 639-645. DOI: 10.1016/j.placenta.2008.04.008.
ZUCKERWISE L C, CRAIG A M, NEWTON J M, et al. Outcomes following a clinical algorithm allowing for delayed hysterectomy in the management of severe placenta accreta spectrum[J/OL]. Am J Obstet Gynecol, 2020, 222(2): 179.e171-179.e179 [2022-12-05]. DOI: 10.1016/j.ajog.2019.08.035.
SILVER R M, LANDON M B, ROUSE D J, et al. Maternal morbidity associated with multiple repeat cesarean deliveries[J]. Obstet Gynecol, 2006, 107(6): 1226-1232. DOI: 10.1097/01.AOG.0000219750.79480.84.
SCHWICKERT A, VAN BEEKHUIZEN H J, BERTHOLDT C, et al. Association of peripartum management and high maternal blood loss at cesarean delivery for placenta accreta spectrum (PAS): a multinational database study[J]. Acta Obstet Gynecol Scand, 2021, 100(Suppl 1): 29-40. DOI: 10.1111/aogs.14103.
FIOCCHI F, MONELLI F, BESUTTI G, et al. MRI of placenta accreta: diagnostic accuracy and impact of interventional radiology on foetal-maternal delivery outcomes in high-risk women[J/OL]. Br J Radiol, 2020, 93(1114): 20200267 [2022-12-05]. DOI: 10.1259/bjr.20200267.
FINAZZO F, D'ANTONIO F, MASSELLI G, et al. Interobserver agreement in MRI assessment of severity of placenta accreta spectrum disorders[J]. Ultrasound Obstet Gynecol, 2020, 55(4): 467-473. DOI: 10.1002/uog.20381.
CANTWELL R, CLUTTON-BROCK T, COOPER G, et al. Saving Mothers' Lives: reviewing maternal deaths to make motherhood safer: 2006-2008. The Eighth Report of the Confidential Enquiries into Maternal Deaths in the United Kingdom[J]. BJOG, 2011, 118(Suppl 1): 1-203. DOI: 10.1111/j.1471-0528.2010.02847.x.
DO Q N, LEWIS M A, XI Y, et al. MRI of the placenta accreta spectrum (PAS) disorder: radiomics analysis correlates with surgical and pathological outcome[J]. J Magn Reson Imaging, 2020, 51(3): 936-946. DOI: 10.1002/jmri.26883.
TAVAKOLI A, KRAMMER J, ATTENBERGER U I, et al. Simultaneous multislice diffusion-weighted imaging of the kidneys at 3 T[J]. Invest Radiol, 2020, 55(4): 233-238. DOI: 10.1097/RLI.0000000000000637.
SANNANANJA B, ELLERMEIER A, HIPPE D S, et al. Utility of diffusion-weighted MR imaging in the diagnosis of placenta accreta spectrum abnormality[J]. Abdom Radiol (NY), 2018, 43(11): 3147-3156. DOI: 10.1007/s00261-018-1599-8.
HOROWITZ J M, HOTALEN I M, MILLER E S, et al. How can pelvic MRI with diffusion-weighted imaging help my pregnant patient?[J]. Am J Perinatol, 2020, 37(6): 577-588. DOI: 10.1055/s-0039-1685492.
FEDERAU C. Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: A review of the evidence[J/OL]. NMR Biomed, 2017, 30: e3780 [2022-04-06]. DOI: 10.1002/nbm.3780.
LEFEBVRE T, HÉBERT M, BILODEAU L, et al. Intravoxel incoherent motion diffusion-weighted MRI for the characterization of inflammation in chronic liver disease[J]. Eur Radiol, 2021, 31(3): 1347-1358. DOI: 10.1007/s00330-020-07203-y.
CHENG Z, YANG Q, HE H, et al. Intravoxel incoherent motion diffusion-weighted imaging and shear wave elastography for evaluating peritumoral liver fibrosis after transarterial chemoembolization in a VX2 rabbit liver tumor model[J/OL]. Front Physiol, 2022, 13: 893925 [2022-12-05]. DOI: 10.3389/fphys.2022.893925.
VAN V D PHI, BECKER A S, CIRITSIS A, et al. Intravoxel incoherent motion analysis of abdominal organs: application of simultaneous multislice acquisition[J]. Invest Radiol, 2018, 53(3): 179-185. DOI: 10.1097/RLI.0000000000000426.
GURNEY-CHAMPION O J, FROELING M, KLAASSEN R, et al. Minimizing the acquisition time for intravoxel incoherent motion magnetic resonance imaging acquisitions in the liver and pancreas[J]. Invest Radiol, 2016, 51(4): 211-220. DOI: 10.1097/RLI.0000000000000225.
BANE O, HECTORS S J, GORDIC S, et al. Multiparametric magnetic resonance imaging shows promising results to assess renal transplant dysfunction with fibrosis[J]. Kidney Int, 2020, 97(2): 414-420. DOI: 10.1016/j.kint.2019.09.030.
ZHANG J L, LEE V S. Renal perfusion imaging by MRI[J]. J Magn Reson Imaging, 2020, 52(2): 369-379. DOI: 10.1002/jmri.26911.
JAUNIAUX E, COLLINS S, BURTON G J. Placenta accreta spectrum: pathophysiology and evidence-based anatomy for prenatal ultrasound imaging[J]. Am J Obstet Gynecol, 2018, 218(1): 75-87. DOI: 10.1016/j.ajog.2017.05.067.
BAO Y W, PANG Y, SUN Z Y, et al. Functional diagnosis of placenta accreta by intravoxel incoherent motion model diffusion-weighted imaging[J]. Eur Radiol, 2021, 31(2): 740-748. DOI: 10.1007/s00330-020-07200-1.
LU T, PU H, LI K D, et al. Can introvoxel incoherent motion MRI be used to differentiate patients with placenta accreta spectrum disorders?[J/OL]. BMC Pregnancy Childbirth, 2019, 19(1): 531 [2022-04-06]. DOI: 10.1186/s12884-019-2676-x.
SIAUVE N, HAYOT P H, DELOISON B, et al. Assessment of human placental perfusion by intravoxel incoherent motion MR imaging[J]. J Matern Fetal Neonatal Med, 2019, 32(2): 293-300. DOI: 10.1080/14767058.2017.1378334.
LU T, WANG Y, GUO A, et al. Monoexponential, biexponential and diffusion kurtosis MR imaging models: quantitative biomarkers in the diagnosis of placenta accreta spectrum disorders[J/OL]. BMC Pregnancy Childbirth, 2022, 22(1): 349 [2022-12-05]. DOI: 10.1186/s12884-022-04644-9.
LU T, SONG B, PU H, et al. Prognosticators of intravoxel incoherent motion (IVIM) MRI for adverse maternal and neonatal clinical outcomes in patients with placenta accreta spectrum disorders[J]. Transl Androl Urol, 2020, 9(2): 258-266. DOI: 10.21037/tau.2019.12.27.
KRISTI B A, DITTE N H, CAROLINE H, et al. Placental diffusion-weighted MRI in normal pregnancies and those complicated by placental dysfunction due to vascular malperfusion[J]. Placenta, 2020, 91: 52-58. DOI: 10.1016/j.placenta.2020.01.009.
SCHABEL M C, ROBERTS V H J, LO J O, et al. Functional imaging of the nonhuman primate Placenta with endogenous blood oxygen level-dependent contrast[J]. Magn Reson Med, 2016, 76(5): 1551-1562. DOI: 10.1002/mrm.26052.
GRUTZENDLER J, NEDERGAARD M. Cellular control of brain capillary blood flow: in vivo imaging veritas[J]. Trends Neurosci, 2019, 42(8): 528-536. DOI: 10.1016/j.tins.2019.05.009.
YOU W, ANDESCAVAGE N N, KAPSE K, et al. Hemodynamic responses of the placenta and brain to maternal hyperoxia in fetuses with congenital heart disease by using blood oxygen-level dependent MRI[J]. Radiology, 2020, 294(1): 141-148. DOI: 10.1148/radiol.2019190751.
VAN DEN BOOMEN M, MANHARD M K, SNEL G J H, et al. Blood oxygen level-dependent MRI of the myocardium with multiecho gradient-echo spin-echo imaging[J]. Radiology, 2020, 294(3): 538-545. DOI: 10.1148/radiol.2020191845.
JIANG K, FERGUSON C M, LERMAN L O. Noninvasive assessment of renal fibrosis by magnetic resonance imaging and ultrasound techniques[J]. Transl Res, 2019, 209: 105-120. DOI: 10.1016/j.trsl.2019.02.009.
SIAUVE N, CHALOUHI G E, DELOISON B, et al. Functional imaging of the human placenta with magnetic resonance[J]. Am J Obstet Gynecol, 2015, 213(4): S103-S114. DOI: 10.1016/j.ajog.2015.06.045.
INGRAM E, MORRIS D, NAISH J, et al. MR imaging measurements of altered placental oxygenation in pregnancies complicated by fetal growth restriction[J]. Radiology, 2017, 285(3): 953-960. DOI: 10.1148/radiol.2017162385.
MAGAWA S, NII M, ENOMOTO N, et al. Evaluation of placental oxygenation in fetal growth restriction using blood oxygen level-dependent magnetic resonance imaging[J]. Placenta, 2022, 126: 40-45. DOI: 10.1016/j.placenta.2022.06.005.
MAGAWA S, NII M, ISHIDA M, et al. Evaluation of placental oxygenation index using blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) during normal late pregnancy[J]. J Matern Fetal Neonatal Med, 2022, 35(25): 5274-5281. DOI: 10.1080/14767058.2021.1878140.
SINDING M, PETERS D A, POULSEN S S, et al. Placental baseline conditions modulate the hyperoxic BOLD-MRI response[J]. Placenta, 2018, 61: 17-23. DOI: 10.1016/j.placenta.2017.11.002.
GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
ATHER S, KADIR T, GLEESON F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications[J]. Clin Radiol, 2020, 75(1): 13-19. DOI: 10.1016/j.crad.2019.04.017.
LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
HOSNY A, PARMAR C, QUACKENBUSH J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18(8): 500-510. DOI: 10.1038/s41568-018-0016-5.
CURRIE G M. Intelligent imaging: artificial intelligence augmented nuclear medicine[J]. J Nucl Med Technol, 2019, 47(3): 217-222. DOI: 10.2967/jnmt.119.232462.
GHAFOORIAN M, KARSSEMEIJER N, HESKES T, et al. Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities[J/OL]. Sci Rep, 2017, 7(1): 5110 [2022-04-06]. DOI: 10.1038/s41598-017-05300-5.
ZHOU Y, YEN G G, YI Z. Evolutionary compression of deep neural networks for biomedical image segmentation[J]. IEEE Trans Neural Netw Learn Syst, 2020, 31(8): 2916-2929. DOI: 10.1109/TNNLS.2019.2933879.
ROMEO V, RICCIARDI C, CUOCOLO R, et al. Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa[J]. Magn Reson Imaging, 2019, 64: 71-76. DOI: 10.1016/j.mri.2019.05.017.
CHEN E, MAR W A, HOROWITZ J M, et al. Texture analysis of placental MRI: can it aid in the prenatal diagnosis of placenta accreta spectrum?[J]. Abdom Radiol (NY), 2019, 44(9): 3175-3184. DOI: 10.1007/s00261-019-02104-1.
SUN H Q, QU H B, CHEN L, et al. Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning[J]. Eur Radiol, 2019, 29(11): 6152-6162. DOI: 10.1007/s00330-019-06372-9.
LU T, ZHANG T Y, LI X Q, et al. Radiomics model based on T2WI for prenatal predicting placenta accreta spectrum disorders[J]. Chin J Med Imaging Technol, 2021, 37(12): 1854-1859. DOI: 10.13929/j.issn.1003-3289.2021.12.022.
WU Q X, YAO K, LIU Z Y, et al. Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: a multicentre study[J]. EBioMedicine, 2019, 50: 355-365. DOI: 10.1016/j.ebiom.2019.11.010.
WU Q, YAO K, LIU Z, et al. Erratum to 'Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study'[J/OL]. EBioMedicine, 2020, 55: 102773 [2022-04-06]. DOI: 10.1016/j.ebiom.2020.102773.
STANZIONE A, VERDE F, CUOCOLO R, et al. Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal[J/OL]. Eur J Radiol, 2022, 155: 110497 [2022-12-05]. DOI: 10.1016/j.ejrad.2022.110497.
KURATA Y, NISHIO M, KIDO A, et al. Automatic segmentation of the uterus on MRI using a convolutional neural network[J/OL]. Comput Biol Med, 2019, 114: 103438 [2022-04-06]. DOI: 10.1016/j.compbiomed.2019.103438.
SHAO Q, XUAN R R, WANG Y T, et al. Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI[J]. Math Biosci Eng, 2021, 18(5): 6198-6215. DOI: 10.3934/mbe.2021310.
XUAN R, LI T, WANG Y, et al. Prenatal prediction and typing of placental invasion using MRI deep and radiomic features[J/OL]. Biomed Eng Online, 2021, 20(1): 56 [2022-04-06]. DOI: 10.1186/s12938-021-00893-5.
LIU J, WU T, PENG Y, et al. Grade Prediction of Bleeding Volume in Cesarean Section of Patients With Pernicious Placenta Previa Based on Deep Learning[J/OL]. Front Bioeng Biotechnol, 2020, 8: 343 [2022-04-06]. DOI: 10.3389/fbioe.2020.00343.
RAVANBAKHSH M, TSCHERNEZKI V, LAST F, et al. Human-machine collaboration for medical image segmentation[J]. Proc IEEE Int Conf Acoust Speech Signal Process, 2020, 2020: 1040-1044. DOI: 10.1109/ICASSP40776.2020.9053555.

PREV Research progress of radiomics in bladder cancer
NEXT Advanced application of amide proton transfer imaging in female reproductive system

Tel & Fax: +8610-67113815    E-mail: