Share：
Share this content in WeChat
X
[Chinese][PDF]98393
• Clinical Article •
Combining clinical characteristics and specific magnetic resonance imaging features to predict the risk of hysterectomy in gravid patients at high risk for placenta accreta spectrum disorders
ZHONG Shuyuan  DING Zhiguang  XU Jianmin  HU Genwen  SU Fangming  CHENG Zhiqiang 

Cite this article as: Zhong SY, Ding ZG, Xu JM, et al. Combining clinical characteristics and specific magnetic resonance imaging features to predict the risk of hysterectomy in gravid patients at high risk for placenta accreta spectrum disorders[J]. Chin J Magn Reson Imaging, 2021, 12(5): 35-39. DOI:10.12015/issn.1674-8034.2021.05.008.


[Abstract] Objective To explore the value of clinical characteristics combined with MRI features for predicting the risk of intraoperative hysterectomy in gravid patients at high risk for placenta accreta spectrum (PAS). Materials andMethods Retrospectively analyzed the MRI images and clinical data of 251 patients (including 64 patients who required hysterectomy and 187 patients did not) who underwent MRI during the third trimester from January 2010 to March 2020 with high risk for PAS disorders according to FIGO guidelines. Clinical characteristics included age, gestational age at delivery, number of cesarean deliveries, gravidity, prior other uterine surgeries and placenta previa. Two radiologists independently evaluated the following MRI features according to the consensus from Society of Abdominal Radiology and European Society of Urogenital Radiology: T2-dark intraplacental bands, placental bulge, loss of retroplacental T2-hypointense line, myometrial thinning, uterine serosa hypervascularity, focal exophytic mass and disruption of low-T2 bladder wall. Univariate analyses of clinical characteristics and MRI features were performed between patients with hysterectomy and those who without. Absence or uncertainty of MRI features was recorded as negative while presence as positive. Logistic regression was used to identify any clinical or MRI features in predicting hysterectomy. ROC analysis and calibration curve were performed to test the predictive power.Results Significant differences were found in number of cesarean deliveries, placenta previa and all seven MRI features except for loss of retroplacental T2-hypointense line between patients with hysterectomy and those who without (P＜0.01). The number of cesarean deliveries (X1: OR=2.611, P=0.017), T2-dark intraplacental bands (X2: OR=4.379, P=0.001), placental bulge (X3: OR=4.804, P=0.000) and uterine serosa hypervascularity (X4: OR=6.691, P=0.000) were independent risk factors for intraoperative hysterectomy. The Logistic regression model combining the four independent risk factors to forecast intraoperative hysterectomy was Logistic (P)=-4.713+0.960X1+1.477X2+1.569X3+1.901X4. The AUC of the combined risk model reached 0.915, which was larger than that each of the four independent risk factors (P＜0.01). The diagnostic sensitivity and specificity of the model were 87.50% and 81.82%. The model illustrated good calibration.Conclusions Combining clinical characteristics and specific MRI features is benefit to the assessment of the risk of intraoperative hysterectomy in gravid patients at high risk of PAS and improve their prognosis.
[Keywords] placenta accreta spectrum；magnetic resonance imaging；prenatal diagnosis；high-risk gravid patient；hysterectomy

ZHONG Shuyuan1   DING Zhiguang1   XU Jianmin1*   HU Genwen1   SU Fangming2   CHENG Zhiqiang3  

1 Department of Radiology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen 518000, China

2 Department of Obstetrics, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen 518000, China

3 Department of Pathology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen 518000, China

XU JM, E-mail: 13600163204@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS This article is supported by the Shenzhen Basic Research Plan Fund Project (No. JCYJ20180228164641207).
Received  2020-08-23
Accepted  2021-03-25
DOI: 10.12015/issn.1674-8034.2021.05.008
Cite this article as: Zhong SY, Ding ZG, Xu JM, et al. Combining clinical characteristics and specific magnetic resonance imaging features to predict the risk of hysterectomy in gravid patients at high risk for placenta accreta spectrum disorders[J]. Chin J Magn Reson Imaging, 2021, 12(5): 35-39. DOI:10.12015/issn.1674-8034.2021.05.008.

1
Machado LSM. Emergency peripartum hysterectomy: incidence, indications, risk factors and outcome[J]. N Am J Med Sci, 2011, 3(8): 358-361. DOI: 10.4297/najms.2011.358.
2
Rahila I, Zubaida M, Samia H, et al. A comparison of antenatally and intraoperatively diagnosed cases of placenta accreta spectrum[J]. J Turk Ger Gynecol Assoc, 2020, 21(2): 84-89. DOI: 10.4274/jtgga.galenos.2019.2019.0063.
3
Zheng CN, Yang TH, Lin JZ, et al. MRI in the diagnosis of placenta accreta[J]. Chin J Magn Reson Imaging, 2017, 8(8): 593-597. DOI: 10.12015/issn.1674-8034.2017.08.007.
4
Jauniaux E, Chantraine F, Silver RM, et al. FIGO consensus guidelines on placenta accreta spectrum disorders: epidemiology[J]. Int J Gynaecol Obstet, 2018, 140(3): 265-273. DOI: 10.1002/ijgo.12407.
5
Laurence B, Vinciane P, Sandra B, et al. Suspected invasive placenta: evaluation with magnetic resonance imaging[J]. Eur Radiol, 2014, 24(12): 3150-3160. DOI: 10.1007/s00330-014-3354-z.
6
Jha P, Pōder L, Bourgioti C, et al. Society of abdominal radiology (SAR) and European Society of Urogenital Radiology (ESUR) joint consensus statement for MR imaging of placenta accreta spectrum disorders[J]. Eur Radiol, 2020, 30(5): 2604-2615. DOI: 10.1007/s00330-019-06617-7.
7
Chen X, Shan R, Zhao L, et al. 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[J]. Eur Radiol, 2017, 28(2): 708-717. DOI: 10.1007/s00330-017-4980-z.
8
American College of Obstetricians and Gynecologists. Obstetric Care Consensus No. 7: Placenta Accreta Spectrum[J]. Obstet Gynecol, 2018, 132(6): 1519-1521. DOI: 10.1097/AOG.0000000000002984.
9
Bourgioti C, Zafeiropoulou K, Fotopoulos S, et al. MRI prognosticators for adverse maternal and neonatal clinical outcome in patients at high risk for placenta accreta spectrum (PAS) disorders[J]. J Magn Reson Imaging, 2019, 50(2): 602-618. DOI: 10.1002/jmri.26592.
10
Chen D, Xu J, Ye P, et al. Risk scoring system with MRI for intraoperative massive hemorrhage in placenta previa and accreta[J]. J Magn Reson Imaging, 2020, 51(3): 947-958. DOI: 10.1002/jmri.26922.
11
Kanal E, Barkovich AJ, Bell C, et al. ACR guidance document on MR safe practices: 2013[J]. J Magn Reson Imaging, 2013, 37(3): 501-530. DOI: 10.1002/jmri.24011.
12
Lax A, Prince MR, Mennitt KW, et al. The value of specific MRI features in the evaluation of suspected placental invasion[J]. Magn Reson Imaging, 2007, 25(1): 87-93. DOI: 10.1016/j.mri.2006.10.007.
13
Einerson BD, Rodriguez CE, Silver RM, et al. Accuracy and interobserver reliability of magnetic resonance imaging for placenta accreta spectrum disorders[J]. Am J Perinat, 2020, DOI: 10.1055/s-0040-1701196.
14
Huang F, Lai Q, Wu H, et al. Application of indirect signs of magnetic resonance imaging (MRI) in prenatal diagnosis of abnormally invasive placenta[J]. Med Sci Monit, 2020, 26(6): e923272. DOI: 10.12659/MSM.923272.
15
Jha P, Rabban J, Chen L M, et al. Placenta accreta spectrum: value of placental bulge as a sign of myometrial invasion on MR imaging[J]. Abdom Radiol, 2019, 44(7): 2572-2581. DOI: 10.1007/s00261-019-02008-0.
16
Chantraine F, Blacher S, Berndt S, et al. Abnormal vascular architecture at the placental-maternal interface in placenta increta[J]. Am J Obstet Gynecol, 2012, 207(3): 181-188. DOI: 10.1016/j.ajog.2012.06.083.
17
Goergen SK, Posma E, Wrede D, et al. Interobserver agreement and diagnostic performance of individual MRI criteria for diagnosis of placental adhesion disorders[J]. Clin Radiol, 2018, 73(10): 901-908. DOI: 10.1016/j.crad.2018.05.021.
18
Linduska N, Dekan S, Messerschmidt A, et al. Placental pathologies in fetal MRI with pathohistological correlation[J]. Placenta, 2009, 30(6): 555-559. DOI: 10.1016/j.placenta.2009.03.010.
19
Zhang J, Xu H, Xin Y, et al. Assessment of the massive hemorrhage in placenta accreta spectrum with magnetic resonance imaging[J]. Exp Ther Med, 2020, 19(3): 2367-2376. DOI: 10.3892/etm.2020.8457.
20
Chen T, Xu XQ, Shi HB, et al. Conventional MRI features for predicting the clinical outcome of patients with invasive placenta[J]. Diagn Interv Radiol, 2017, 23(3): 173. DOI: 10.5152/dir.2016.16412.
21
Marshall NE, Fu R, Guise J. Impact of multiple cesarean deliveries on maternal morbidity: a systematic review[J]. Am J Obstet Gynecol, 2011, 205(3): 261-262. DOI: 10.1016/j.ajog.2011.06.035.
22
Chu C, Zhao S, Ding M, et al. Combining clinical characteristics and specific magnetic resonance imaging features to predict placenta accreta[J]. J Comput Assist Tomo, 2019, 43(5): 775-779. DOI: 10.1097/RCT.0000000000000894.
23
Clark HR, Ng TW, Khan A, et al. Placenta accreta spectrum: correlation of MRI parameters with pathologic and surgical outcomes of high-risk pregnancies[J]. AJR Am J Roentgenol, 2020, 214(6): 1417-1423. DOI: 10.2214/AJR.19.21705.

PREV Application of biparametric magnetic resonance imaging in the detection of prostate cancer: a contrastive study based on whole mount section after radical prostatectomy
NEXT A magnetic resonance imaging study of the influence of runner-related factors on the cross-sectional area of thigh muscles in male amateur marathon runners
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn