Citation: Chu, C.; Liu, M.; Zhang, Y.;
Zhao, S.; Ge, Y.; Li, W.; Gao, C.
MRI-Based Radiomics Analysis for
Intraoperative Risk Assessment in
Gravid Patients at High Risk with
Placenta Accreta Spectrum.
Diagnostics 2022, 12, 485. https://
doi.org/10.3390/
diagnostics12020485
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 27 November 2021
Accepted: 25 December 2021
Published: 14 February 2022
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Article
MRI-Based Radiomics Analysis for Intraoperative Risk
Assessment in Gravid Patients at High Risk with Placenta
Accreta Spectrum
Caiting Chu
1,†
, Ming Liu
1,†
, Yuzhen Zhang
1
, Shuhui Zhao
1
, Yaqiong Ge
2
, Wenhua Li
1,
* and Chengjin Gao
1,
*
1
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China;
chucaiting@xinhuamed.com.cn (C.C.); liuming01@xinhuamed.com.cn (M.L.);
zhangyuzhen@xinhuamed.com.cn (Y.Z.); zhaoshuhui@xinhuamed.com.cn (S.Z.)
2
GE Healthcare, Pudong New Town, No. 1, Huatuo Road, Shanghai 201203, China; Yaqiong.Ge@ge.com
* Correspondence: liwenhua@xinhuamed.com.cn (W.L.); gaochengjin@xinhuamed.com.cn (C.G.)
† These authors contributed equally to this work.
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.
Keywords: radiomics; MRI; placenta accreta spectrum; high risk
1. Introduction
Placenta accreta spectrum (PAS) represents a heterogeneous group of abnormal pla-
cental implantation, such as placenta accreta, placenta increta, and placenta percreta, based
on the different depths of villi invasion from the myometrium to the uterine serosa [
1
]. PAS
incidence increases annually and is estimated to be over 9000 per year by 2020 [
2
]. Among
them, a history of previous cesarean section (CS) and placenta previa are strongly associated
with the prevalence and incidence of PAS [
3
,
4
]. Herein, pregnant women with a history of
prior CS or present placenta previa are viewed as gravid patients at high risk with PAS [
5
,
6
].
Such patients face serious risks after delivery of the fetus, such as placental residue, life-
threatening hemorrhage, and even death, which are closely related to the breadth and
depth of abnormal placental implantation [
3
,
7
]. Hysterectomy is recommended as a safe
management plan for patients with PAS, owing to the effective control of major hemorrhage.
The corresponding deficiency is the loss of fertility of patients [
8
]. Therefore, patients who
want to preserve their fertility must be willing to choose conservative managements in
cases of safety to be guaranteed [
9
]. The method to develop an appropriate treatment
plan for these patients, including conservative surgical treatment along with hemorrhage
control, is yet to be solved.
Diagnostics 2022, 12, 485. https://doi.org/10.3390/diagnostics12020485 https://www.mdpi.com/journal/diagnostics
