Erratum to ‘Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study’

Autor: Qingxia Wu, Kuan Yao, Zhenyu Liu, Longfei Li, Xin Zhao, Shuo Wang, Honglei Shang, Yusong Lin, Zejun Wen, Xiaoan Zhang, Jie Tian, Meiyun Wang
Jazyk: angličtina
Rok vydání: 2020
Předmět:
SSFSE
Single Shot Fast Spin Echo
Research paper
MRI
Magnetic resonance imaging
T2WI
T2-Weighted Imaging
Placenta
CD
Cesarean Delivery
lcsh:Medicine
SVM
Support Vector Machine
Placenta accreta spectrum
EBL
Estimated Blood Loss
General Biochemistry
Genetics and Molecular Biology
Postpartum haemorrhage
NRI
Net Reclassification Improvement
Estimated blood loss
PPH
Postpartum Haemorrhage
Pregnancy
Image Interpretation
Computer-Assisted
Image Processing
Computer-Assisted
Humans
HASTE
Half-Fourier Acquisition Single-shot Turbo spin Echo
AIC
Akaike's Information Criterion
Retrospective Studies
lcsh:R5-920
Radiomics
Cesarean Section
PP
Placenta Previa
Postpartum Hemorrhage
lcsh:R
PACS
Picture Archiving and Communication system
IDI
Integrated Discrimination Improvement
Reproducibility of Results
General Medicine
Prognosis
Magnetic Resonance Imaging
Nomograms
ROC Curve
Female
VOI
Volume of Interest
PAS
Placenta Accreta Spectrum
Erratum
lcsh:Medicine (General)
LASSO
Least Absolute Shrinkage and Selection Operator
Biomarkers
Zdroj: EBioMedicine, Vol 55, Iss, Pp-(2020)
EBioMedicine
ISSN: 2352-3964
Popis: Background Identification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD). Methods A total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application. Findings Thirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844–0.933) and 0.832 (95% CI, 0.746–0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort. Interpretation The clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.
Databáze: OpenAIRE
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