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Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study

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 nomogr...

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Autores principales: Wu, Qingxia, Yao, Kuan, Liu, Zhenyu, Li, Longfei, Zhao, Xin, Wang, Shuo, Shang, Honglei, Lin, Yusong, Wen, Zejun, Tian, Jie, Wang, Meiyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921361/
https://www.ncbi.nlm.nih.gov/pubmed/31767539
http://dx.doi.org/10.1016/j.ebiom.2019.11.010
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author Wu, Qingxia
Yao, Kuan
Liu, Zhenyu
Li, Longfei
Zhao, Xin
Wang, Shuo
Shang, Honglei
Lin, Yusong
Wen, Zejun
Tian, Jie
Wang, Meiyun
author_facet Wu, Qingxia
Yao, Kuan
Liu, Zhenyu
Li, Longfei
Zhao, Xin
Wang, Shuo
Shang, Honglei
Lin, Yusong
Wen, Zejun
Tian, Jie
Wang, Meiyun
author_sort Wu, Qingxia
collection PubMed
description 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.
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spelling pubmed-69213612019-12-27 Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study Wu, Qingxia Yao, Kuan Liu, Zhenyu Li, Longfei Zhao, Xin Wang, Shuo Shang, Honglei Lin, Yusong Wen, Zejun Tian, Jie Wang, Meiyun EBioMedicine Research paper 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. Elsevier 2019-11-22 /pmc/articles/PMC6921361/ /pubmed/31767539 http://dx.doi.org/10.1016/j.ebiom.2019.11.010 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research paper
Wu, Qingxia
Yao, Kuan
Liu, Zhenyu
Li, Longfei
Zhao, Xin
Wang, Shuo
Shang, Honglei
Lin, Yusong
Wen, Zejun
Tian, Jie
Wang, Meiyun
Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
title Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
title_full Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
title_fullStr Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
title_full_unstemmed Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
title_short Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
title_sort radiomics analysis of placenta on t2wi facilitates prediction of postpartum haemorrhage: a multicentre study
topic Research paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6921361/
https://www.ncbi.nlm.nih.gov/pubmed/31767539
http://dx.doi.org/10.1016/j.ebiom.2019.11.010
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