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

Abstract

BackgroundIdentification 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).MethodsA 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.FindingsThirty-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.InterpretationThe 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.

A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

“…Amongst these features, most were textural features of images, which demonstrated microscopic description of the tumour including cellularity, peritumoural oedema, and compression of normal brain tissue by tumour. Some features cannot be easily identified by the human visual system or be interpreted to understand the specific meaning [ 34 , 35 , 40 , 41 ]. We observed that radiomics features including T1C_ original_ shape_ maximum 2D diameter slice, T1C_ original_ shape_ maximum 3D diameter, T2_lbp-3D-m2_glrlm_short run high grey level emphasis, etc.…”

Predicting risk of postpartum haemorrhage: a systematic review

“…The search strategy identified 2146 citations; following removal of duplicates and screening, 56 full-text articles were assessed for eligibility (PRISMA Flow Diagram, Figure 1). This review included 14 studies [14][15][16][17][18][19][20][21][22][23][24][25][26][27] with a total of 14 final prediction models identified.…”

“…The populations of the included studies are shown in Tables S1 and S2. Five studies 16,18,21,22,27 included only women with placenta praevia, four studies 17,[23][24][25] included only vaginal deliveries, three studies 14,15,20 had a population consisting of CS (planned and unplanned), one study defined population as consisting of women with placenta accreta spectrum 26 and one study 19 had a population encompassing the general obstetric population.…”

From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health

“…[1]. Also non-oncological studies have started to appear (examples include recent studies on cognitive disorders [2], neuron degeneration [3], cognitive impairment [4], hippocampal sclerosis [5], central nervous system [6], heart failure [7], stroke [8], postpartum haemorrhage [9], and carpal and cubital tunnel syndrome [10]). evaluations (size-based imaging endpoints) with very limited correlation with physiological outcome or early information about response to specific targeted therapies or pursuit of exploratory endpoints toward the design of the so-called integral cancer markers.…”