Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa

Romeo, Valeria; Ricciardi, Carlo; Cuocolo, Renato; Stanzione, Arnaldo; Verde, Francesco; Sarno, Laura; Improta, Giovanni; Mainenti, Pier Paolo; D’Armiento, Maria; Brunetti, Arturo; Maurea, Simone

doi:10.1016/j.mri.2019.05.017

Cited by 88 publications

(61 citation statements)

References 24 publications

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“…The PAS diagnosis based on ultrasound and MRI was not included in the model in order to avoid the subjective bias from the experience of doctors. Romeo V et al found machine learning analysis of texture features of placenta on T2WI could identify PAS in patients with PP [18]. However, the region-of-interest based features might not reflect the heterogeneity of placenta.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics analysis provides us a quantitative method to reflect tissue or tumor heterogeneity and tends to be more stable compared with subjective evaluation. The studies of radiomics analysis for placenta evaluation have emerged recently but until now are relatively rare [17], [18], [19], [20]. These studies found texture analysis of placenta is a feasible tool for PAS diagnosis and even PAS severity assessment [18].…”

Section: Introductionmentioning

confidence: 99%

“…The studies of radiomics analysis for placenta evaluation have emerged recently but until now are relatively rare [17], [18], [19], [20]. These studies found texture analysis of placenta is a feasible tool for PAS diagnosis and even PAS severity assessment [18]. Siauve N stated that radiomics analysis or machine learning is especially suitable for placenta evaluation with increased heterogeneity in late gestational weeks [17].…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Liu

et al. 2019

EBioMedicine

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yao

Liu

et al. 2019

EBioMedicine

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“…Conversely, Motwani et al 35 investigated the feasibility and accuracy of machine learning to predict 5-year all-cause mortality in patients undergoing coronary computed tomographic angiography (CCTA) and compared the performances to the existing clinical or CCTA metrics. Some classification procedures have been proposed by researchers that compare different techniques [36][37][38][39] or assess cardiovascular risk based on machine learning. [40][41][42] Another study investigated heart valve disease with the adaptive neuro-fuzzy inference system.…”

Section: Related Work and Aimmentioning

confidence: 99%

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Ricciardi

Valente

Edmund

et al. 2020

Health Informatics J

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Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.

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“…This study confirmed the increase in placental heterogeneity with gestational age. In the other study, Romeo et al reported their preliminary experiences with a radiomic approach for the diagnosis of invasive placentation in patients with placenta previa, using a combination of ML and TA [5]. They concluded that it was feasible to use this tool to identify the placental tissue abnormalities underlying placental adherence abnormalities.…”

Section: What Is New?mentioning

confidence: 99%

How and why should the radiologist look at the placenta?

Siauve

2019

Eur Radiol

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This editorial comment refers to the article "Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning" by Sun et al. in European Radiology. Key Points • Understanding how the placenta works is one of the major challenges facing radiologists. • New perspectives are opening up for MRI studies of the placenta. • The authors propose a new approach to placental MRI based on texture analysis and machine learning. Keywords Magnetic resonance imaging. Placenta. Artificial intelligence Abbreviations AI Artificial intelligence FGR Fetal growth retardation FIESTA Fast imaging employing steady-state acquisition FISP Fast imaging with steady-state precession HASTE Half-acquisition turbo spin-echo ML Machine learning TA Texture analysis

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Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa

Cited by 88 publications

References 24 publications

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

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

Linear discriminant analysis and principal component analysis to predict coronary artery disease

How and why should the radiologist look at the placenta?

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