Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study

[11] Yang M(2016)Evaluation of interobserver variability and diagnostic performance of developed MRI-based radiological scoring system for invasive placenta previa J Magn Reson Imaging 44 573-6151

[12] Ding Y(2019)How and why should the radiologist look at the placenta? Eur Radiol 29 6149-365

[13] Jauniaux E(2019)Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: a multicentre study EBioMedicine 50 355-76

[14] Chantraine F(2019)Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa Magn Resone Imaging 64 71-6162

[15] Silver RM(2019)Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning Eur Radiol 29 6152-5352

[16] Kapoor H(2021)Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging Abdominal Radiology 46 5344-2664

[17] Hanaoka M(2020)Deep semantic segmentation feature-based radiomics for the classification tasks in medical image analysis IEEE Journal of Biomedical and Health Informatics 25 2655-522

[18] Dawkins A(2021)Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases Med Phys 48 513-7899

[19] Jauniaux E(2021)Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma Med Phys 48 7891-18

[20] Collins S(2021)Prenatal prediction and typing of placental invasion using MRI deep and radiomic features BioMedical Engineering OnLine 20 1-6215

[11] Yang M(2016)Evaluation of interobserver variability and diagnostic performance of developed MRI-based radiological scoring system for invasive placenta previa J Magn Reson Imaging 44 573-6151

[12] Ding Y(2019)How and why should the radiologist look at the placenta? Eur Radiol 29 6149-365

[13] Jauniaux E(2019)Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: a multicentre study EBioMedicine 50 355-76

[14] Chantraine F(2019)Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa Magn Resone Imaging 64 71-6162

[15] Silver RM(2019)Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning Eur Radiol 29 6152-5352

[16] Kapoor H(2021)Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging Abdominal Radiology 46 5344-2664

[17] Hanaoka M(2020)Deep semantic segmentation feature-based radiomics for the classification tasks in medical image analysis IEEE Journal of Biomedical and Health Informatics 25 2655-522

[18] Dawkins A(2021)Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases Med Phys 48 513-7899

[19] Jauniaux E(2021)Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma Med Phys 48 7891-18

[20] Collins S(2021)Prenatal prediction and typing of placental invasion using MRI deep and radiomic features BioMedical Engineering OnLine 20 1-6215