Literature DB >> 31392479

How and why should the radiologist look at the placenta?

N Siauve1,2.   

Abstract

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:  Artificial intelligence; Magnetic resonance imaging; Placenta

Mesh:

Year:  2019        PMID: 31392479     DOI: 10.1007/s00330-019-06373-8

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  8 in total

1.  The value of specific MRI features in the evaluation of suspected placental invasion.

Authors:  Allison Lax; Martin R Prince; Kevin W Mennitt; J Reid Schwebach; Nancy E Budorick
Journal:  Magn Reson Imaging       Date:  2006-11-14       Impact factor: 2.546

Review 2.  MRI evaluation of the placenta from normal variants to abnormalities of implantation and malignancies.

Authors:  Arwa A Zaghal; Hero K Hussain; Ghina A Berjawi
Journal:  J Magn Reson Imaging       Date:  2019-05-17       Impact factor: 4.813

Review 3.  Prenatal identification of invasive placentation using magnetic resonance imaging: systematic review and meta-analysis.

Authors:  F D'Antonio; C Iacovella; J Palacios-Jaraquemada; C H Bruno; L Manzoli; A Bhide
Journal:  Ultrasound Obstet Gynecol       Date:  2014-06-02       Impact factor: 7.299

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

Authors:  Valeria Romeo; Carlo Ricciardi; Renato Cuocolo; Arnaldo Stanzione; Francesco Verde; Laura Sarno; Giovanni Improta; Pier Paolo Mainenti; Maria D'Armiento; Arturo Brunetti; Simone Maurea
Journal:  Magn Reson Imaging       Date:  2019-05-15       Impact factor: 2.546

Review 5.  Functional imaging of the human placenta with magnetic resonance.

Authors:  Nathalie Siauve; Gihad E Chalouhi; Benjamin Deloison; Marianne Alison; Olivier Clement; Yves Ville; Laurent J Salomon
Journal:  Am J Obstet Gynecol       Date:  2015-10       Impact factor: 8.661

6.  The Human Placenta Project: placental structure, development, and function in real time.

Authors:  A E Guttmacher; Y T Maddox; C Y Spong
Journal:  Placenta       Date:  2014-03-06       Impact factor: 3.481

7.  In vivo textural and morphometric analysis of placental development in healthy & growth-restricted pregnancies using magnetic resonance imaging.

Authors:  Nickie Andescavage; Sonia Dahdouh; Marni Jacobs; Sayali Yewale; Dorothy Bulas; Sara Iqbal; Ahmet Baschat; Catherine Limperopoulos
Journal:  Pediatr Res       Date:  2019-01-25       Impact factor: 3.756

8.  Texture analysis of magnetic resonance images of the human placenta throughout gestation: A feasibility study.

Authors:  Quyen N Do; Matthew A Lewis; Ananth J Madhuranthakam; Yin Xi; April A Bailey; Robert E Lenkinski; Diane M Twickler
Journal:  PLoS One       Date:  2019-01-22       Impact factor: 3.240

  8 in total
  3 in total

1.  Prediction of placenta accreta spectrum in patients with placenta previa using clinical risk factors, ultrasound and magnetic resonance imaging findings.

Authors:  Valeria Romeo; Francesco Verde; Laura Sarno; Sonia Migliorini; Mario Petretta; Pier Paolo Mainenti; Maria D'Armiento; Maurizio Guida; Arturo Brunetti; Simone Maurea
Journal:  Radiol Med       Date:  2021-06-22       Impact factor: 3.469

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

Authors:  Zhengjie Ye; Rongrong Xuan; Menglin Ouyang; Yutao Wang; Jian Xu; Wei Jin
Journal:  Abdom Radiol (NY)       Date:  2022-09-12

3.  Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging.

Authors:  Hainan Ren; Naoko Mori; Shunji Mugikura; Hiroaki Shimizu; Sakiko Kageyama; Masatoshi Saito; Kei Takase
Journal:  Abdom Radiol (NY)       Date:  2021-07-30
  3 in total

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