405 related articles for article (PubMed ID: 31444599)
21. Fraction of cell-free fetal DNA in the maternal serum as a predictor of abnormal placental invasion-a pilot study.
Samuel A; Bonanno C; Oliphant A; Batey A; Wright JD
Prenat Diagn; 2013 Nov; 33(11):1050-3. PubMed ID: 23836321
[TBL] [Abstract][Full Text] [Related]
22. Utility of ultrasound and MRI in prenatal diagnosis of placenta accreta: a pilot study.
Lim PS; Greenberg M; Edelson MI; Bell KA; Edmonds PR; Mackey AM
AJR Am J Roentgenol; 2011 Dec; 197(6):1506-13. PubMed ID: 22109309
[TBL] [Abstract][Full Text] [Related]
23. Radiomics-based prediction of FIGO grade for placenta accreta spectrum.
Bartels HC; O'Doherty J; Wolsztynski E; Brophy DP; MacDermott R; Atallah D; Saliba S; Young C; Downey P; Donnelly J; Geoghegan T; Brennan DJ; Curran KM
Eur Radiol Exp; 2023 Sep; 7(1):54. PubMed ID: 37726591
[TBL] [Abstract][Full Text] [Related]
24. The effect of abnormal placentation on maternal serum fetal fraction of cell-free DNA.
Rodriguez M; Smith EL; Silva LM; Gultekin-Elbir EE; Tetla R; Genc MR
J Perinat Med; 2023 Jan; 51(1):97-101. PubMed ID: 36383690
[TBL] [Abstract][Full Text] [Related]
25. Another look at ultrasound and magnetic resonance imaging for diagnosis of placenta accreta.
Budorick NE; Figueroa R; Vizcarra M; Shin J
J Matern Fetal Neonatal Med; 2017 Oct; 30(20):2422-2427. PubMed ID: 27806657
[TBL] [Abstract][Full Text] [Related]
26. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging.
Ren H; Mori N; Mugikura S; Shimizu H; Kageyama S; Saito M; Takase K
Abdom Radiol (NY); 2021 Nov; 46(11):5344-5352. PubMed ID: 34331104
[TBL] [Abstract][Full Text] [Related]
27. Prenatal prediction and typing of placental invasion using MRI deep and radiomic features.
Xuan R; Li T; Wang Y; Xu J; Jin W
Biomed Eng Online; 2021 Jun; 20(1):56. PubMed ID: 34090428
[TBL] [Abstract][Full Text] [Related]
28. Radiomics strategy for glioma grading using texture features from multiparametric MRI.
Tian Q; Yan LF; Zhang X; Zhang X; Hu YC; Han Y; Liu ZC; Nan HY; Sun Q; Sun YZ; Yang Y; Yu Y; Zhang J; Hu B; Xiao G; Chen P; Tian S; Xu J; Wang W; Cui GB
J Magn Reson Imaging; 2018 Dec; 48(6):1518-1528. PubMed ID: 29573085
[TBL] [Abstract][Full Text] [Related]
29. Use of magnetic resonance imaging in evaluation of placental invasion.
Teo TH; Law YM; Tay KH; Tan BS; Cheah FK
Clin Radiol; 2009 May; 64(5):511-6. PubMed ID: 19348847
[TBL] [Abstract][Full Text] [Related]
30. MRI in the diagnosis and surgical management of abnormal placentation.
Palacios-Jaraquemada JM; Bruno CH; Martín E
Acta Obstet Gynecol Scand; 2013 Apr; 92(4):392-7. PubMed ID: 22881062
[TBL] [Abstract][Full Text] [Related]
31. Prenatal ultrasound diagnosis and outcome of placenta previa accreta after cesarean delivery: a systematic review and meta-analysis.
Jauniaux E; Bhide A
Am J Obstet Gynecol; 2017 Jul; 217(1):27-36. PubMed ID: 28268196
[TBL] [Abstract][Full Text] [Related]
32. Placental evaluation with magnetic resonance.
Allen BC; Leyendecker JR
Radiol Clin North Am; 2013 Nov; 51(6):955-66. PubMed ID: 24210438
[TBL] [Abstract][Full Text] [Related]
33. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.
Liang M; Cai Z; Zhang H; Huang C; Meng Y; Zhao L; Li D; Ma X; Zhao X
Acad Radiol; 2019 Nov; 26(11):1495-1504. PubMed ID: 30711405
[TBL] [Abstract][Full Text] [Related]
34. An investigation of machine learning methods in delta-radiomics feature analysis.
Chang Y; Lafata K; Sun W; Wang C; Chang Z; Kirkpatrick JP; Yin FF
PLoS One; 2019; 14(12):e0226348. PubMed ID: 31834910
[TBL] [Abstract][Full Text] [Related]
35. [Accuracy of placenta accreta prenatal diagnosis by ultrasound and MRI in a high-risk population].
Daney de Marcillac F; Molière S; Pinton A; Weingertner AS; Fritz G; Viville B; Roedlich MN; Gaudineau A; Sananes N; Favre R; Nisand I; Langer B
J Gynecol Obstet Biol Reprod (Paris); 2016 Feb; 45(2):198-206. PubMed ID: 26321608
[TBL] [Abstract][Full Text] [Related]
36. Reliability of Magnetic Resonance Imaging in diagnosis and assessment the depth of invasion of placental accreta in high risk gravid women.
Khalaf LMR; Zeid HA; Othman ER
Clin Imaging; 2019; 58():5-11. PubMed ID: 31212062
[TBL] [Abstract][Full Text] [Related]
37. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.
Shayesteh S; Nazari M; Salahshour A; Sandoughdaran S; Hajianfar G; Khateri M; Yaghobi Joybari A; Jozian F; Fatehi Feyzabad SH; Arabi H; Shiri I; Zaidi H
Med Phys; 2021 Jul; 48(7):3691-3701. PubMed ID: 33894058
[TBL] [Abstract][Full Text] [Related]
38. Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI.
Kocak B; Durmaz ES; Kadioglu P; Polat Korkmaz O; Comunoglu N; Tanriover N; Kocer N; Islak C; Kizilkilic O
Eur Radiol; 2019 Jun; 29(6):2731-2739. PubMed ID: 30506213
[TBL] [Abstract][Full Text] [Related]
39. Predicting the risk of fetal growth restriction by radiomics analysis of the placenta on T2WI: A retrospective case-control study.
Song F; Li R; Lin J; Lv M; Qian Z; Wang L; Wu W
Placenta; 2023 Mar; 134():15-22. PubMed ID: 36863127
[TBL] [Abstract][Full Text] [Related]
40. A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.
Wang H; Wang Y; Zhang H; Yin X; Wang C; Lu Y; Song Y; Zhu H; Yang G
J Magn Reson Imaging; 2024 Feb; 59(2):483-493. PubMed ID: 37177832
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
