Detalhe da pesquisa
1.
Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI.
Radiology
; 307(4): e222276, 2023 05.
Artigo
em Inglês
| MEDLINE | ID: mdl-37039688
2.
Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer.
Eur Radiol
; 30(8): 4262-4271, 2020 Aug.
Artigo
em Inglês
| MEDLINE | ID: mdl-32219507
3.
Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI.
Eur J Radiol
; 166: 110964, 2023 Sep.
Artigo
em Inglês
| MEDLINE | ID: mdl-37453274
4.
MRI-targeted biopsy cores from prostate index lesions: assessment and prediction of the number needed.
Prostate Cancer Prostatic Dis
; 26(3): 543-551, 2023 09.
Artigo
em Inglês
| MEDLINE | ID: mdl-36209237
5.
Inter-Reader Variability Using PI-RADS v2 Versus PI-RADS v2.1: Most New Disagreement Stems from Scores 1 and 2.
Rofo
; 194(8): 852-861, 2022 08.
Artigo
em Inglês
| MEDLINE | ID: mdl-35545106
6.
Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer.
Sci Rep
; 10(1): 15982, 2020 09 29.
Artigo
em Inglês
| MEDLINE | ID: mdl-32994502
7.
Optimizing size thresholds for detection of clinically significant prostate cancer on MRI: Peripheral zone cancers are smaller and more predictable than transition zone tumors.
Eur J Radiol
; 129: 109071, 2020 Aug.
Artigo
em Inglês
| MEDLINE | ID: mdl-32531720