170 related articles for article (PubMed ID: 36249048)
1. Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network.
Zhu L; Gao G; Zhu Y; Han C; Liu X; Li D; Liu W; Wang X; Zhang J; Zhang X; Wang X
Front Oncol; 2022; 12():958065. PubMed ID: 36249048
[TBL] [Abstract][Full Text] [Related]
2. Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI.
Sun Z; Wu P; Cui Y; Liu X; Wang K; Gao G; Wang H; Zhang X; Wang X
J Magn Reson Imaging; 2023 Oct; 58(4):1067-1081. PubMed ID: 36825823
[TBL] [Abstract][Full Text] [Related]
3. A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging.
Mehralivand S; Yang D; Harmon SA; Xu D; Xu Z; Roth H; Masoudi S; Sanford TH; Kesani D; Lay NS; Merino MJ; Wood BJ; Pinto PA; Choyke PL; Turkbey B
Acad Radiol; 2022 Aug; 29(8):1159-1168. PubMed ID: 34598869
[TBL] [Abstract][Full Text] [Related]
4. Utility of Clinical-Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions.
Jin P; Yang L; Qiao X; Hu C; Hu C; Wang X; Bao J
Front Oncol; 2022; 12():840786. PubMed ID: 35280813
[TBL] [Abstract][Full Text] [Related]
5. Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.
Hiremath A; Shiradkar R; Merisaari H; Prasanna P; Ettala O; Taimen P; Aronen HJ; Boström PJ; Jambor I; Madabhushi A
Eur Radiol; 2021 Jan; 31(1):379-391. PubMed ID: 32700021
[TBL] [Abstract][Full Text] [Related]
6. Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer.
Xu L; Zhang G; Shi B; Liu Y; Zou T; Yan W; Xiao Y; Xue H; Feng F; Lei J; Jin Z; Sun H
Cancer Imaging; 2019 Dec; 19(1):90. PubMed ID: 31864408
[TBL] [Abstract][Full Text] [Related]
7. Which clinical and radiological characteristics can predict clinically significant prostate cancer in PI-RADS 3 lesions? A retrospective study in a high-volume academic center.
Hermie I; Van Besien J; De Visschere P; Lumen N; Decaestecker K
Eur J Radiol; 2019 May; 114():92-98. PubMed ID: 31005183
[TBL] [Abstract][Full Text] [Related]
8. Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets.
Zabihollahy F; Schieda N; Krishna Jeyaraj S; Ukwatta E
Med Phys; 2019 Jul; 46(7):3078-3090. PubMed ID: 31002381
[TBL] [Abstract][Full Text] [Related]
9. Evaluation of Weighted Diffusion Subtraction for Detection of Clinically Significant Prostate Cancer.
Sato T; Isoda H; Fujimoto K; Furuta A; Fujimoto M; Ito K; Kobayashi T; Nakamoto Y
J Magn Reson Imaging; 2021 Dec; 54(6):1979-1988. PubMed ID: 34085328
[TBL] [Abstract][Full Text] [Related]
10. Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet.
Chen Y; Xing L; Yu L; Bagshaw HP; Buyyounouski MK; Han B
Med Phys; 2020 Dec; 47(12):6421-6429. PubMed ID: 33012016
[TBL] [Abstract][Full Text] [Related]
11. Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.
Hectors SJ; Chen C; Chen J; Wang J; Gordon S; Yu M; Al Hussein Al Awamlh B; Sabuncu MR; Margolis DJA; Hu JC
J Magn Reson Imaging; 2021 Nov; 54(5):1466-1473. PubMed ID: 33970516
[TBL] [Abstract][Full Text] [Related]
12. Effects of the addition of quantitative apparent diffusion coefficient data on the diagnostic performance of the PI-RADS v2 scoring system to detect clinically significant prostate cancer.
Moraes MO; Roman DHH; Copetti J; de S Santos F; Agra A; Noronha JAP; Carvalhal G; Neto EJD; Zanon M; Baldisserotto M; Hochhegger B
World J Urol; 2020 Apr; 38(4):981-991. PubMed ID: 31175458
[TBL] [Abstract][Full Text] [Related]
13. Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method.
Zabihollahy F; Ukwatta E; Krishna S; Schieda N
J Magn Reson Imaging; 2020 Apr; 51(4):1223-1234. PubMed ID: 31456317
[TBL] [Abstract][Full Text] [Related]
14. Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making.
Pan JF; Su R; Cao JZ; Zhao ZY; Ren DW; Ye SZ; Huang RD; Tao ZL; Yu CL; Jiang JH; Ma Q
Front Oncol; 2021; 11():740868. PubMed ID: 34589437
[TBL] [Abstract][Full Text] [Related]
15. Prospective PI-RADS v2.1 Atypical Benign Prostatic Hyperplasia Nodules With Marked Restricted Diffusion: Detection of Clinically Significant Prostate Cancer on Multiparametric MRI.
Costa DN; Jia L; Subramanian N; Xi Y; Rofsky NM; Recchimuzzi DZ; de Leon AD; Arraj P; Pedrosa I
AJR Am J Roentgenol; 2021 Aug; 217(2):395-403. PubMed ID: 32876473
[No Abstract] [Full Text] [Related]
16. Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets.
Gresser E; Schachtner B; Stüber AT; Solyanik O; Schreier A; Huber T; Froelich MF; Magistro G; Kretschmer A; Stief C; Ricke J; Ingrisch M; Nörenberg D
Quant Imaging Med Surg; 2022 Nov; 12(11):4990-5003. PubMed ID: 36330197
[TBL] [Abstract][Full Text] [Related]
17. MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features.
Dominguez I; Rios-Ibacache O; Caprile P; Gonzalez J; San Francisco IF; Besa C
Diagnostics (Basel); 2023 Aug; 13(17):. PubMed ID: 37685317
[TBL] [Abstract][Full Text] [Related]
18. Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment.
Schelb P; Kohl S; Radtke JP; Wiesenfarth M; Kickingereder P; Bickelhaupt S; Kuder TA; Stenzinger A; Hohenfellner M; Schlemmer HP; Maier-Hein KH; Bonekamp D
Radiology; 2019 Dec; 293(3):607-617. PubMed ID: 31592731
[TBL] [Abstract][Full Text] [Related]
19. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.
Lin YC; Lin CH; Lu HY; Chiang HJ; Wang HK; Huang YT; Ng SH; Hong JH; Yen TC; Lai CH; Lin G
Eur Radiol; 2020 Mar; 30(3):1297-1305. PubMed ID: 31712961
[TBL] [Abstract][Full Text] [Related]
20. Fully automated detection of prostate transition zone tumors on T2-weighted and apparent diffusion coefficient (ADC) map MR images using U-Net ensemble.
Wong T; Schieda N; Sathiadoss P; Haroon M; Abreu-Gomez J; Ukwatta E
Med Phys; 2021 Nov; 48(11):6889-6900. PubMed ID: 34418108
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]