187 related articles for article (PubMed ID: 36967794)
1. Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer.
Zhang M; Liu Y; Yao J; Wang K; Tu J; Hu Z; Jin Y; Du Y; Sun X; Chen L; Wang Z
Front Endocrinol (Lausanne); 2023; 14():1137322. PubMed ID: 36967794
[TBL] [Abstract][Full Text] [Related]
2. Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms.
Zhang X; Feng C; Bai X; Peng X; Guo Q; Chen L; Xue J
Arch Esp Urol; 2023 Sep; 76(7):494-503. PubMed ID: 37867334
[TBL] [Abstract][Full Text] [Related]
3. Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study.
Chiu PK; Shen X; Wang G; Ho CL; Leung CH; Ng CF; Choi KS; Teoh JY
Prostate Cancer Prostatic Dis; 2022 Apr; 25(4):672-676. PubMed ID: 34267331
[TBL] [Abstract][Full Text] [Related]
4. Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine.
Lee HJ; Hwang SI; Han SM; Park SH; Kim SH; Cho JY; Seong CG; Choe G
Eur Radiol; 2010 Jun; 20(6):1476-84. PubMed ID: 20016902
[TBL] [Abstract][Full Text] [Related]
5. Pre-operative prediction of advanced prostatic cancer using clinical decision support systems: accuracy comparison between support vector machine and artificial neural network.
Kim SY; Moon SK; Jung DC; Hwang SI; Sung CK; Cho JY; Kim SH; Lee J; Lee HJ
Korean J Radiol; 2011; 12(5):588-94. PubMed ID: 21927560
[TBL] [Abstract][Full Text] [Related]
6. Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?
Peng T; Xiao J; Li L; Pu B; Niu X; Zeng X; Wang Z; Gao C; Li C; Chen L; Yang J
Int J Comput Assist Radiol Surg; 2021 Dec; 16(12):2235-2249. PubMed ID: 34677748
[TBL] [Abstract][Full Text] [Related]
7. Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation.
Kan Y; Zhang Q; Hao J; Wang W; Zhuang J; Gao J; Huang H; Liang J; Marra G; Calleris G; Oderda M; Zhao X; Gontero P; Guo H
Eur Radiol; 2020 Nov; 30(11):6274-6284. PubMed ID: 32524222
[TBL] [Abstract][Full Text] [Related]
8. Prostate Cancer Risk Prediction and Online Calculation Based on Machine Learning Algorithm.
Wang C; Chang QX; Wang XM; Wang KY; Wang H; Cui Z; Li CP
Chin Med Sci J; 2022 Sep; 37(3):210-217. PubMed ID: 36321176
[TBL] [Abstract][Full Text] [Related]
9. Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis.
Lin YT; Chu CY; Hung KS; Lu CH; Bednarczyk EM; Chen HY
Comput Methods Programs Biomed; 2022 Oct; 225():107028. PubMed ID: 35930862
[TBL] [Abstract][Full Text] [Related]
10. Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy.
Jones S; Hargrave C; Deegan T; Holt T; Mengersen K
Med Phys; 2020 Apr; 47(4):1452-1459. PubMed ID: 31981427
[TBL] [Abstract][Full Text] [Related]
11. Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer.
Wang X; Zhang X; Li H; Zhang M; Liu Y; Li X
J Cancer Res Clin Oncol; 2023 Sep; 149(11):8759-8768. PubMed ID: 37127828
[TBL] [Abstract][Full Text] [Related]
12. Machine Learning-Based Models Enhance the Prediction of Prostate Cancer.
Chen S; Jian T; Chi C; Liang Y; Liang X; Yu Y; Jiang F; Lu J
Front Oncol; 2022; 12():941349. PubMed ID: 35875103
[TBL] [Abstract][Full Text] [Related]
13. An artificial neural network for prostate cancer staging when serum prostate specific antigen is 10 ng./ml. or less.
Zlotta AR; Remzi M; Snow PB; Schulman CC; Marberger M; Djavan B
J Urol; 2003 May; 169(5):1724-8. PubMed ID: 12686818
[TBL] [Abstract][Full Text] [Related]
14. Mapping the spatial distribution of the dengue vector
Rahman MS; Pientong C; Zafar S; Ekalaksananan T; Paul RE; Haque U; Rocklöv J; Overgaard HJ
One Health; 2021 Dec; 13():100358. PubMed ID: 34934797
[TBL] [Abstract][Full Text] [Related]
15. Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.
Yu S; Tao J; Dong B; Fan Y; Du H; Deng H; Cui J; Hong G; Zhang X
BMC Urol; 2021 May; 21(1):80. PubMed ID: 33993876
[TBL] [Abstract][Full Text] [Related]
16. Machine learning prediction of prostate cancer from transrectal ultrasound video clips.
Wang K; Chen P; Feng B; Tu J; Hu Z; Zhang M; Yang J; Zhan Y; Yao J; Xu D
Front Oncol; 2022; 12():948662. PubMed ID: 36091110
[TBL] [Abstract][Full Text] [Related]
17. Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics.
Wildeboer RR; Mannaerts CK; van Sloun RJG; Budäus L; Tilki D; Wijkstra H; Salomon G; Mischi M
Eur Radiol; 2020 Feb; 30(2):806-815. PubMed ID: 31602512
[TBL] [Abstract][Full Text] [Related]
18. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women.
Shim JG; Kim DW; Ryu KH; Cho EA; Ahn JH; Kim JI; Lee SH
Arch Osteoporos; 2020 Oct; 15(1):169. PubMed ID: 33097976
[TBL] [Abstract][Full Text] [Related]
19. Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients.
Aussavavirojekul P; Hoonlor A; Srinualnad S
Prostate; 2022 Feb; 82(2):235-244. PubMed ID: 34783053
[TBL] [Abstract][Full Text] [Related]
20. An artificial neural network to predict the outcome of repeat prostate biopsies.
Remzi M; Anagnostou T; Ravery V; Zlotta A; Stephan C; Marberger M; Djavan B
Urology; 2003 Sep; 62(3):456-60. PubMed ID: 12946746
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
