289 related articles for article (PubMed ID: 34677748)
1. 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]
2. Transition zone prostate cancer: Logistic regression and machine-learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis.
Wu M; Krishna S; Thornhill RE; Flood TA; McInnes MDF; Schieda N
J Magn Reson Imaging; 2019 Sep; 50(3):940-950. PubMed ID: 30701625
[TBL] [Abstract][Full Text] [Related]
3. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
Tahmassebi A; Wengert GJ; Helbich TH; Bago-Horvath Z; Alaei S; Bartsch R; Dubsky P; Baltzer P; Clauser P; Kapetas P; Morris EA; Meyer-Baese A; Pinker K
Invest Radiol; 2019 Feb; 54(2):110-117. PubMed ID: 30358693
[TBL] [Abstract][Full Text] [Related]
4. Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation.
Ji X; Zhang J; Shi W; He D; Bao J; Wei X; Huang Y; Liu Y; Chen JC; Gao X; Tang Y; Xia W
Phys Eng Sci Med; 2021 Sep; 44(3):745-754. PubMed ID: 34075559
[TBL] [Abstract][Full Text] [Related]
5. A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients.
Xinyang S; Shuang Z; Tianci S; Xiangyu H; Yangyang W; Mengying D; Jingran Z; Feng Y
Magn Reson Imaging; 2024 Apr; 107():15-23. PubMed ID: 38181835
[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. Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI.
Iyama Y; Nakaura T; Katahira K; Iyama A; Nagayama Y; Oda S; Utsunomiya D; Yamashita Y
Eur Radiol; 2017 Sep; 27(9):3600-3608. PubMed ID: 28289941
[TBL] [Abstract][Full Text] [Related]
8. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.
Bernatz S; Ackermann J; Mandel P; Kaltenbach B; Zhdanovich Y; Harter PN; Döring C; Hammerstingl R; Bodelle B; Smith K; Bucher A; Albrecht M; Rosbach N; Basten L; Yel I; Wenzel M; Bankov K; Koch I; Chun FK; Köllermann J; Wild PJ; Vogl TJ
Eur Radiol; 2020 Dec; 30(12):6757-6769. PubMed ID: 32676784
[TBL] [Abstract][Full Text] [Related]
9. Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.
Bonekamp D; Kohl S; Wiesenfarth M; Schelb P; Radtke JP; Götz M; Kickingereder P; Yaqubi K; Hitthaler B; Gählert N; Kuder TA; Deister F; Freitag M; Hohenfellner M; Hadaschik BA; Schlemmer HP; Maier-Hein KH
Radiology; 2018 Oct; 289(1):128-137. PubMed ID: 30063191
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Prediction of prostate cancer aggressiveness with a combination of radiomics and machine learning-based analysis of dynamic contrast-enhanced MRI.
Liu B; Cheng J; Guo DJ; He XJ; Luo YD; Zeng Y; Li CM
Clin Radiol; 2019 Nov; 74(11):896.e1-896.e8. PubMed ID: 31495546
[TBL] [Abstract][Full Text] [Related]
14. Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer.
Ma L; Zhou Q; Yin H; Ang X; Li Y; Xie G; Li G
Transl Cancer Res; 2022 May; 11(5):1146-1161. PubMed ID: 35706813
[TBL] [Abstract][Full Text] [Related]
15. An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study.
Hiremath A; Shiradkar R; Fu P; Mahran A; Rastinehad AR; Tewari A; Tirumani SH; Purysko A; Ponsky L; Madabhushi A
Lancet Digit Health; 2021 Jul; 3(7):e445-e454. PubMed ID: 34167765
[TBL] [Abstract][Full Text] [Related]
16. Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring.
Singh D; Kumar V; Das CJ; Singh A; Mehndiratta A
NMR Biomed; 2021 Jun; 34(6):e4495. PubMed ID: 33638244
[TBL] [Abstract][Full Text] [Related]
17. Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies.
Hussain L; Saeed S; Awan IA; Idris A; Nadeem MSA; Chaudhry QU
Curr Med Imaging Rev; 2019; 15(6):595-606. PubMed ID: 32008569
[TBL] [Abstract][Full Text] [Related]
18. Comparison of quantitative apparent diffusion coefficient parameters with prostate imaging reporting and data system V2 assessment for detection of clinically significant peripheral zone prostate cancer.
Hassanzadeh E; Alessandrino F; Olubiyi OI; Glazer DI; Mulkern RV; Fedorov A; Tempany CM; Fennessy FM
Abdom Radiol (NY); 2018 May; 43(5):1237-1244. PubMed ID: 28840280
[TBL] [Abstract][Full Text] [Related]
19. Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance.
Sushentsev N; Rundo L; Blyuss O; Nazarenko T; Suvorov A; Gnanapragasam VJ; Sala E; Barrett T
Eur Radiol; 2022 Jan; 32(1):680-689. PubMed ID: 34255161
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
