BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

202 related articles for article (PubMed ID: 31187216)

  • 1. Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.
    Antonelli M; Johnston EW; Dikaios N; Cheung KK; Sidhu HS; Appayya MB; Giganti F; Simmons LAM; Freeman A; Allen C; Ahmed HU; Atkinson D; Ourselin S; Punwani S
    Eur Radiol; 2019 Sep; 29(9):4754-4764. PubMed ID: 31187216
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer.
    Dikaios N; Giganti F; Sidhu HS; Johnston EW; Appayya MB; Simmons L; Freeman A; Ahmed HU; Atkinson D; Punwani S
    Eur Radiol; 2019 Aug; 29(8):4150-4159. PubMed ID: 30456585
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI.
    Dikaios N; Alkalbani J; Abd-Alazeez M; Sidhu HS; Kirkham A; Ahmed HU; Emberton M; Freeman A; Halligan S; Taylor S; Atkinson D; Punwani S
    Eur Radiol; 2015 Sep; 25(9):2727-37. PubMed ID: 25680730
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.
    Ginsburg SB; Algohary A; Pahwa S; Gulani V; Ponsky L; Aronen HJ; Boström PJ; Böhm M; Haynes AM; Brenner P; Delprado W; Thompson J; Pulbrock M; Taimen P; Villani R; Stricker P; Rastinehad AR; Jambor I; Madabhushi A
    J Magn Reson Imaging; 2017 Jul; 46(1):184-193. PubMed ID: 27990722
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes.
    Abreu-Gomez J; Walker D; Alotaibi T; McInnes MDF; Flood TA; Schieda N
    Eur Radiol; 2020 Aug; 30(8):4251-4261. PubMed ID: 32211965
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Evaluation of Diffusion Kurtosis Imaging Versus Standard Diffusion Imaging for Detection and Grading of Peripheral Zone Prostate Cancer.
    Roethke MC; Kuder TA; Kuru TH; Fenchel M; Hadaschik BA; Laun FB; Schlemmer HP; Stieltjes B
    Invest Radiol; 2015 Aug; 50(8):483-9. PubMed ID: 25867657
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prospective Inclusion of Apparent Diffusion Coefficients in Multiparametric Prostate MRI Structured Reports: Discrimination of Clinically Insignificant and Significant Cancers.
    Costa DN; Xi Y; Aziz M; Passoni N; Shakir N; Goldberg K; Francis F; Roehrborn CG; Leon AD; Pedrosa I
    AJR Am J Roentgenol; 2019 Jan; 212(1):109-116. PubMed ID: 30383404
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Apparent Diffusion Coefficient Values of the Benign Central Zone of the Prostate: Comparison With Low- and High-Grade Prostate Cancer.
    Gupta RT; Kauffman CR; Garcia-Reyes K; Palmeri ML; Madden JF; Polascik TJ; Rosenkrantz AB
    AJR Am J Roentgenol; 2015 Aug; 205(2):331-6. PubMed ID: 26204283
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Diagnostic evaluation of diffusion kurtosis imaging for prostate cancer: Detection in a biopsy population.
    Ding K; Yao Y; Gao Y; Lu X; Chen H; Tang Q; Hua C; Zhou M; Zou X; Yin Q
    Eur J Radiol; 2019 Sep; 118():138-146. PubMed ID: 31439233
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. High spatiotemporal resolution dynamic contrast-enhanced MRI improves the image-based discrimination of histopathology risk groups of peripheral zone prostate cancer: a supervised machine learning approach.
    Winkel DJ; Breit HC; Block TK; Boll DT; Heye TJ
    Eur Radiol; 2020 Sep; 30(9):4828-4837. PubMed ID: 32328763
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Characterization of Prostate Cancer with Gleason Score of at Least 7 by Using Quantitative Multiparametric MR Imaging: Validation of a Computer-aided Diagnosis System in Patients Referred for Prostate Biopsy.
    Dinh AH; Melodelima C; Souchon R; Moldovan PC; Bratan F; Pagnoux G; Mège-Lechevallier F; Ruffion A; Crouzet S; Colombel M; Rouvière O
    Radiology; 2018 May; 287(2):525-533. PubMed ID: 29361244
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. The performance of intravoxel-incoherent motion diffusion-weighted imaging derived hypoxia for the risk stratification of prostate cancer in peripheral zone.
    Chen Z; Xue Y; Zhang Z; Li W; Wen M; Zhao Y; Li J; Weng Z; Ye Q
    Eur J Radiol; 2020 Apr; 125():108865. PubMed ID: 32058895
    [TBL] [Abstract][Full Text] [Related]  

  • 17. VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient.
    Johnston EW; Bonet-Carne E; Ferizi U; Yvernault B; Pye H; Patel D; Clemente J; Piga W; Heavey S; Sidhu HS; Giganti F; O'Callaghan J; Brizmohun Appayya M; Grey A; Saborowska A; Ourselin S; Hawkes D; Moore CM; Emberton M; Ahmed HU; Whitaker H; Rodriguez-Justo M; Freeman A; Atkinson D; Alexander D; Panagiotaki E; Punwani S
    Radiology; 2019 May; 291(2):391-397. PubMed ID: 30938627
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient.
    Donati OF; Mazaheri Y; Afaq A; Vargas HA; Zheng J; Moskowitz CS; Hricak H; Akin O
    Radiology; 2014 Apr; 271(1):143-52. PubMed ID: 24475824
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer.
    Rosenkrantz AB; Sigmund EE; Johnson G; Babb JS; Mussi TC; Melamed J; Taneja SS; Lee VS; Jensen JH
    Radiology; 2012 Jul; 264(1):126-35. PubMed ID: 22550312
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.