Literature DB >> 30957321

Head-to-head comparison between multiparametric MRI, the partin tables, memorial sloan kettering cancer center nomogram, and CAPRA score in predicting extraprostatic cancer in patients undergoing radical prostatectomy.

Elisa Zanelli1, Gianluca Giannarini2, Lorenzo Cereser1, Chiara Zuiani1, Giuseppe Como1, Stefano Pizzolitto3, Alessandro Crestani2, Claudio Valotto2, Vincenzo Ficarra4, Rossano Girometti1.   

Abstract

BACKGROUND: It is unclear whether clinical models including the Partin tables (PT), the Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and the cancer of the prostate risk assessment (CAPRA) can benefit from incorporating multiparametric magnetic resonance imaging (mpMRI) when staging prostate cancer (PCa).
PURPOSE: To compare the accuracy of clinical models, mpMRI, and mpMRI plus clinical models in predicting stage ≥pT3 of PCa. STUDY TYPE: Prospective monocentric cohort study. POPULATION: Seventy-three patients who underwent radical prostatectomy between 2016-2018. FIELD STRENGTH/SEQUENCE: 3.0T using turbo spin echo (TSE) imaging, single-shot echoplanar diffusion-weighted imaging, and T1 -weighted high-resolution-isotropic-volume-examination (THRIVE) contrast-enhanced imaging. ASSESSMENT: We calculated the probability of extraprostatic extension (EPE) using the PT and MSKCC, as well as the CAPRA score. Three readers with 2-8 years of experience in mpMRI independently staged PCa on imaging. STATISTICAL TESTS: Receiver operating characteristics analysis and logistic regression analysis to investigate the per-patient accuracy of mpMRI vs. clinical models vs. mpMRI plus clinical models in predicting stage ≥pT3. The alpha level was 0.05.
RESULTS: Median probability for EPE and MSKCCn was 27.3% and 47.0%, respectively. Median CAPRA score was 3. Stage ≥pT3 occurred in 32.9% of patients. Areas under the curve (AUCs) were 0.62 for PT, 0.62 for MSKCCn, 0.64 for CAPRA, and 0.73-0.75 for mpMRI (readers 1-3) (P > 0.05 for all comparisons). Compared with mpMRI, the combination of mpMRI with PT or MSKCCn provided lower AUCs (P > 0.05 for all the readers), while the combination with CAPRA provided significantly higher (P < 0.05) AUCs in the case of readers 1 and 3. On multivariable analysis, mpMRI by reader 1 was the only independent predictor of stage ≥pT3 (odds ratio 7.40). DATA
CONCLUSION: mpMRI was more accurate than clinical models and mpMRI plus clinical models in predicting stage ≥pT3, except for the combination of mpMRI and CAPRA in two out of three readers. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1604-1613.
© 2019 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  diagnostic accuracy; magnetic resonance imaging; nomograms; prostatic neoplasms

Year:  2019        PMID: 30957321     DOI: 10.1002/jmri.26743

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  9 in total

1.  Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study.

Authors:  Ying Hou; Yi-Hong Zhang; Jie Bao; Mei-Ling Bao; Guang Yang; Hai-Bin Shi; Yang Song; Yu-Dong Zhang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-21       Impact factor: 9.236

2.  Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative.

Authors:  Nnenaya Agochukwu-Mmonu; Adharsh Murali; Daniela Wittmann; Brian Denton; Rodney L Dunn; James Montie; James Peabody; David Miller; Karandeep Singh
Journal:  Eur Urol Open Sci       Date:  2022-04-18

3.  mEPE-score: a comprehensive grading system for predicting pathologic extraprostatic extension of prostate cancer at multiparametric magnetic resonance imaging.

Authors:  Marco Gatti; Riccardo Faletti; Francesco Gentile; Enrico Soncin; Giorgio Calleris; Alberto Fornari; Marco Oderda; Alessandro Serafini; Giulio Antonino Strazzarino; Elena Vissio; Laura Bergamasco; Stefano Cirillo; Mauro Giulio Papotti; Paolo Gontero; Paolo Fonio
Journal:  Eur Radiol       Date:  2022-03-15       Impact factor: 7.034

Review 4.  Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer.

Authors:  Niklas Harland; Arnulf Stenzl; Tilman Todenhöfer
Journal:  World J Mens Health       Date:  2020-06-24       Impact factor: 5.400

5.  External Validation of the Extraprostatic Extension Grade on MRI and Its Incremental Value to Clinical Models for Assessing Extraprostatic Cancer.

Authors:  Lili Xu; Gumuyang Zhang; Xiaoxiao Zhang; Xin Bai; Weigang Yan; Yu Xiao; Hao Sun; Zhengyu Jin
Journal:  Front Oncol       Date:  2021-04-01       Impact factor: 6.244

Review 6.  Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests.

Authors:  Alberto Casagrande; Francesco Fabris; Rossano Girometti
Journal:  Med Biol Eng Comput       Date:  2022-02-23       Impact factor: 2.602

7.  Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population.

Authors:  Artitaya Lophatananon; Matthew H V Byrne; Tristan Barrett; Anne Warren; Kenneth Muir; Ibifuro Dokubo; Fanos Georgiades; Mostafa Sheba; Lisa Bibby; Vincent J Gnanapragasam
Journal:  BMC Cancer       Date:  2022-08-11       Impact factor: 4.638

8.  MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer.

Authors:  Jun-Yi Xiang; Xiao-Shan Huang; Jian-Xia Xu; Ren-Hua Huang; Xiao-Zhong Zheng; Li-Ming Xue; Yu-Long Liu
Journal:  Biomed Res Int       Date:  2022-08-30       Impact factor: 3.246

9.  International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

Authors:  Andreas G Wibmer; Michael W Kattan; Francesco Alessandrino; Alexander D J Baur; Lars Boesen; Felipe Boschini Franco; David Bonekamp; Riccardo Campa; Hannes Cash; Violeta Catalá; Sebastien Crouzet; Sounil Dinnoo; James Eastham; Fiona M Fennessy; Kamyar Ghabili; Markus Hohenfellner; Angelique W Levi; Xinge Ji; Vibeke Løgager; Daniel J Margolis; Paul C Moldovan; Valeria Panebianco; Tobias Penzkofer; Philippe Puech; Jan Philipp Radtke; Olivier Rouvière; Heinz-Peter Schlemmer; Preston C Sprenkle; Clare M Tempany; Joan C Vilanova; Jeffrey Weinreb; Hedvig Hricak; Amita Shukla-Dave
Journal:  Cancers (Basel)       Date:  2021-05-27       Impact factor: 6.639

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.