Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis-Reference-Cited by-同舟云学术

Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis

Published:2021-08-31 Issue:12 Volume:46 Page:5647-5658
ISSN:2366-004X
Container-title:Abdominal Radiology
language:en
Short-container-title:Abdom Radiol

Author:

Lim Christopher S.^ORCID,Abreu-Gomez Jorge,Thornhill Rebecca,James Nick,Al Kindi Ahmed,Lim Andrew S.,Schieda Nicola

Publisher

Springer Science and Business Media LLC

Subject

Urology,Gastroenterology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

https://link.springer.com/content/pdf/10.1007/s00261-021-03235-0.pdf

Reference53 articles.

1. Turkbey B, Rosenkrantz AB, Haider MA et al (2019) Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 76:340-351

2. Schieda N, Lim CS, Zabihollahy F et al (2021) Quantitative Prostate MRI. Journal of Magnetic Resonance Imaging 53:1632-1645

3. Purysko AS, Baroni RH, Giganti F et al (2020) PI-RADS Version 2.1: A Critical Review, From the AJR Special Series on Radiology Reporting and Data Systems. American Journal of Roentgenology 216:20-32

4. Westphalen AC, McCulloch CE, Anaokar JM et al (2020) Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology 296:76-84

5. Barkovich EJ, Shankar PR, Westphalen AC (2019) A Systematic Review of the Existing Prostate Imaging Reporting and Data System Version 2 (PI-RADSv2) Literature and Subset Meta-Analysis of PI-RADSv2 Categories Stratified by Gleason Scores. AJR Am J Roentgenol 212:847-854

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