External validation of novel magnetic resonance imaging-based models for prostate cancer prediction

2019 ◽  
Vol 125 (3) ◽  
pp. 407-416 ◽  
Author(s):  
Lukas Püllen ◽  
Jan P. Radtke ◽  
Manuel Wiesenfarth ◽  
Monique J. Roobol ◽  
Jan F.M. Verbeek ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
External Validation ◽  
Resonance Imaging ◽  
Cancer Prediction

scholarly journals Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0159803 ◽  
Author(s):  
Matthias C. Roethke ◽  
Timur H. Kuru ◽  
Maya B. Mueller-Wolf ◽  
Erik Agterhuis ◽  
Christopher Edler ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Automated Analysis ◽  
Analysis Tool ◽  
Resonance Imaging ◽  
Cancer Prediction ◽  
Multiparametric Magnetic Resonance Imaging

scholarly journals Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 959
Author(s):  
Jasper J. Twilt ◽  
Kicky G. van Leeuwen ◽  
Henkjan J. Huisman ◽  
Jurgen J. Fütterer ◽  
Maarten de Rooij
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
External Validation ◽  
Lesion Detection ◽  
Resonance Imaging ◽  
Clinical Workflow ◽  
The Impact ◽  
Lesion Classification

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

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