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Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer

Dikaios, Nikolaos, Giganti, Francesco, Sidhu, Harbir S, Johnston, Edward W., Appayya, Mrishta B., Simmons, Lucy, Freeman, Alex, Ahmed, Hashim U., Atkinson, David and Punwani, Shonit (2018) Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer European Radiology.

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Abstract

Objectives

Compare the performance of zone-specific multi-parametric-MRI (mp-MRI) diagnostic models in prostate cancer detection with experienced radiologists.

Methods

A single-centre, IRB approved, prospective STARD compliant 3 T MRI test dataset of 203 patients was generated to test validity and generalisability of previously reported 1.5 T mp-MRI diagnostic models. All patients included within the test dataset underwent 3 T mp-MRI, comprising T2, diffusion-weighted and dynamic contrast-enhanced imaging followed by transperineal template ± targeted index lesion biopsy. Separate diagnostic models (transition zone (TZ) and peripheral zone (PZ)) were applied to respective zones. Sensitivity/specificity and the area under the receiver operating characteristic curve (ROC-AUC) were calculated for the two zone-specific models. Two radiologists (A and B) independently Likert scored test 3 T mp-MRI dataset, allowing ROC analysis for each radiologist for each prostate zone.

Results

Diagnostic models applied to the test dataset demonstrated a ROC-AUC = 0.74 (95% CI 0.67–0.81) in the PZ and 0.68 (95% CI 0.61–0.75) in the TZ. Radiologist A/B had a ROC-AUC = 0.78/0.74 in the PZ and 0.69/0.69 in the TZ. Radiologists A and B each scored 51 patients in the PZ and 41 and 45 patients respectively in the TZ as Likert 3. The PZ model demonstrated a ROC-AUC = 0.65/0.67 for the patients Likert scored as indeterminate by radiologist A/B respectively, whereas the TZ model demonstrated a ROC-AUC = 0.74/0.69.

Conclusion

Zone-specific mp-MRI diagnostic models demonstrate generalisability between 1.5 and 3 T mp-MRI protocols and show similar classification performance to experienced radiologists for prostate cancer detection. Results also indicate the ability of diagnostic models to classify cases with an indeterminate radiologist score.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Dikaios, Nikolaosn.dikaios@surrey.ac.uk
Giganti, Francesco
Sidhu, Harbir S
Johnston, Edward W.
Appayya, Mrishta B.
Simmons, Lucy
Freeman, Alex
Ahmed, Hashim U.
Atkinson, David
Punwani, Shonit
Date : 19 November 2018
DOI : 10.1007/s00330-018-5799-y
Copyright Disclaimer : Copyright The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:/ / creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords : Magnetic resonance imaging; Prostatic neoplasms; Diagnosis; Computer-assisted; Logistic models
Depositing User : Melanie Hughes
Date Deposited : 21 Nov 2018 08:58
Last Modified : 21 Nov 2018 08:58
URI: http://epubs.surrey.ac.uk/id/eprint/849914

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