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Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists

Antonelli, Michela, Johnston, Edward W., Dikaios, Nikolaos, Cheung, King K., Sidhu, Harbir S., Appayya, Mrishta B., Giganti, Francesco, Simmons, Lucy A. M., Freeman, Alex, Allen, Clare , Ahmed, Hashim U., Atkinson, David, Ourselin, Sebastien and Punwani, Shonit (2019) Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists European Radiology.

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Abstract

Objective

The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists.

Methods

A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists.

Results

The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82).

Conclusions

Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists.

Key Points

• Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific.

• Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists.

• Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Antonelli, Michela
Johnston, Edward W.
Dikaios, Nikolaosn.dikaios@surrey.ac.uk
Cheung, King K.
Sidhu, Harbir S.
Appayya, Mrishta B.
Giganti, Francesco
Simmons, Lucy A. M.
Freeman, Alex
Allen, Clare
Ahmed, Hashim U.
Atkinson, David
Ourselin, Sebastien
Punwani, Shonit
Date : 11 June 2019
Funders : National Institute for Health Research (NIHR)
DOI : 10.1007/s00330-019-06244-2
Copyright Disclaimer : © The Author(s) 2019. Open Access. 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 : Machine learning; Prostate cancer; Magnetic resonance imaging; Gleason score; Diagnosis, computer-assisted
Depositing User : Clive Harris
Date Deposited : 20 Jun 2019 07:51
Last Modified : 20 Jun 2019 07:51
URI: http://epubs.surrey.ac.uk/id/eprint/852054

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