Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics.

Gong, L; Xu, M; Fang, M; Zou, J; Yang, S; Yu, X; Xu, D; Zhou, L; Li, H; He, B; Wang, Y; Fang, X; Dong, D; Tian, J

Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics.

Gong, L Xu, M

Fang, M Zou, J Yang, S Yu, X Xu, D Zhou, L Li, H He, B Wang, Y Fang, X Dong, D Tian, J

Permalink

Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Journal of magnetic resonance imaging : JMRI, 2020, 52, (4), pp. 1102-1109
Issue Date:: 2020-10

Closed Access

	Filename	Description	Size
	jmri.27132.pdf		733.16 kB		View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Gong, L
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.contributor.author	Fang, M
dc.contributor.author	Zou, J
dc.contributor.author	Yang, S
dc.contributor.author	Yu, X
dc.contributor.author	Xu, D
dc.contributor.author	Zhou, L
dc.contributor.author	Li, H
dc.contributor.author	He, B
dc.contributor.author	Wang, Y
dc.contributor.author	Fang, X
dc.contributor.author	Dong, D
dc.contributor.author	Tian, J
dc.date.accessioned	2021-05-26T05:22:26Z
dc.date.available	2020-03-03
dc.date.available	2021-05-26T05:22:26Z
dc.date.issued	2020-10
dc.identifier.citation	Journal of magnetic resonance imaging : JMRI, 2020, 52, (4), pp. 1102-1109
dc.identifier.issn	1053-1807
dc.identifier.issn	1522-2586
dc.identifier.uri	http://hdl.handle.net/10453/149243
dc.description.abstract	<h4>Background</h4>Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision-making for prostate cancer (PCa). Treatment regimens between lower-grade (GS ≤7) and high-grade (GS >7) PCa differ largely and have great effects on cancer progression.<h4>Purpose</h4>To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high-grade PCa.<h4>Study type</h4>Retrospective.<h4>Population</h4>In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018.<h4>Field strength/sequence</h4>3.0T, pelvic phased-array coils, bpMRI including T<sub>2</sub> -weighted imaging (T<sub>2</sub> WI) and diffusion-weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI.<h4>Assessment</h4>The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal-Wallis test, the minimum redundancy-maximum relevance, and the sequential backward elimination algorithm. Two single-sequence radiomic (T<sub>2</sub> WI, DWI) and two combined (T<sub>2</sub> WI-DWI, T<sub>2</sub> WI-DWI-Clinic) models were respectively constructed and validated via logistic regression.<h4>Statistical tests</h4>The Kruskal-Wallis test and chi-squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models.<h4>Result</h4>All radiomic models showed significant (P < 0.001) predictive performances. Between the single-sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T<sub>2</sub> WI-DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924).<h4>Data conclusion</h4>Radiomics based on bpMRI can noninvasively identify high-grade PCa before the operation, which is helpful for individualized diagnosis of PCa.<h4>Level of evidence</h4>4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1102-1109.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Wiley
dc.relation.ispartof	Journal of magnetic resonance imaging : JMRI
dc.relation.isbasedon	10.1002/jmri.27132
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	02 Physical Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Nuclear Medicine & Medical Imaging
dc.subject.mesh	Humans
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Male
dc.subject.mesh	Neoplasm Grading
dc.subject.mesh	Prostatic Neoplasms
dc.subject.mesh	Retrospective Studies
dc.subject.mesh	Humans
dc.subject.mesh	Prostatic Neoplasms
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Retrospective Studies
dc.subject.mesh	Male
dc.subject.mesh	Neoplasm Grading
dc.subject.mesh	Humans
dc.subject.mesh	Magnetic Resonance Imaging
dc.subject.mesh	Male
dc.subject.mesh	Neoplasm Grading
dc.subject.mesh	Prostatic Neoplasms
dc.subject.mesh	Retrospective Studies
dc.title	Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics.
dc.type	Journal Article
utslib.citation.volume	52
utslib.location.activity	United States
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-26T05:22:24Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	52
utslib.citation.issue	4

Abstract:

Background

Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision-making for prostate cancer (PCa). Treatment regimens between lower-grade (GS ≤7) and high-grade (GS >7) PCa differ largely and have great effects on cancer progression.

Purpose

To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high-grade PCa.

Study type

Retrospective.

Population

In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018.

Field strength/sequence

3.0T, pelvic phased-array coils, bpMRI including T₂ -weighted imaging (T₂ WI) and diffusion-weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI.

Assessment

The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal-Wallis test, the minimum redundancy-maximum relevance, and the sequential backward elimination algorithm. Two single-sequence radiomic (T₂ WI, DWI) and two combined (T₂ WI-DWI, T₂ WI-DWI-Clinic) models were respectively constructed and validated via logistic regression.

Statistical tests

The Kruskal-Wallis test and chi-squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models.

Result

All radiomic models showed significant (P < 0.001) predictive performances. Between the single-sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T₂ WI-DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924).

Data conclusion

Radiomics based on bpMRI can noninvasively identify high-grade PCa before the operation, which is helpful for individualized diagnosis of PCa.

Level of evidence

4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1102-1109.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149243