Dataset Information

A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

ABSTRACT:

Objectives

To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).

Materials and methods

This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis.

Results

In the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66-0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52-0.73]).

Conclusions

A DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%.

Key points

• Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa. • Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. • Applying deep learning to an auto-fixed VOI radiomics approach can be valuable.

SUBMITTER: Bleker J

PROVIDER: S-EPMC9381625 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

Bleker Jeroen J Kwee Thomas C TC Rouw Dennis D Roest Christian C Borstlap Jaap J de Jong Igle Jan IJ Dierckx Rudi A J O RAJO Huisman Henkjan H Yakar Derya D

European radiology 20220414 9

<h4>Objectives</h4>To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).<h4>Materials and methods</h4>This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented wi ...[more]

PMID: 35420303

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Dataset Information

A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

Objectives

Materials and methods

Results

Conclusions

Key points

Publications

A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.

Similar Datasets

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