A deep learning masked segmentation alternative to manual segmentation in bi-parametric MRI prostate cancer radiomics
| Autor: | Jeroen Bleker, Thomas C. Kwee, Dennis Rouw, Christian Roest, Jaap Borstlap, Igle Jan de Jong, Rudi A. J. O. Dierckx, Henkjan Huisman, Derya Yakar |
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| Přispěvatelé: | Guided Treatment in Optimal Selected Cancer Patients (GUTS), Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Molecular Neuroscience and Ageing Research (MOLAR) |
| Předmět: |
Male
Data curation Prostate Deep learning General Medicine Magnetic Resonance Imaging Multi-center study Diffusion Magnetic Resonance Imaging Urological cancers Radboud Institute for Health Sciences [Radboudumc 15] Humans Radiology Nuclear Medicine and imaging Prostatic neoplasms Biomarkers Retrospective Studies |
| Zdroj: | University of Groningen ECR 2021 European Radiology, 32. SPRINGER European Radiology, 32, 9, pp. 6526-6535 European Radiology, 32, 6526-6535 |
| ISSN: | 0938-7994 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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