A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics.
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).Ma...
| Publicado en: | European Radiology Vol. 32; no. 9; pp. 6526 - 6536 |
|---|---|
| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158547004&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158547004 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2022 vid: 32 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158547004 158547004 NLM35420303 158547004 10.1007/s00330-022-08712-8 NLM35420303 158547004 ppf: 6526 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics. aug: au: Bleker, Jeroen Kwee, Thomas C. Rouw, Dennis Roest, Christian Borstlap, Jaap de Jong, Igle Jan Dierckx, Rudi A. J. O. Huisman, Henkjan Yakar, Derya affil: Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Meditech Building, Room 305, Hanzeplein 1, 9700 RB, Groningen, The Netherlands sug: subj: Prostatic Neoplasms Prostatic Neoplasms Pathology Male Human Magnetic Resonance Imaging Methods Prostate Retrospective Design Prostate Pathology Comparative Studies Multicenter Studies Evaluation Research Validation Studies Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|