Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.

Autor: Pellicer-Valero OJ; Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València (UV), Av. Universitat, sn, 46100, Bujassot, Valencia, Spain. Oscar.Pellicer@uv.es., Marenco Jiménez JL; Department of Urology, Fundación Instituto Valenciano de Oncología (FIVO), Beltrán Báguena, 8, 46009, Valencia, Spain., Gonzalez-Perez V; Department of Medical Physics, Fundación Instituto, Valenciano de Oncología (FIVO), Beltrán Báguena, 8, 46009, Valencia, Spain., Casanova Ramón-Borja JL; Department of Urology, Fundación Instituto Valenciano de Oncología (FIVO), Beltrán Báguena, 8, 46009, Valencia, Spain., Martín García I; Department of Radiodiagnosis, Fundación Instituto, Valenciano de Oncología (FIVO), Beltrán Báguena, 8, 46009, Valencia, Spain., Barrios Benito M; Department of Radiodiagnosis, Fundación Instituto, Valenciano de Oncología (FIVO), Beltrán Báguena, 8, 46009, Valencia, Spain., Pelechano Gómez P; Department of Radiodiagnosis, Fundación Instituto, Valenciano de Oncología (FIVO), Beltrán Báguena, 8, 46009, Valencia, Spain., Rubio-Briones J; Department of Urology, Fundación Instituto Valenciano de Oncología (FIVO), Beltrán Báguena, 8, 46009, Valencia, Spain., Rupérez MJ; Instituto de Ingeniería Mecánica y Biomecánica, Universitat Politècnica de València (UPV), Camino de Vera, sn, 46022, Valencia, Spain., Martín-Guerrero JD; Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València (UV), Av. Universitat, sn, 46100, Bujassot, Valencia, Spain.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Feb 22; Vol. 12 (1), pp. 2975. Date of Electronic Publication: 2022 Feb 22.
DOI: 10.1038/s41598-022-06730-6
Abstrakt: Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still complex even for experts. This paper proposes a fully automatic system based on Deep Learning that performs localization, segmentation and Gleason grade group (GGG) estimation of PCa lesions from prostate mpMRIs. It uses 490 mpMRIs for training/validation and 75 for testing from two different datasets: ProstateX and Valencian Oncology Institute Foundation. In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG[Formula: see text]2 significance criterion of 0.96/1.00/0.79 for the ProstateX dataset, and 0.95/1.00/0.80 for the IVO dataset. At a patient level, the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO. Furthermore, on the online ProstateX grand challenge, the model obtained an AUC of 0.85 (0.87 when trained only on the ProstateX data, tying up with the original winner of the challenge). For expert comparison, IVO radiologist's PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and 0.85/0.58 at a patient level. The full code for the ProstateX-trained model is openly available at https://github.com/OscarPellicer/prostate_lesion_detection . We hope that this will represent a landmark for future research to use, compare and improve upon.
(© 2022. The Author(s).)
Databáze: MEDLINE
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