1
|
Prata F, Anceschi U, Cordelli E, Faiella E, Civitella A, Tuzzolo P, Iannuzzi A, Ragusa A, Esperto F, Prata SM, Sicilia R, Muto G, Grasso RF, Scarpa RM, Soda P, Simone G, Papalia R. Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features. Curr Oncol 2023; 30:2021-2031. [PMID: 36826118 PMCID: PMC9955797 DOI: 10.3390/curroncol30020157] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer. METHODS From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation. RESULTS The feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%. CONCLUSIONS Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.
Collapse
Affiliation(s)
- Francesco Prata
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
- Correspondence: ; Tel.: +39-39-3437-3027; Fax: +39-062-2541-1995
| | - Umberto Anceschi
- Department of Urology, IRCCS “Regina Elena” National Cancer Institute, 00144 Rome, Italy
| | - Ermanno Cordelli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Eliodoro Faiella
- Department of Diagnostic and Interventional Radiology, Sant’Anna Hospital, 22042 San Fermo della Battaglia, Italy
| | - Angelo Civitella
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Piergiorgio Tuzzolo
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Andrea Iannuzzi
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Alberto Ragusa
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Francesco Esperto
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Salvatore Mario Prata
- Simple Operating Unit of Lower Urinary Tract Surgery, SS. Trinità Hospital, 03039 Sora, Italy
| | - Rosa Sicilia
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Giovanni Muto
- Department of Urology, Humanitas Gradenigo University, 10153 Turin, Italy
| | - Rosario Francesco Grasso
- Department of Diagnostic and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Roberto Mario Scarpa
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| | - Giuseppe Simone
- Department of Urology, IRCCS “Regina Elena” National Cancer Institute, 00144 Rome, Italy
| | - Rocco Papalia
- Department of Urology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
| |
Collapse
|
2
|
Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
|