Sophia Zackrisson
Manager
Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI
Author
Summary, in English
Objectives
Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was to assess whether a deep learning algorithm can replace PI-RADS 2.1 based ellipsoid formula (EF) for calculating PV.
Methods
Eight different measures of PV were retrospectively collected for each of 124 patients who underwent radical prostatectomy and preoperative MRI of the prostate (multicenter and multi-scanner MRI’s 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PVDL) and ellipsoid formula by two radiologists (PVEF1 and PVEF2) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PVMPE). A sensitivity analysis was performed using a prostatectomy specimen as the reference standard. Inter-reader agreement was evaluated between the radiologists using the ellipsoid formula and between the expert and inexperienced radiologists performing manual planimetry.
Results
PVDL showed better agreement and precision than PVEF1 and PVEF2 using the reference standard PVMPE (mean difference [95% limits of agreement] PVDL: −0.33 [−10.80; 10.14], PVEF1: −3.83 [−19.55; 11.89], PVEF2: −3.05 [−18.55; 12.45]) or the PV determined based on specimen weight (PVDL: −4.22 [−22.52; 14.07], PVEF1: −7.89 [−30.50; 14.73], PVEF2: −6.97 [−30.13; 16.18]). Inter-reader agreement was excellent between the two experienced radiologists using the ellipsoid formula and was good between expert and inexperienced radiologists performing manual planimetry.
Conclusion
Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.
Key Points
• A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.
• The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set.
Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was to assess whether a deep learning algorithm can replace PI-RADS 2.1 based ellipsoid formula (EF) for calculating PV.
Methods
Eight different measures of PV were retrospectively collected for each of 124 patients who underwent radical prostatectomy and preoperative MRI of the prostate (multicenter and multi-scanner MRI’s 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PVDL) and ellipsoid formula by two radiologists (PVEF1 and PVEF2) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PVMPE). A sensitivity analysis was performed using a prostatectomy specimen as the reference standard. Inter-reader agreement was evaluated between the radiologists using the ellipsoid formula and between the expert and inexperienced radiologists performing manual planimetry.
Results
PVDL showed better agreement and precision than PVEF1 and PVEF2 using the reference standard PVMPE (mean difference [95% limits of agreement] PVDL: −0.33 [−10.80; 10.14], PVEF1: −3.83 [−19.55; 11.89], PVEF2: −3.05 [−18.55; 12.45]) or the PV determined based on specimen weight (PVDL: −4.22 [−22.52; 14.07], PVEF1: −7.89 [−30.50; 14.73], PVEF2: −6.97 [−30.13; 16.18]). Inter-reader agreement was excellent between the two experienced radiologists using the ellipsoid formula and was good between expert and inexperienced radiologists performing manual planimetry.
Conclusion
Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.
Key Points
• A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.
• The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set.
Department/s
- LUCC: Lund University Cancer Centre
- Radiology Diagnostics, Malmö
- Department of Translational Medicine
- Diagnostic Radiology, (Lund)
- Department of Clinical Sciences, Lund
- Urological cancer, Malmö
- eSSENCE: The e-Science Collaboration
- EpiHealth: Epidemiology for Health
Publishing year
2023
Language
English
Pages
2519-2528
Publication/Series
European Radiology
Volume
33
Document type
Journal article
Publisher
Springer
Topic
- Radiology, Nuclear Medicine and Medical Imaging
- Cancer and Oncology
Keywords
- Magnetic resonance imaging
- Prostate neoplasms
- Deep learning
- Prostate-specific antigen
Status
Published
Research group
- Radiology Diagnostics, Malmö
- Urological cancer, Malmö
ISBN/ISSN/Other
- ISSN: 0938-7994