| 1. |
- Alterbeck, Max, et al.
(författare)
-
A pilot study of an organised population-based testing programme for prostate cancer
- 2024
-
Ingår i: BJU International. - 1464-4096. ; 133:1, s. 87-95
-
Tidskriftsartikel (refereegranskat)abstract
- ObjectiveTo determine the feasibility of a digitally automated population-based programme for organised prostate cancer testing (OPT) in Southern Sweden.Patients and MethodsA pilot project for a regional OPT was conducted between September 2020 and February 2021, inviting 999 randomly selected men aged 50, 56, or 62 years. Risk stratification was based on prostate-specific antigen (PSA) level, PSA density (PSAD), and bi-parametric prostate magnetic resonance imaging (MRI). Men with a PSA level of 3-99 ng/mL had an MRI, and men with elevated PSA level (& GE;3 ng/mL) had a urological check-up, including a digital rectal examination and transrectal ultrasonography (TRUS). Indications for targeted and/or systematic transrectal prostate biopsies were suspicious lesions on MRI (Prostate Imaging-Reporting and Data System [PI-RADS] 4-5) and/or PSAD > 0.15 ng/mL/mL. Additional indications for prostate biopsies were palpable tumours, PSA ratio < 0.1, or cancer suspicion on TRUS. Patient selection, mail correspondence, data collection, and algorithm processing were performed by an automated digital management system. Feasibility is reported descriptively.ResultsA total of 418 men had a PSA test (42%), with increasing participation rates by age (50 years, 38%; 56 years, 44%; and 62 years, 45%). Among these, 35 men (8%) had elevated PSA levels (& GE;3 ng/mL: one of 139, aged 50 years; 10/143, aged 56 years; and 24/146, aged 62 years). On MRI, 16 men (48%) had a negative scan (PI-RADS < 3), seven men (21%) had PI-RADS 3, nine men (27%) had PI-RADS 4, and one man (3%) had PI-RADS 5. All men with PI-RADS 4 or 5 underwent prostate biopsies, as well as two men with PI-RADS 3 due to PSAD > 0.15 ng/mL/mL or a suspicious finding on TRUS. Prostate cancer was diagnosed in 10 men. Six men underwent active treatment, whereas four men were assigned to active surveillance.ConclusionOur OPT model is feasible from an operational point of view, but due to the limited scale of this study no conclusions can be made regarding the efficacy of the diagnostic model or outcome.
|
|
| 2. |
- Thimansson, Erik, et al.
(författare)
-
Deep learning performance on MRI prostate gland segmentation : evaluation of two commercially available algorithms compared with an expert radiologist
- 2024
-
Ingår i: Journal of Medical Imaging. - 2329-4302. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: Accurate whole-gland prostate segmentation is crucial for successful ultrasound-MRI fusion biopsy, focal cancer treatment, and radiation therapy techniques. Commercially available artificial intelligence (AI) models, using deep learning algorithms (DLAs) for prostate gland segmentation, are rapidly increasing in numbers. Typically, their performance in a true clinical context is scarcely examined or published. We used a heterogenous clinical MRI dataset in this study aiming to contribute to validation of AI-models.APPROACH: We included 123 patients in this retrospective multicenter (7 hospitals), multiscanner (8 scanners, 2 vendors, 1.5T and 3T) study comparing prostate contour assessment by 2 commercially available Food and Drug Association (FDA)-cleared and CE-marked algorithms (DLA1 and DLA2) using an expert radiologist's manual contours as a reference standard (RSexp) in this clinical heterogeneous MRI dataset. No in-house training of the DLAs was performed before testing. Several methods for comparing segmentation overlap were used, the Dice similarity coefficient (DSC) being the most important.RESULTS: The DSC mean and standard deviation for DLA1 versus the radiologist reference standard (RSexp) was 0.90±0.05 and for DLA2 versus RSexp it was 0.89±0.04. A paired t-test to compare the DSC for DLA1 and DLA2 showed no statistically significant difference (p=0.8).CONCLUSIONS: Two commercially available DL algorithms (FDA-cleared and CE-marked) can perform accurate whole-gland prostate segmentation on a par with expert radiologist manual planimetry on a real-world clinical dataset. Implementing AI models in the clinical routine may free up time that can be better invested in complex work tasks, adding more patient value.
|
|
