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The article discusses a novel model for integrated prostate cancer diagnostics that combines MRI, prostate biopsy, and pathology reports. Developed by a multi-professional group in Sweden, the model aims to streamline data collection, improve patient outcomes, and enhance quality assessment. The model was tested in a pilot study with positive feedback from urologists, radiologists, and pathologists, showing potential for widespread implementation and validation in clinical practice. The structured approach offers a concise summary of relevant information, benefiting both healthcare providers and patients. [Extracted from the article]

Objective: To determine the feasibility of a digitally automated population‐based programme for organised prostate cancer testing (OPT) in Southern Sweden. Patients and Methods: A 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 (≥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. Results: A 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 (≥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. Conclusion: Our 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. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the feasibility of AI-assisted reading of prostate magnetic resonance imaging (MRI) in Organized Prostate cancer Testing (OPT)., Methods: Retrospective cohort study including 57 men with elevated prostate-specific antigen (PSA) levels ≥3 µg/L that performed bi-parametric MRI in OPT. The results of a CE-marked deep learning (DL) algorithm for prostate MRI lesion detection were compared with assessments performed by on-site radiologists and reference radiologists. Per patient PI-RADS (Prostate Imaging-Reporting and Data System)/Likert scores were cross-tabulated and compared with biopsy outcomes, if performed. Positive MRI was defined as PI-RADS/Likert ≥4. Reader variability was assessed with weighted kappa scores., Results: The number of positive MRIs was 13 (23%), 8 (14%), and 29 (51%) for the local radiologists, expert consensus, and DL, respectively. Kappa scores were moderate for local radiologists versus expert consensus 0.55 (95% confidence interval [CI]: 0.37-0.74), slight for local radiologists versus DL 0.12 (95% CI: -0.07 to 0.32), and slight for expert consensus versus DL 0.17 (95% CI: -0.01 to 0.35). Out of 10 cases with biopsy proven prostate cancer with Gleason ≥3+4 the DL scored 7 as Likert ≥4., Interpretation: The Dl-algorithm showed low agreement with both local and expert radiologists. Training and validation of DL-algorithms in specific screening cohorts is essential before introduction in organized testing.