There is no generally accepted screening strategy for prostate cancer (PCa). From February 2014 to December 2019 a randomized trial (PROBASE) recruited 46 642 men at age 45 to determine the efficacy of risk‐adapted prostate‐specific antigen‐based (PSA) screening, starting at either 45 or 50 years. PSA tests are used to classify participants into a low (<1.5 ng/mL), intermediate (1.5‐2.99 ng/mL) or high (≥3 ng/mL) risk group. In cases of confirmed PSA values ≥3 ng/mL participants are recommended a prostate biopsy with multiparametric magnetic resonance imaging (mpMRI). Half of the participants (N = 23 341) were offered PSA screening immediately at age 45; the other half (N = 23 301) were offered digital rectal examination (DRE) with delayed PSA screening at age 50. Of 23 301 participants who accepted baseline PSA testing in the immediate screening arm, 89.2% fell into the low, 9.3% into intermediate, and 1.5% (N = 344) into the high risk group. Repeat PSA measurement confirmed high‐risk status for 186 men (0.8%), of whom 120 (64.5%) underwent a biopsy. A total of 48 PCas was detected (overall prevalence 0.2%), of which 15 had International Society of Uropathology (ISUP) grade 1, 29 had ISUP 2 and only 4 had ISUP ≥3 cancers. In the delayed screening arm, 23 194 participants were enrolled and 6537 underwent a DRE with 57 suspicious findings, two of which showed PCa (both ISUP 1; detection rate 0.03%). In conclusion, the prevalence of screen‐detected aggressive (ISUP ≥3) PCa in 45‐year‐old men is very low. DRE did not turn out effective for early detection of PCa.

Background: This study investigated the performance of simultaneous ¹⁸F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Methods: One hundred and twenty-four patients underwent simultaneous ¹⁸F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion: ¹⁸F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data.