Efforts made over the last decade for the detection of prostate cancer (PCa) have revolutionised disease diagnostics, and implementation of prebiopsy magnetic resonance imaging (MRI) has received widespread acceptance. However, universal adoption of prebiopsy MRI and the benefits achieved have been limited by availability and equivocal MRI findings. This review aims to evaluate the latest evidence on the role of existing PCa risk calculators (RCs), and blood and urinary biomarkers as part of the diagnostic algorithm to improve the diagnosis of clinically significant PCa (csPCa) and reduce unnecessary MRI procedures and biopsies. We will also evaluate the potential of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) to enhance sensitivity and specificity for PCa diagnosis, complement MRI, and refine biopsy strategies within the diagnostic pathway. We performed a narrative review using the PubMed/MEDLINE database, which included papers published between January 2014 and June 2024. The outcome measures included rates of reduced diagnoses of nonsignificant PCa (defined as International Society of Urological Pathology [ISUP] grade group 1) cases, diagnoses of csPCa (defined as ISUP grade group ≥2) cases missed, and MRI scans and prostate biopsies avoided. In men with abnormal prostate-specific antigen (PSA) levels, further risk stratification using RCs, or blood or urine biomarkers can reduce up to 16–51% MRI scans, while missing 1–16% csPCa cases. In case of equivocal MRI results or Prostate Imaging Reporting and Data System 3 lesions, RCs or biomarkers could reduce up to 72% of biopsies, while missing only 3–13% csPCa cases. PSMA PET has emerging potential to improve csPCa prediction in combination with MRI and may further reduce unnecessary biopsies. A limitation of this study is that this is a narrative but not a systematic review. RCs and biomarkers have been demonstrated to enhance the performance and efficiency of MRI in detecting csPCa in men with elevated PSA levels. PSMA PET shows promise in detecting csPCa, complementing MRI and refining biopsy indications. In men with a suspicion of prostate cancer, magnetic resonance imaging prostate scans are effective in predicting clinically relevant cancer, but challenges including availability and equivocal scans exist. A personalised approach by adding one or more of clinical risk calculators, blood or urine biomarkers, or even novel imaging techniques such as positron emission tomography scans may improve cancer prediction further and reduce unnecessary scans and biopsies.

Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.

Bilateral extended pelvic lymph node dissection at the time of radical prostatectomy is the current standard of care if pelvic lymph node dissection is indicated; often, however, pelvic lymph node dissection is performed in pN0 disease. With the more accurate staging achieved with magnetic resonance imaging-targeted biopsies for prostate cancer diagnosis, the indication for bilateral extended pelvic lymph node dissection may be revised. We aimed to assess the feasibility of unilateral extended pelvic lymph node dissection in the era of modern prostate cancer imaging. We analyzed a multi-institutional data set of men with cN0 disease diagnosed by magnetic resonance imaging-targeted biopsy who underwent prostatectomy and bilateral extended pelvic lymph node dissection. The outcome of the study was lymph node invasion contralateral to the prostatic lobe with worse disease features, ie, dominant lobe. Logistic regression to predict lymph node invasion contralateral to the dominant lobe was generated and internally validated. Overall, data from 2,253 patients were considered. Lymph node invasion was documented in 302 (13%) patients; 83 (4%) patients had lymph node invasion contralateral to the dominant prostatic lobe. A model including prostate-specific antigen, maximum diameter of the index lesion, seminal vesicle invasion on magnetic resonance imaging, International Society of Urological Pathology grade in the nondominant side, and percentage of positive cores in the nondominant side achieved an area under the curve of 84% after internal validation. With a cutoff of contralateral lymph node invasion of 1%, 602 (27%) contralateral pelvic lymph node dissections would be omitted with only 1 (1.2%) lymph node invasion missed. Pelvic lymph node dissection could be omitted contralateral to the prostate lobe with worse disease features in selected patients. We propose a model that can help avoid contralateral pelvic lymph node dissection in almost one-third of cases.