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Abstract Robot-assisted surgery has evolved into a crucial treatment for prostate cancer (PCa). However, from its appearance to today, brain-computer interface, virtual reality, and metaverse have revolutionized the field of robot-assisted surgery for PCa, presenting both opportunities and challenges. Especially in the context of contemporary big data and precision medicine, facing the heterogeneity of PCa and the complexity of clinical problems, it still needs to be continuously upgraded and improved. Keeping this in mind, this article summarized the 5 stages of the historical development of robot-assisted surgery for PCa, encompassing the stages of emergence, promotion, development, maturity, and intelligence. Initially, safety concerns were paramount, but subsequent research and engineering advancements have focused on enhancing device efficacy, surgical technology, and achieving precise multi modal treatment. The dominance of da Vinci robot-assisted surgical system has seen this evolution intimately tied to its successive versions. In the future, robot-assisted surgery for PCa will move towards intelligence, promising improved patient outcomes and personalized therapy, alongside formidable challenges. To guide future development, we propose 10 significant prospects spanning clinical, research, engineering, materials, social, and economic domains, envisioning a future era of artificial intelligence in the surgical treatment of PCa.

We attempted to perform risk categories based on the free/total prostate-specific antigen ratio (%fPSA), prostate-specific antigen (PSA) density (PSAD, in ng ml−2), and multiparametric magnetic resonance imaging (mpMRI) step by step, with the goal of determining the best clinical diagnostic strategy to avoid unnecessary tests and prostate biopsy (PBx) in biopsy-naïve men with PSA levels ranging from 4 ng ml−1 to 10 ng ml−1. We included 439 patients who had mpMRI and PBx between August 2018 and July 2021 (West China Hospital, Chengdu, China). To detect clinically significant prostate cancer (csPCa) on PBx, receiver-operating characteristic (ROC) curves and their respective area under the curve were calculated. Based on %fPSA, PSAD, and Prostate Imaging-Reporting and Data System (PI-RADS) scores, the negative predictive value (NPV) and positive predictive value (PPV) were calculated sequentially. The optimal %fPSA threshold was determined to be 0.16, and the optimal PSAD threshold was 0.12 for %fPSA ≥0.16 and 0.23 for %fPSA

This study aimed to assess the role of prostate-specific antigen density (PSAD) and negative multiparametric magnetic resonance imaging (mpMRI) in predicting prostate cancer for biopsy-naïve men based on a large cohort of the Chinese population. From a prostate biopsy database between March 2017 and July 2021, we retrospectively identified 240 biopsy-naïve patients with negative prebiopsy mpMRI (Prostate Imaging Reporting and Data System version 2 [PI-RADS v2] score

We attempted to perform risk categories based on the free/total prostate-specific antigen ratio (%fPSA), prostate-specific antigen (PSA) density (PSAD, in ng ml-2), and multiparametric magnetic resonance imaging (mpMRI) step by step, with the goal of determining the best clinical diagnostic strategy to avoid unnecessary tests and prostate biopsy (PBx) in biopsy-naive men with PSA levels ranging from 4 ng ml-1 to 10 ng ml-1. We included 439 patients who had mpMRI and PBx between August 2018 and July 2021 (West China Hospital, Chengdu, China). To detect clinically significant prostate cancer (csPCa) on PBx, receiver-operating characteristic (ROC) curves and their respective area under the curve were calculated. Based on %fPSA, PSAD, and Prostate Imaging-Reporting and Data System (PI-RADS) scores, the negative predictive value (NPV) and positive predictive value (PPV) were calculated sequentially. The optimal %fPSA threshold was determined to be 0.16, and the optimal PSAD threshold was 0.12 for %fPSA ≥0.16 and 0.23 for %fPSA <0.16, respectively. When PSAD <0.12 was combined with patients with %fPSA ≥0.16, the NPV of csPCa increased from 0.832 (95% confidence interval [CI]: 0.766-0.887) to 0.931 (95% CI: 0.833-0.981); the detection rate of csPCa was similar when further stratified by PI-RADS scores (P = 0.552). Combining %fPSA <0.16 with PSAD ≥0.23 ng ml-2 predicted significantly more csPCa patients than those with PSAD <0.23 ng ml-2 (58.4% vs 26.7%, P < 0.001). Using PI-RADS scores 4 and 5, the PPV was 0.739 (95% CI: 0.634-0.827) when further stratified by mpMRI results. In biopsy-naïve patients with PSA level of 4-10 ng ml-1, stratification of %fPSA and PSAD combined with PI-RADS scores may be useful in the decision-making process prior to undergoing PBx. [ABSTRACT FROM AUTHOR]