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Epidemiology, diagnosis and treatment of anterior prostate cancer.

Nat Rev Urol

January 2025

Department of Urology, Guy's and St Thomas' NHS Foundation Trust, London, UK.

Anterior prostate cancers (APCs) are a group of impalpable neoplasms located in regions anterior to the urethra, which comprise the transition zone, apical peripheral zone and anterior fibromuscular stroma. These regions are typically undersampled using conventional biopsy schemes, leading to a low detection rate for APC and a high rate of false negatives. Radical prostatectomy series suggest prevalence rates of at least 10-30%, but transperineal systematic biopsy is ideal for diagnosis, particularly where multiparametric MRI is unavailable.

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Objective: This study aimed to investigate the influence of p16 immunohistochemical expression on the biochemical recurrence rate of pT2-pT3 prostate cancer.

Materials And Methods: A total of 488 pT2-pT3 stage prostate adenocarcinomas undergoing radical prostatectomy were included in this study. Following a review of Gleason classification and retrieval of sociodemographic and clinicopathological data, as well as the date of last consultation and biochemical recurrence, immunohistochemistry for p16 was performed.

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Background: This paper describes the rationale and design of the RECOVER study. Currently, there is no consensus regarding the optimal treatment for high-risk, non-metastatic prostate cancer (PCa). The study primarily aims to evaluate and compare the impact of treatment with robot-assisted radical prostatectomy (RP) versus external beam radiation therapy (EBRT) with androgen deprivation therapy (ADT) for men with high-risk, non-metastatic PCa regarding health-related quality of life (HRQoL) and functional outcomes.

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Purpose: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pelvic lymph node dissection (ePLND) by preoperative nomograms.

Methods: [Ga]Ga-PSMA-617 PET/CT scan of 116 eligible PCa patients (82 in the training cohort and 34 in the test cohort) who underwent radical prostatectomy with ePLND were analyzed in our study. The Med3D deep learning network was utilized to extract discriminative features from the entire prostate volume of interest on the PET/CT images.

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