This study evaluated the effect of body composition and pelvic fat distribution on the aggressiveness and prognosis of localized prostate cancer. This study included patients who underwent robot-assisted radical prostatectomy with positive surgical margins. Clinicodemographic data were collected from patients’ medical reports. Pretreatment magnetic resonance images (MRI) obtained for cancer staging were reviewed by a single radiologist to calculate pelvic fat distribution and body composition. We correlated these body composition parameters with initial prostate-specific antigen (iPSA), Gleason score, extracapsular tumor extension, and biochemical recurrence (BCR)–free survival. The iPSA was significantly associated with body mass index (BMI; P = .027), pelvic fat volume (P = .004), and perirectal fat volume (P = .001), whereas the Gleason score was significantly associated with BMI only (P = .011). Tumor extracapsular extension was significantly associated with increased periprostatic fat volume (P = .047). Patients with less subcutaneous fat thickness (<2.4 cm) had significantly poor BCR–free survival (P = .039). Pelvic fat distribution, including pelvic fat volume, perirectal fat volume, and periprostatic fat volume, were significantly correlated with prostate cancer aggressiveness. Patients with less subcutaneous fat had an increased risk of BCR after radical prostatectomy.

Background: The International Society of Urological Pathology (ISUP) grade and positive surgical margins (PSMs) after radical prostatectomy (RP) may reflect the prognosis of prostate cancer (PCa) patients. This study aimed to investigate whether DCE-MRI parameters (i.e., K^trans, k_ep, and IAUC) could predict ISUP grade and PSMs after RP. Method: Forty-five PCa patients underwent preoperative DCE-MRI. The clinical characteristics and DCE-MRI parameters of the 45 patients were compared between the low- and high-risk (i.e., ISUP grades III-V) groups and between patients with or without PSMs after RP. Multivariate logistic regression analysis was used to identify the significant predictors of placement in the high-risk group and PSMs. Results: The DCE parameter K^trans-max was significantly higher in the high-risk group than in the low-risk group (p = 0.028) and was also a significant predictor of placement in the high-risk group (odds ratio [OR] = 1.032, 95% confidence interval [CI] = 1.005–1.060, p = 0.021). Patients with PSMs had significantly higher prostate-specific antigen (PSA) titers, positive biopsy core percentages, K^trans-max, k_ep-median, and k_ep-max than others (all p < 0.05). Of these, positive biopsy core percentage (OR = 1.035, 95% CI = 1.003–1.068, p = 0.032) and k_ep-max (OR = 1.078, 95% CI = 1.012–1.148, p = 0.020) were significant predictors of PSMs. Conclusion: Preoperative DCE-MRI parameters, specifically K^trans-max and k_ep-max, could potentially serve as preoperative imaging biomarkers for postoperative PCa prognosis based on their predictability of PCa risk group and PSM on RP, respectively.

The durability of anti-SARS-CoV-2 antibodies after vaccination or infection varies among individuals, and the role of biological aging remains unclear. This study investigated the impact of PhenoAge acceleration on antibody responses and the risk of breakthrough infection. We analyzed serum concentration of antibodies against the receptor-binding domain of the SARS-CoV-2 S protein and calculated PhenoAge/PhenoAgeAccel using nine clinical biomarkers in 93 participants, including COVID-19-infected and vaccinated individuals, with follow-up every 3 months. Receiving ≥ 3 vaccine doses was associated with high antibody levels (OR: 4.36, p = 0.001), but natural infection was the strongest predictor (OR: 124.79, p < 0.001). Antibody titers > 2500 IU/mL protected against breakthrough infection (HR: 0.04, p = 0.0368). Despite ≥ 3 vaccine doses, individuals with titers ≤ 2500 IU/mL had increased risk (HR: 34.25, p = 0.0155). PhenoAge acceleration was associated with higher infection risk (HR: 4.75, p = 0.042). Booster vaccination and natural infection are key to sustaining high antibody levels. Accelerated biological aging may increase infection risk. High antibody levels are essential to prevent breakthrough SARS-CoV-2 infection, and vaccine doses alone are insufficient without an adequate antibody response.