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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Adult, Aged, Antigens, CD, Biomarkers, Biopsy, Cohort Studies, Endoglin, Follow-Up Studies, Humans, Interleukin-6, Male, Middle Aged, Neoplasm Staging, Predictive Value of Tests, Prostate-Specific Antigen, Prostatectomy, Prostatic Neoplasms, Receptors, Cell Surface, Receptors, Interleukin-6, Recurrence, Time Factors, Transforming Growth Factor beta1, Treatment Outcome, Vascular Cell Adhesion Molecule-1.
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Do pre-treatment biomarker levels improve the accuracy of post-prostatectomy nomogram for prediction of biochemical recurrence? Context: We tested the ability of several pre-operative blood-based biomarkers to enhance the accuracy of standard post-operative features for the prediction of biochemical recurrence (BCR) after radical prostatectomy (RP). Pre-operative plasma levels of Endoglin, interleukin-6 (IL-6), interleukin-6 soluble receptor (IL-6sR), transforming growth factor-beta1 (TGF-beta1), urokinase plasminogen activator (uPA), urokinase plasminogen inhibitor-1 (PAI-1), urokinase plasminogen receptor (uPAR), vascular cell adhesion molecule-1 (VCAM1), and vascular endothelial growth factor (VEGF) were measured using commercially available enzyme immunoassays in 423 consecutive patients treated with RP for clinically localized prostate cancer. Standard post-operative features consisted of surgical margin status, extracapsular extension, seminal vesicle invasion, lymph node involvement, and pathologic Gleason sum. Multivariable modeling was used to explore the gain in the predictive accuracy. The accuracy was quantified by the c-index statistic and was internally validated with 200 bootstrap resamples. Plasma IL-6 (P = 0.03), IL-6sR (P < 0.001), TGF-beta1 (P = 0.005), and V-CAM1 (P = 0.01) achieved independent predictor status after adjusting for the effects of standard post-operative features. After stepwise backward variable elimination, a model relying on RP Gleason sum, IL-6sR, TGF-beta1, VCAM1, and uPA improved the predictive accuracy of the standard post-operative model by 4% (86.1% vs. 82.1%, P < 0.001).
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Pre-operative plasma biomarkers improved the accuracy of established post-operative prognostic factors of BCR by a significant margin. Incorporation of these biomarkers into standard predictive models may allow more accurate identification of patients who are likely to fail RP thereby allowing more efficient delivery of adjuvant therapy. Final Decision: yes