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A novel robust nomogram based on peripheral monocyte counts for predicting lymph node metastasis of prostate cancer


1 Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
2 Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
3 Department of Urology, Cancer Center of Sun Yat-sen University, Guangzhou 510060, China
4 Department of International Medical Education, The Southern Medical University, Guangzhou 510515, China
5 Department of Surgery, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
6 Department of Laboratory Medicine, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
7 Department of Urology, Nanfang Hospital of Southern Medical University, Guangzhou 510515, China

Correspondence Address:
Ming-Kun Chen,
Department of Urology, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630
China
Shan-Chao Zhao,
Department of Urology, Nanfang Hospital of Southern Medical University, Guangzhou 510515
China
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/aja.aja_89_20

PMID: 33533737

Accurate methods for identifying pelvic lymph node metastasis (LNM) of prostate cancer (PCa) prior to surgery are still lacking. We aimed to investigate the predictive value of peripheral monocyte count (PMC) for LNM of PCa in this study. Two hundred and ninety-eight patients from three centers were divided into a training set (n = 125) and a validation set (n = 173). In the training set, the independent predictors of LNM were analyzed using univariate and multivariate logistic regression analyses, and the optimal cutoff value was calculated by the receiver operating characteristic (ROC) curve. The sensitivity and specificity of the optimal cutoff were authenticated in the validation cohort. Finally, a nomogram based on the PMC was constructed for predicting LNM. Multivariate analyses of the training cohort demonstrated that clinical T stage, preoperative Gleason score, and PMC were independent risk factors for LNM. The subsequent ROC analysis showed that the optimal cutoff value of PMC for diagnosing LNM was 0.405 × 109 l−1 with a sensitivity of 60.0% and a specificity of 67.8%. In the validation set, the optimal cutoff value showed significantly higher sensitivity than that of conventional magnetic resonance imaging (MRI) (0.619 vs 0.238, P < 0.001). The nomogram involving PMC, free prostate-specific antigen (fPSA), clinical T stage, preoperative Gleason score, and monocyte-to-lymphocyte ratio (MLR) was generated, which showed a robust predictive capacity for predicting LNM before the operation. Our results indicated that PMC as a single agent, or combined with other clinical parameters, showed a robust predictive capacity for LNM in PCa. It can be employed as a complementary factor for the decision of whether to conduct pelvic lymph node dissection.


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