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

ABSTRACT: 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.

SUBMITTER: Zhou JW

PROVIDER: S-EPMC8269827 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundYoung breast cancer (YBC) patients demonstrate a heightened propensity for regional lymph node metastasis (RLNM) in contrast to cohorts across varying age demographics. The aim of our study was to identify clinicopathologic prognostic variables in YBC patients with RLNM and construct a practical and reliable nomogram for the prediction of overall survival (OS) using the Surveillance, Epidemiology, and End Results (SEER) database.MethodsYoung individuals (≤40 years) with a diagnosis of breast cancer with RLNM were recognized from the SEER database between 2010 and 2015, and further randomly split into two cohorts: the training set (n=4,497) and the validation set (n=1,927). We first performed univariate and multivariate Cox regression analyses to confirm independent survival predictors of OS. A novel prognostic nomogram was developed and evaluated using Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). To make a clear distinction between high- and low-risk patients in terms of patient survival, Kaplan-Meier survival curves were assessed using the log-rank test.ResultsNine risk factors were found as independent prognostic variables in predicting OS, including race, grade, histology, surgery, radiation, molecular subtype, American Joint Committee on Cancer (AJCC) stage 7th edition, T stage, and N stage. The C-index values of our nomogram were 0.786 [95% confidence interval (CI): 0.767-0.805] and 0.791 (95% CI: 0.760-0.822) in our training and validation groups, respectively. The ROC curves demonstrated sufficient discriminating ability, while the predicted and real survival rates were fairly consistent, as shown by the calibration plots. The prediction model had a higher net benefit and acceptable clinical value, as shown by the DCA curves.ConclusionsIn YBC patients with RLNM, we successfully established a unique nomogram to forecast the 2-, 3-, and 5-year OS. Clinicians may utilize this nomogram to pinpoint patients at higher risk and provide them with appropriate customized therapies.

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

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