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Here,we developed a prostate cancer(PCa)risk nomogram including lymphocyte-to-monocyte ratio(LMR)for initial prostate biopsy,and internal and external validation were further conducted.A prediction model was developed on a training set.Significant risk factors with P＜ 0.10 in multivariate logistic regression models were used to generate a nomogram.Discrimination,calibration,and clinical usefulness of the model were assessed using C-index,calibration plot,and decision curve analysis(DCA).The nomogram was re-examined with the internal and external validation set.A nomogram predicting PCa risk in patients with prostate-specific antigen(PSA)4-10 ng ml-1 was also developed.The model displayed good discrimination with C-index of 0.830(95％confidence interval[CI]:0.812-0.852).High C-index of 0.864(95％CI:0.840-0.888)and 0.871(95％CI:0.861-0.881)was still reached in the internal and external validation sets,respectively.The nomogram exhibited better performance compared to the nomogram with PSA only(C-index:0.763,95％Cl:0.746-0.780,P ＜ 0.001)and the nomogram with LMR excluded(C-index:0.824,95％CI:0.804-0.844,P ＜ 0.010).The calibration curve demonstrated good agreement in the internal and external validation sets.DCA showed that the nomogram was useful at the threshold probability of ＞4％and ＜99％.The nomogram predicting PCa risk in patients with PSA 4-10 ng ml-1 also displayed good calibration and discrimination performance(C-index:0.734,95％Cl:0.708-0.760).This nomogram incorporating age,PSA,digital rectal examination,abnormal imaging signals,PSA density,and LMR could be used to facilitate individual PCa risk prediction in initial prostate biopsy.