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A retrospective study found that a serum lipid-based nomogram was a reliable tool for predicting the risk of prostate cancer.

Various clinical prediction models have been developed in some Western countries for individually assessing prostate cancer (PCa) risk to reduce unnecessary biopsies. These models are more accurate and scientific than the traditional methods based on prostate-specific antigen (PSA) and digital rectal examination (DRE); however, they fail to consider the effect of a high-fat diet on PCa. 

A study in the journal BMC Urology conducted a retrospective analysis in the Chinese population to determine if serum lipid profiles can help optimize PCa diagnosis.

Study Population and Method

The study included 548 patients. The nomogram was developed by randomly selecting 384 (70%) patients who were assigned to the training group, with 164 (30%) patients set aside for validation. Baseline characteristics were similar between the training and validation group participants.

Multivariable Analysis for Predicting Prostate Cancer

In multivariable logistic regression analysis, total PSA (tPSA), free/total PSA ratio (f/tPSA), PSA density (PSAD), triglyceride (TG), low-density lipoprotein (LDL), DRE, and trans-rectal ultrasonography (TRUS) were significantly associated with the presence of PCa in the biopsy. 

Two prediction models were established. Model 1 comprised common clinical indicators (tPSA, f/tPSA, PSAD, DRE, TRUS), while Model 2 incorporated TG and LDL along with these common clinical indicators. Model 2 demonstrated a significant superiority over Model 1 and PSA for predicting PCa in both the training and validation groups.

Nomogram for Prostate Cancer Risk Estimation

Using multivariate logistic regression, a predictive model was constructed. Seven variables were incorporated in the model (Model 2) as predictors, namely tPSA, f/tPSA, PSAD, DRE, TRUS, TG, and LDL. The cut-off value for Model 2 was determined to be 0.502 in the training group. 

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Sensitivity, specificity, positive predictive value, negative predictive value, false negative rate, and false positive rate were 74.03%, 82.76%, 79.2%, 78.3%, 25.97%, and 17.24%, respectively. Based on these findings, a prostate biopsy is recommended when the prediction probability exceeds the threshold value of 0.502. The model was further validated using the validation group cases in combination with PCPT-CRC and two domestic predictive models. Prediction Model 2 showed significantly superior predictive performance than PCPT-CRC and the two domestic predictive models.

Validation of the Predictive Model

The calibration plot demonstrated a high degree of consistency between the bias-corrected and ideal curves in both the training and validation sets, indicating that the nomogram accurately predicted the occurrence risk and showed good calibration. The net benefit of the two models (Models 1 and 2) was higher than that of PSA when the threshold probability was 0.06–0.82. Model 2 showed the greatest net benefit, outperforming all other indices.  

Source:

Feng, F., Zhong, Y., Yang, C., Lin, F., Jian-Hua, H., Mai, Y., Zhao, P., Wei, W., Zhu, H., & Zhou, X. (2023). Establishment and validation of serum lipid-based nomogram for predicting the risk of prostate cancer. BMC Urology, 23(1). https://doi.org/10.1186/s12894-023-01291-w