Zhongguo quanke yixue (Jul 2024)
Development and Validation of a Prediction Model for Prostate Cancer Early Screening
Abstract
Background As a common malignant tumor, prostate cancer (PCa) poses a significant threat to the well-being of men worldwide. The prognosis of PCa is intricately linked to the grade and stage of the tumor at the time of initial detection. Prostate specific antigen (PSA) is a key biomarker for evaluating prostate health, yet lacks specificity for prostate cancer tumors. Elevated PSA levels can also be caused by benign prostate diseases. And the indiscriminate use of biopsy resulting in overdiagnosis. Hence, the development of a prostate cancer risk prediction model based on pre-biopsy clinical indicators in patients can serve as a valuable tool for early screening of individuals with suspicious findings warranting biopsy. Objective To examine the individual risk factors associated with positive prostate biopsy outcomes and develop a risk assessment model for predicting positive biopsy results in PCa screening. Methods A total of 1 138 patients who underwent prostate biopsy in the Department of Urology, the First Affiliated Hospital of Air Force Medical University from January 2011 to June 2023 were gathered and organized. Following the exclusion of 351 cases with inadequate clinical data, the remaining 787 cases were randomly allocated into a training set and validation set in a 7∶3 ratio by R software. Patient demographics and routine biochemical test results prior to biopsy were compiled, with PCa diagnosis determined based on the outcomes of the biopsy. LASSO regression analysis in the R software was utilized to identify independent risk factors associated with the development of PCa based on biochemical indicators. Subsequently, multivariate logistic regression analysis in SPSS software was employed to construct an early screening and predictive model for PCa, with a Nomogram being generated. The model was validated according to the data of training set and validation set. Results The study utilized LASSO regression analysis to identify 6 independent risk factors associated with positive prostate biopsy results, including age, total PSA (tPSA), alkaline phosphatase, serum protein level, Ca2+, and urea. Multivariate Logistic regression analysis revealed that individuals aged 60 years or older (OR=3.769, 95%CI=2.393-5.937), with tPSA levels of 10 μg/L or higher (OR=2.259, 95%CI=1.419-3.596), and alkaline phosphatase levels exceeding 45 U/L (45-<125 U/L, OR=20.136, 95%CI=4.419-91.752; ≥125 U/L, OR=45.691, 95%CI=9.199-226.951) were at increased risk for positive prostate biopsy outcomes (P<0.05). Conversely, higher levels of serum total protein (≥65 g/L, OR=0.086, 95%CI=0.031-0.236), Ca2+ (≥2.11 mmol/L, OR=0.148, 95%CI=0.054-0.403), and urea (≥9.5 mmol/L, OR=0.069, 95%CI=0.019-0.252) were found to be protective factors against positive prostate biopsy results (P<0.05). Based on the identification of 6 independent risk factors exhibiting statistically significant differences, a nomogram was constructed and a predictive model was developed. The predictive model yielded an Area under the receiver operating characteristic (ROC) curve (AUC) of 0.778 (95%CI=0.740-0.816) for PCa in the training set, with a sensitivity of 53.2% and a specificity of 85.5%. In the validation cohort, the AUC for PCa was 0.770 (95%CI=0.708-0.832), with a sensitivity of 61.2% and a specificity of 80.0%. The goodness of fit test indicated P=0.543 in the training set and P=0.372 in the validation set, demonstrating a satisfactory level of fit. The discriminant analysis (DCA) demonstrated that the high-risk threshold in the training set was below 10%, while in the validation set it was approximately 15%, indicating valuable implications for clinical practice. Conclusion This study developed a PCa nomogram risk prediction model incorporating 6 biochemical indicators, namely age, tPSA, alkaline phosphatase, serum total protein, Ca2+, and urea, prior to prostate biopsy, to effectively forecast PCa risk in patients with favorable early screening outcomes.
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