[Difference between prostate cancer patients' Gleason scores from preoperative biopsy and those from postoperative...

To evaluate the consistency of the Gleason scores of PCa patients based on preoperative biopsy with those from postoperative pathology, identify the possible factors influencing results of scoring, and construct a risk scoring model.

We collected the demographic and clinical data on the patients with PCa confirmed by preoperative prostate biopsy or postoperative pathology and treated by radical prostatectomy within 6 months after diagnosis. Using paired sample t-test, we identified the difference between the Gleason scores based on preoperative biopsy and those from postoperative pathology, analyzed the demographic and clinical data on the patients for relevant factors affecting the consistency of the Gleason scores, and calculated and visualized the relative risk values of the factors through Poisson regression. From the continuous variables with statistical significance, we screened independent risk factors for the difference in the Gleason scores by Lasso regression analysis, established a risk scoring model, generated risk coefficients, and evaluated the predictive ability of the model using the ROC curve. Based on the results of imaging examination with statistically significant differences, we constructed a column chart by logistic regression and evaluated the predictive validity of the chart using calibration curves, decision curves and ROC curves.

The results of paired sample t-test for 210 PCa patients showed statistically significant differences between the Gleason scores from preoperative biopsy and those from postoperative pathology (P < 0.001). There were significant differences in the body weight, BMI and PSA level as well as in all other factors but prostate calcification between the patients with consistent and those with inconsistent Gleason scores (all P < 0.05). An 8-factor prediction model was successfully constructed, which could predict the consistency of Gleason scores, with a better predicting performance than the single indicator within the model. The nomogram exhibited a C-index value of 0.85, with the calibration curve similar to the standard one, the threshold of the decision curve 0.10－0.92, and the area under the ROC curve higher than other predictive indicators.

Based on the demographic and clinical data on PCa patients, a risk prediction model and a column chart were successfully constructed, which could effectively predict the difference between the Gleason scores from preoperative prostate biopsy and those from postoperative pathology.