| Summary: | BackgroundPeople residing in rural areas have higher prostate cancer (PCa) mortality to incidence ratio (M/I) and worse prognosis than those in urban areas of China. Clinical characteristics at initial diagnosis are significantly associated with biochemical recurrence, overall survival, and PCa disease-free survival.ObjectiveThis study aimed at investigating the clinical characteristics at initial diagnosis of urban and rural PCa patients and to establish a logistic regression model for identifying independent predictors for high-grade PCa.Materials and MethodsClinical characteristics for PCa patients were collected from the largest prostate biopsy center in Anhui province, China, from December 2015 to March 2019. First, urban–rural disparities in clinical characteristics were evaluated at initial diagnosis. Second, based on pathological findings, we classified all participants into the benign+ low/intermediate-grade PCa or high-grade PCa groups. Univariate and multivariate logistic regression analyses were performed to identify independent factors for predicting high-grade PCa, while a nomogram for predicting high-grade PCa was generated based on all independent factors. The model was evaluated using area under receiver-operating characteristic (ROC) curve as well as calibration curve analyses and compared to a model without the place of residence factor of individuals.ResultsStatistically significant differences were observed between urban and rural PCa patients with regard to tPSA, PSA density (PSAD), and Gleason score (GS) (p < 0.05). Logistic regression analysis revealed that tPSA [OR = 1.060, 95% confidence interval (CI): 1.024, 1.098], PSAD (OR = 14.678, 95%CI: 4.137, 52.071), place of residence of individuals (OR = 5.900, 95%CI: 1.068, 32.601), and prostate imaging reporting and data system version 2 (PI-RADS v2) (OR = 4.360, 95%CI: 1.953, 9.733) were independent predictive factors for high-grade PCa. The area under the curve (AUC) of the nomogram was greater than that of the model without the place of residence of individuals. The calibration curve of the nomogram indicated that the prediction curve was basically fitted to the standard curve, suggesting that the prediction model had a better calibration ability.ConclusionsCompared to urban PCa patients, rural PCa patients presented elevated tPSA, PSAD levels, and higher pathological grades. The place of residence of the individuals was an independent predictor for high-grade PCa in Anhui Province, China. Therefore, appropriate strategies, such as narrowing urban-rural gaps in access to health care and increasing awareness on the importance of early detection should be implemented to reduce PCa mortality rates.
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