ArticleJournal of research in medical sciences : the official journal of Isfahan University of Medical Sciences2025
Development of a predictive model for preoperative bone metastasis risk assessment in prostate cancer through integration of semi-quantitative single-photon emission computed tomography/computed tomography indices with peripheral blood biomarkers.
Article in Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: To investigate the risk factors of preoperative bone metastasis (BM) of prostate cancer (PCa) by using semi-quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) whole-body bone imaging and peripheral blood biomarkers, and to establish a morphological map model to evaluate its prediction accuracy. Materials and Methods: Clinical data of 220 patients diagnosed with PCa were retrospectively analyzed. BM was identified using SPECT/CT. Based on the presence or absence of BM, patients were divided into two groups. Univariate and multiple logistic regression analyses were performed on various factors, including age, laboratory parameters, prostate volume determined, clinical tumor stage (cTx), and Gleason score (GS). Draw the receiver operating characteristic curve and calculate the area under the curve (AUC), and analyze the predictive efficacy. In addition, we construct a nomogram representing the BM prediction model using clinical data and generate a calibration plot to assess the accuracy of our predictions. Results: The analysis of univariate and multiple logistic regression demonstrated that (target area-nontarget area)/nontarget area (T-NT)/NT, alkaline phosphatase (ALP), total prostate-specific antigen (tPSA), cTx, and GS were independent predictors of BM in PCa. The tPSA displayed the highest AUC of 0.68 (95% confidence interval [CI]: 0.62-0.75) among the five independent predictors. The best predictive efficacy was shown when the predictive model was established using these five factors, as evidenced by the AUC of 0.80 (95% CI: 0.75-0.86) being higher than any single indicator. The predictive model's external validation data sensitivity and specificity metrics were 66.67% (8/12) and 95.65% (22/23), respectively, which were consistent with the model's initial sensitivity of 58.02% and specificity of 87.77%, indicating high accuracy and stability. Conclusion: The established predictive model, incorporating (T-NT)/NT from semi-quantitative SPECT/CT, ALP, tPSA, cTx, and GS, exhibits strong predictive efficacy with high accuracy and stability, providing a reliable tool for preoperative assessment of BM risk in PCa patients.
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