全身骨显像联合ALP对前列腺癌骨转移瘤负荷分层的预测价值

Predictive Value of Bone Scintigraphy Combined with Alkaline Phosphatase for Stratifying Tumor Burden in Prostate Cancer Bone Metastases

摘要: 目的：探讨全身骨显像半定量参数肿瘤靶本比(target-to-background ratio, TBR)联合血清碱性磷酸酶(alkaline phosphatase, ALP)对前列腺癌(prostate cancer, PCa)骨转移瘤负荷分层的预测价值，评估功能代谢指标在常规临床病理指标基础上提升高瘤负荷识别能力的作用，为传统分层标准提供功能代谢层面的初步补充依据。方法：回顾性纳入2023年7月至2025年8月于本院收治的156例前列腺癌骨转移患者，根据疾病状态分为激素敏感性前列腺癌(metastatic hormone-sensitive prostate cancer, mHSPC, n = 94)与去势抵抗性前列腺癌(metastatic castration-resistant prostate cancer, mCRPC, n = 62)两个亚组，分别采用CHAARTED标准(mHSPC亚组)、SYMPHONY标准(mCRPC亚组)，将患者分别划分为高瘤负荷组与低瘤负荷组。收集患者临床病理特征、血清ALP水平及全身骨显像TBR值。构建嵌套模型(Model A：临床指标——Gleason评分 ≥ 8分 + T3~4期；Model B：临床指标 + ALP；Model C：临床指标 + ALP + TBR)，比较ROC曲线下面积(AUC)及似然比检验，评估ALP与TBR在临床指标基础上的预测价值；采用决策曲线分析(decision curve analysis, DCA)评估整合模型(Model C)的临床净获益。结果：单因素分析显示，mHSPC亚组中年龄、Gleason评分 ≥ 8分、T3~4期、ALP及TBR为高瘤负荷的影响因素；mCRPC亚组中Gleason评分 ≥ 8分、T3~4期、ALP及TBR为高瘤负荷的影响因素(均P < 0.05)。多因素嵌套模型分析显示，两亚组中，在基础临床指标(Gleason评分 ≥ 8分、T3~4期)基础上依次引入ALP及TBR后，模型AUC逐步提升，mHSPC亚组从0.689升至0.901再至0.934，mCRPC亚组从0.661升至0.782再至0.838 (似然比检验均P < 0.05)，提示ALP及TBR具有显著预测价值。Model C在两亚组中均为最优评估模型。结论：在常规临床病理指标基础上联合二者，能显著提升对前列腺癌骨转移高瘤负荷的识别能力，且整合模型在临床常用决策阈值范围内具有正向净获益，可为个体化治疗提供更灵活的量化参考。

Abstract: Objectives: To investigate the predictive value of maximum tumor-to-background ratio (TBR) derived from whole-body bone scintigraphy combined with serum alkaline phosphatase (ALP) for stratifying bone metastasis burden in prostate cancer (PCa), and to evaluate whether ALP and TBR enhance the identification of high-burden status beyond clinical indicators (Gleason score and T stage), thereby providing a supplement to the traditional stratfication criteria. Methods: A retrospective analysis was conducted on 156 PCa patients with bone metastases admitted to the Affiliated Hospital of North China University of Science and Technology from July 2023 to August 2025. Patients were stratified into two subgroups: metastatic hormone-sensitive prostate cancer (mHSPC, n = 94) and metastatic castration-resistant prostate cancer (mCRPC, n = 62). Based on the CHAARTED criteria (for mHSPC) and SYMPHONY criteria (for mCRPC), patients were further classified into high- and low-burden groups. Clinicopathological characteristics, serum ALP levels, and TBR values were collected. Nested models were constructed: Model A (clinical indicators: Gleason score ≥ 8 + T3~4 stage), Model B (clinical indicators + ALP), and Model C (clinical indicators + ALP + TBR). The area under the receiver operating characteristic curve (AUC) and likelihood ratio tests were used to evaluate the predictive value of ALP and TBR beyond clinical indicators. Decision curve analysis (DCA) was used to evaluate the clinical net benefit of the integrated model (Model C). Results: Univariate analysis identified age, Gleason score ≥ 8, T3~4 stage, ALP, and TBR as significant factors in the mHSPC subgroup, and Gleason score ≥ 8, ALP, and TBR in the mCRPC subgroup (all P < 0.05). Multivariate analysis showed that the sequential addition of ALP and TBR to clinical indicators significantly improved model performance. In the mHSPC subgroup, the AUC increased from 0.689 (Model A) to 0.901 (Model B) and further to 0.934 (Model C); in the mCRPC subgroup, the AUC increased from 0.661 to 0.782 and further to 0.838 (all likelihood ratio tests P < 0.05). Model C was the optimal model in both subgroups. Conclusions: Serum ALP combine with TBR, on the basis of conventional clinicopathological indicators significantly improves the identification of high-burden status in prostate cancer bone metastases. The integrated model demonstrates positive net benefit within clinically relevant threshold ranges, offering a flexible quantitative reference for individualized treatment decisions.