Background The early detection of clinically significant prostate cancer (csPCa) through the integration of multidimensional parameters presents a promising avenue for improving survival outcomes for this fatal disease. This study aimed to assess the contribution of prostate transition zone (TZ) to predictive models based on the prostate health index (PHI), with the goal of enhancing early detection of csPCa in the prostate-specific antigen (PSA) gray zone. Methods In this observational cross-sectional study, a total of 177 PSA gray zone patients (total prostate-specific antigen [tPSA] level ranging from 4.0 to 10.0 ng/mL) were recruited and received PHI detections from August 2020 to March 2022. Prostatic morphologies especially the TZ morphological parameters were measured by transrectal ultrasound (TRUS). Results Univariable logistic regression indicated prostatic morphological parameters including total prostate volume (PV) indexes and transitional zone volume indexes were all associated with csPCa (P < .05), while the multivariable analysis demonstrated that C-reactive protein (CRP), PHI, PHI density (PHID), and PHI transition zone density (PHI-TZD) were the 4 independent risk factors. The receiver-operating characteristic (ROC) curve analysis suggested that integrated predictive models (PHID, PHI-TZD) yield area under the curves (AUCs) of 0.9135 and 0.9105 in csPCa prediction, which shows a relatively satisfactory predictive capability compared with other predictors. Moreover, the PHI-TZD outperformed PHID by avoiding 30 patients' unnecessary biopsies while maintaining 74.36% specificity at a sensitivity of 90%. Decision-curve analysis (DCA) confirmed the comparable performance of the multivariable full-risk prediction models, without the inclusion of the net benefit, thereby highlighting the superior diagnostic efficacy of PHID and PHI-TZD in comparison with other diagnostic models, in both univariable and multivariable models. Conclusion Our data confirmed the value of prostate TZ morphological parameters and suggested a significant advantage for the TZ-adjusted PHI predictive model (PHI-TZD) compared with PHI and PHID in the early detection of gray zone csPCa under specific conditions.

中文翻译：

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评估前列腺移行区形态参数在基于 PHI 的预测模型中检测灰区前列腺癌的作用。

背景 通过整合多维参数对具有临床意义的前列腺癌 (csPCa) 进行早期检测，为改善这种致命疾病的生存结果提供了一条有希望的途径。本研究旨在评估前列腺过渡区 (TZ) 对基于前列腺健康指数 (PHI) 的预测模型的贡献，目的是增强前列腺特异性抗原 (PSA) 灰色区域中 csPCa 的早期检测。方法 在这项观察性横断面研究中，共招募了 177 名 PSA 灰区患者（总前列腺特异性抗原 [tPSA] 水平为 4.0 至 10.0 ng/mL），并于 2020 年 8 月至 2022 年 3 月接受了 PHI 检测。通过经直肠超声（TRUS）测量形态，尤其是 TZ 形态参数。结果 单变量logistic回归显示前列腺形态参数，包括总前列腺体积（PV）指数和移行区体积指数均与csPCa相关（P < .05），而多变量分析表明，C反应蛋白（CRP）、PHI、PHI密度（PHID）和 PHI 过渡区密度（PHI-TZD）是 4 个独立的危险因素。受试者工作特征（ROC）曲线分析表明，综合预测模型（PHID、PHI-TZD）在csPCa预测中的曲线下面积（AUC）分别为0.9135和0.9105，与其他预测因子相比，显示出相对令人满意的预测能力。此外，PHI-TZD 优于 PHID，避免了 30 名患者不必要的活检，同时保持 74.36% 的特异性和 90% 的灵敏度。决策曲线分析 (DCA) 证实了多变量全风险预测模型的可比性能（不包含净收益），从而突出了 PHID 和 PHI-TZD 与其他诊断模型相比，在单变量中的卓越诊断功效和多变量模型。结论 我们的数据证实了前列腺 TZ 形态参数的价值，并表明 TZ 调整 PHI 预测模型 (PHI-TZD) 与 PHI 和 PHID 相比，在特定条件下早期检测灰区 csPCa 方面具有显着优势。