PSAMR联合PI-RADS评分对临床意义前列腺癌的诊断能力探讨及列线图预测模型的建立和验证,Journal of Cancer Research and Clinical Oncology

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PSAMR联合PI-RADS评分对临床意义前列腺癌的诊断能力探讨及列线图预测模型的建立和验证
Journal of Cancer Research and Clinical Oncology ( IF 2.7 ) Pub Date : 2023-06-27 , DOI: 10.1007/s00432-023-05008-2
Dengke Li ₁ , Lulu Zhang ₁ , Yujie Xu ₁ , Xun Wu ₁ , Shaokui Hua ₁ , Yan Jiang ₁ , Qunlian Huang ₁ , Yukui Gao ₁

Affiliation

目的

本研究的目的是探讨前列腺特异性抗原质量比（PSAMR）结合前列腺成像报告和数据系统（PI-RADS）评分对临床显着前列腺癌（CSPC）的诊断能力，开发并验证列线图预测模型未接受前列腺活检的患者发生前列腺癌的概率。

方法

我们首先回顾性收集2021年7月至2023年1月在万安医学院弋矶山医院接受经会阴前列腺穿刺术的患者的临床和病理资料，通过logistic单因素和多因素回归分析，确定CSPC的独立危险因素。生成受试者工作特征 (ROC) 曲线来比较不同因素诊断 CSPC 的能力。然后，我们将数据集分为训练集和验证集，比较它们的异质性，并基于训练集开发诺模图预测模型。最后，我们在区分、校准和临床实用性方面验证了列线图预测模型。

结果

Logistic多元回归分析表明，年龄[64-69（OR = 2.736，P = 0.029）；69–75（OR = 4.728，P = 0.001）；> 75（OR = 11.344，P < 0.001）]，PSAMR [0.44–0.73（OR = 4.144，P = 0.028）；0.73–1.64（OR = 13.022，P < 0.001）；> 1.64（OR = 50.541，P < 0.001）]，PI-RADS 评分 [4 分（OR = 7.780，P < 0.001）；5分(OR = 24.533, P < 0.001)]是CSPC的独立危险因素。PSA、PSAMR、PI-RADS评分以及PSAMR联合PI-RADS评分的ROC曲线的曲线下面积(AUC)分别为0.797、0.874、0.889和0.928。PSAMR和PI-RADS评分对CSPC的诊断性能优于PSA，但逊色于PSAMR联合PI-RADS。年龄、PSAMR 和 PI-RADS 均包含在列线图预测模型中。在区分验证中，训练集 ROC 曲线和验证集 ROC 曲线的 AUC 分别为 0.943（95% CI 0.917–0.970）和 0.878（95% CI 0.816–0.940）。校准曲线一致性良好，决策分析曲线表明模型具有良好的临床疗效。

结论

我们发现PSAMR结合PI-RADS评分对CSPC有很强的诊断能力，并提供了Nomogram预测模型，结合临床数据预测前列腺癌发生的概率。

"点击查看英文标题和摘要"

Exploration of the diagnostic capacity of PSAMR combined with PI-RADS scoring for clinically significant prostate cancer and establishment and validation of the Nomogram prediction model

Purpose

The objective of this investigation was to explore the diagnostic capability of Prostate Specific Antigen Mass Ratio (PSAMR) combined with Prostate Imaging Reporting and Data System (PI-RADS) scoring for clinically significant prostate cancer (CSPC), develop and validate a Nomogram prediction model for the probability of prostate cancer occurrence in patients who have not undergone prostate biopsy.

Methods

Initially, we retrospectively collected clinical and pathological data of patients who underwent trans-perineal prostate puncture at Yijishan Hospital of Wanan Medical College from July 2021 to January 2023. Through logistic univariate and multivariate regression analysis, independent risk factors for CSPC were determined. Receiver Operating Characteristic (ROC) curves were generated to compare the ability of different factors for diagnosis of CSPC. Then, we split the dataset into a training set and validation set, compared their heterogeneity, and developed a Nomogram prediction model based on the training set. Finally, we validated the Nomogram prediction model in terms of discrimination, calibration, and clinical usefulness.

Results

Logistic multivariate regression analysis illustrated that age [64–69 (OR = 2.736, P = 0.029); 69–75 (OR = 4.728, P = 0.001); > 75 (OR = 11.344, P < 0.001)], PSAMR [0.44–0.73 (OR = 4.144, P = 0.028); 0.73–1.64(OR = 13.022, P < 0.001); > 1.64(OR = 50.541, P < 0.001)], and PI-RADS score [4 points (OR = 7.780, P < 0.001); 5 points (OR = 24.533, P < 0.001)] were independent risk factors for CSPC. The Area Under the Curve (AUC) of the ROC curves of PSA, PSAMR, PI-RADS score, and PSAMR combined with PI-RADS score were respectively 0.797, 0.874, 0.889, and 0.928. The performance of PSAMR and PI-RADS score for diagnosis of CSPC was superior to PSA, but inferior to PSAMR combined with PI-RADS. Age, PSAMR, and PI-RADS were included in the Nomogram prediction model. The AUCs of the training set ROC curve and the validation set ROC curve were 0.943 (95% CI 0.917–0.970) and 0.878 (95% CI 0.816–0.940), respectively, in the discrimination validation. The calibration curve showed good consistency, and the decision analysis curve suggested the model had good clinical efficacy.

Conclusions

We found that PSAMR combined with PI-RADS scoring had a strong diagnostic capability for CSPC, and provided a Nomogram prediction model to predict the probability of prostate cancer occurrence combined with clinical data.

更新日期：2023-06-27

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