Lu Yangbai, Li Binfei, Huang Hongxing, Leng Qu, Wang Qiang, Zhong Rui, Huang Yaqiang, Li Canyong, Yuan Runqiang, Zhang Yongxin
Department of Urology, Zhongshan City People's Hospital, Zhongshan, Guangdong, China.
Department of Anesthesiology, Zhongshan City People's Hospital, Zhongshan, Guangdong, China.
Front Oncol. 2022 Dec 5;12:1020317. doi: 10.3389/fonc.2022.1020317. eCollection 2022.
To investigate the predictive performance of the combined model by integrating clinical variables and radiomic features for the accurate detection of prostate cancer (PCa) in patients with prostate-specific antigen (PSA) serum levels of 4-10 ng/mL.
A retrospective study of 136 males (mean age, 67.3 ± 8.4 years) with Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 category ≤3 lesions and PSA serum levels of 4-10 ng/mL were performed. All patients underwent multiparametric MRI at 3.0T and transrectal ultrasound-guided systematic prostate biopsy in their clinical workup. Radiomic features were extracted from axial T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps of each patient using PyRadiomics. Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were implemented to identify the most significant radiomic features. Independent clinic-radiological factors were identified univariate and multivariate regression analyses. Seven machine-learning algorithms were compared to construct a single-layered radiomic score (ie, radscore) and multivariate regression analysis was applied to construct the fusion radscore. Finally, the radiomic nomogram was further developed by integrating useful clinic-radiological factors and fusion radscore using multivariate regression analysis. The discriminative power of the nomogram was evaluated by area under the curve (AUC), DeLong test, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).
The transitional zone-specific antigen density was identified as the only independent clinic-radiological factor, which yielded an AUC of 0.592 (95% confidence interval [CI]: 0.527-0.657). The ADC radscore based on six features and Naive Bayes achieved an AUC of 0.779 (95%CI: 0.730-0.828); the T2WI radscore based on 13 features and Support Vector Machine yielded an AUC of 0.808 (95%CI: 0.761-0.855). The fusion radscore obtained an improved AUC of 0.844 (95%CI: 0.801-0.887), which was higher than the single-layered radscores (both P<0.05). The radiomic nomogram achieved the highest value among all models (all P<0.05), with an AUC of 0.872 (95%CI: 0.835-0.909). Calibration curve showed good agreement and DCA together with CIC confirmed the clinical benefits of the radiomic nomogram.
The radiomic nomogram holds the potential for accurate and noninvasive identification of PCa in patients with PI-RADS ≤3 lesions and PSA of 4-10 ng/mL, which could reduce unnecessary biopsy.
通过整合临床变量和影像组学特征，研究联合模型对前列腺特异性抗原（PSA）血清水平为4 - 10 ng/mL的患者中前列腺癌（PCa）的准确检测的预测性能。
对136例男性（平均年龄67.3±8.4岁）进行回顾性研究，这些患者的前列腺影像报告和数据系统（PI-RADS）v2.1类别≤3级病变且PSA血清水平为4 - 10 ng/mL。所有患者在临床检查中均接受了3.0T多参数MRI和经直肠超声引导下的系统性前列腺穿刺活检。使用PyRadiomics从每位患者的轴向T2加权图像（T2WI）和表观扩散系数（ADC）图中提取影像组学特征。采用Pearson相关系数（PCC）和递归特征消除（RFE）来识别最显著的影像组学特征。通过单因素和多因素回归分析确定独立的临床-放射学因素。比较七种机器学习算法以构建单层影像组学评分（即radscore），并应用多因素回归分析构建融合radscore。最后，通过多因素回归分析整合有用的临床-放射学因素和融合radscore，进一步开发影像组学列线图。通过曲线下面积（AUC）、DeLong检验、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）评估列线图的判别能力。
移行区特异性抗原密度被确定为唯一的独立临床-放射学因素，其AUC为0.592（95%置信区间[CI]：0.527 - 0.657）。基于六个特征和朴素贝叶斯的ADC radscore的AUC为0.779（95%CI：0.730 - 0.828）；基于13个特征和支持向量机的T2WI radscore的AUC为0.808（95%CI：0.761 - 0.855）。融合radscore的AUC提高到0.844（95%CI：0.801 - 0.887），高于单层radscore（均P<0.05）。影像组学列线图在所有模型中具有最高值（均P<0.05），AUC为0.872（95%CI：0.835 - 0.909）。校准曲线显示出良好的一致性，DCA和CIC共同证实了影像组学列线图的临床益处。
影像组学列线图具有准确、无创识别PI-RADS≤3级病变且PSA为4 - 10 ng/mL的患者中PCa的潜力，这可以减少不必要的活检。
