Wu Muyu, Zhang Yucan, Zhang Xiaoqun, Lin Xiaozhu, Ding Qiaoqiao, Li Peiyong
Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
Heliyon. 2024 Jan 8;10(2):e24292. doi: 10.1016/j.heliyon.2024.e24292. eCollection 2024 Jan 30.
Early screening of prostate cancer (PCa) is pivotal but challenging in the clinical scenario due to the phenomena of false positivity or false negativity of some serological evaluations, e.g. PSA testing. Decline of serum Zn levels in PCa patients reportedly plays a crucial role in early screening of PCa. Accordingly, we combined 4 indices comprising the serum levels of total PSA (tPSA), free PSA (fPSA), Zn and demographic information (especially age) in order to ameliorate the efficacies of PCa screening with support vector machine (SVM) algorithms.
A total of 858 male patients with prostate disorders and 345 healthy male controls were enrolled. Patients' data included 4 variables and serum Zn was quantified via a self-invented Zn responsive AIE-based fluorescent probe as previously published. tPSA and fPSA were routinely determined by a chemiluminescent method. Mathematical simulations were conducted to establish a SVM model for the combined diagnostics with the four variables. Moreover, ROC and its characteristic AUC were also employed to evaluate the classification efficacy of the model. Sigmoid function was utilized to estimate corresponding probabilities of classifying the clinical subjects as per 5 grades, which were incorporated into our established prostate index (PI) stratification system.
In SVM model, the mean AUC of the ROC with the quartet of variables was approximately 84% for PCa diagnosis, whereas the mean AUC of the ROCs with tPSA, fPSA, [Zn] or age alone was 64%, 62%, 55% and 59%, respectively. We further established an integrated prostate index (PI) stratification system with 5 grades and a software package to support clinicians in predicting PCa, with the accuracy of our risk stratification system being 83.3%, 91.6% and 83.3% in predicting normal, benign and PCa cases in corresponding groups. Follow-up findings especially MRI results and PI-RADS scores supported the reliability of this stratification platform as well.
Findings from our present study demonstrated that index combination via SVM algorithms may well facilitate clinicians in early differential screening of PCa. Meanwhile, our established PI stratification system based on SVM model and Sigmoid function provided substantial accuracy in preclinical risk prediction of developing prostate cancer.
前列腺癌（PCa）的早期筛查至关重要，但在临床实际中却颇具挑战，这是因为一些血清学评估（如PSA检测）存在假阳性或假阴性现象。据报道，PCa患者血清锌水平的下降在PCa的早期筛查中起着关键作用。因此，我们将总PSA（tPSA）、游离PSA（fPSA）、锌的血清水平以及人口统计学信息（尤其是年龄）这4项指标相结合，以借助支持向量机（SVM）算法提高PCa筛查的效果。
共纳入858例患有前列腺疾病的男性患者和345例健康男性对照。患者数据包括4个变量，血清锌通过一种先前发表的基于具有锌响应性的聚集诱导发光（AIE）荧光探针进行定量检测。tPSA和fPSA采用化学发光法常规测定。进行数学模拟以建立包含这4个变量的联合诊断SVM模型。此外，还采用ROC及其特征性AUC评估该模型的分类效能。利用Sigmoid函数根据5个等级估计将临床受试者分类的相应概率，并将其纳入我们建立的前列腺指数（PI）分层系统。
在SVM模型中，用于PCa诊断的包含这4个变量的ROC曲线平均AUC约为84%，而单独使用tPSA、fPSA、[锌]或年龄的ROC曲线平均AUC分别为64%、62%、55%和59%。我们进一步建立了一个具有5个等级的综合前列腺指数（PI）分层系统以及一个软件包，以支持临床医生预测PCa，我们的风险分层系统在预测相应组中的正常、良性和PCa病例时的准确率分别为83.3%、91.6%和83.3%。随访结果尤其是MRI结果和PI-RADS评分也支持了这个分层平台的可靠性。
我们目前的研究结果表明，通过SVM算法进行指标组合能够很好地帮助临床医生对PCa进行早期鉴别筛查。同时，我们基于SVM模型和Sigmoid函数建立的PI分层系统在前列腺癌发生的临床前风险预测中具有较高的准确性。
