Prostate Cancer P D:通过机器学习技术改善前列腺癌的诊断

2021-07-21 AlexYang MedSci原创

使用血液检测前列腺特异性抗原(PSA)来早期检测前列腺癌,可以诊断较早期和器官限制阶段的前列腺癌。然而,使用PSA作为前列腺活检决定的唯一工具,也导致了很多不必要的活检和对无痛性前列腺癌的过度诊断。最

使用血液检测前列腺特异性抗原(PSA)来早期检测前列腺癌,可以诊断较早期和器官限制阶段的前列腺癌。然而,使用PSA作为前列腺活检决定的唯一工具,也导致了很多不必要的活检和对无痛性前列腺癌的过度诊断。最终导致了对这些早期癌症的过度治疗,从而无法使所有患者受益。虽然在ERSPC试验中,PSA筛查可以减少转移性疾病和癌症死亡率,但在PSA筛查应用于人群之前,还需要解决不必要的活检和治疗(手术或放疗)的危害。

近期,有研究人员将2003年1月至2017年6月进行的连续系统性前列腺活检作为训练队列,将2017年7月至2019年11月的前瞻性活检作为验证队列,调查了机器学习技术在改善前列腺癌诊断中的价值。如果PSA为0.4-50 ng/ml,并且已知数字直肠检查(DRE)、经直肠超声(TRUS)前列腺体积、TRUS异常等信息,则纳入研究。临床意义上的PCa(csPCa)定义为Gleason 3+4或以上。在PSA、PSA密度、欧洲前列腺癌筛查随机研究(ERSPC)风险计算(ERSPC-RC)和使用PSA、DRE和TRUS信息的各种ML技术之间比较了接受者操作特征(ROC)的曲线下面积(AUC)。使用的ML技术包括XGBoost、LightGBM、Catboost、支持向量机(SVM)、逻辑回归(LR)和随机森林(RF)。

训练和验证队列分别包括3881名和778名连续男性。在验证队列中,RF模型在预测PCa或csPCa方面的表现优于其他ML技术和PSA、PSA密度和ERSPC-RC。在csPCa预测中,PSA、PSA密度、ERSPC-RC和RF的AUC分别为0.71、0.80、0.83和0.88。对csPCa的敏感性为90-95%时,RF模型的阴性预测值(NPV)为97.5-98.0%,避免了38.3-52.2%比例的不必要活检。决策曲线分析(DCA)显示RF模型比PSA、PSA密度和ERSPC-RC提供了更好的净临床效益。

决策曲线分析

综上所述,通过使用相同的临床参数,ML技术在预测csPCa方面的表现优于ERSPC-RC或PSA密度,并能避免高达50%的不必要活检

原始出处:

Peter Ka-Fung Chiu, Xiao Shen, Guanjin Wang et al. Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study. Prostate Cancer P D. Jul 2021

 

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    2021-07-23 lsndxfj
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    2021-07-23 sunylz
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    2021-07-21 misszhang

    前列腺癌相关研究,学习了,谢谢梅斯

    0

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