Institute of Informatics and Telematics (IIT), CNR, 56124, Pisa, Italy.
Sci Rep. 2023 May 15;13(1):7875. doi: 10.1038/s41598-023-35023-9.
Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods.
局部前列腺癌是一种非常异质的疾病，无论是从临床角度还是从生物/生化角度来看，这使得对患者进行风险分层的任务极具挑战性。特别是，早期检测和区分疾病的惰性形式和侵袭性形式非常重要，需要术后更密切的监测和及时的治疗决策。这项工作通过引入一种新的模型选择技术扩展了最近开发的一种监督机器学习 (ML) 技术，称为一致投票网络 (CVN)，以应对模型过拟合的危险。对于区分局部前列腺癌的惰性和侵袭性类型这一具有挑战性的问题，通过在一年内实现更精细的粒度对术后无进展生存进行准确的预后预测，提高了相对于现有技术水平的准确性。针对结合多组学和临床预后生物标志物的问题开发新的 ML 技术是一种有前途的新方法，可以提高多样化和个性化癌症患者治疗的能力。所提出的方法允许在临床高危类别中对患者进行更精细的术后分层，这可能对监测方案和治疗决策的时机产生影响，补充现有的预后方法。
