Stoyanova Radka, Takhar Mandeep, Tschudi Yohann, Ford John C, Solórzano Gabriel, Erho Nicholas, Balagurunathan Yoganand, Punnen Sanoj, Davicioni Elai, Gillies Robert J, Pollack Alan
Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.
Research and Development, GenomeDx Biosciences, Vancouver, BC, Canada.
Transl Cancer Res. 2016 Aug;5(4):432-447. doi: 10.21037/tcr.2016.06.20.
Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinical-pathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated.
前列腺癌表现出肿瘤内异质性,我们推测这是导致当前治疗前临床病理和基因组评估表现不佳的主要混杂因素。这些局限性迫切需要开发更好的计算工具,以识别前列腺癌低风险男性与其他可能有发生转移性癌症风险的男性。患者分层将直接转化为患者治疗,即做出关于主动监测或强化治疗的决策。多参数磁共振成像(mpMRI)提供了一个平台,通过绘制个体肿瘤栖息地来研究肿瘤异质性。我们假设对这些栖息地进行定量评估(放射组学)会产生不同的描述符组合,揭示具有不同生理学和表型的区域。放射基因组学是一门将放射组学描述的肿瘤形态与其基因组数据描述的基因组联系起来的学科,有潜力得出与现有经过验证的基因组风险分层生物标志物相关并互补的“放射表型”。在本文中,我们首先描述为分析前列腺mpMRI量身定制的放射组学流程,并在此过程中介绍我们对放射组学模块的具体实现。我们还总结了放射组学领域在前列腺癌诊断和侵袭性评估方面的努力。最后,我们描述基于mpMRI-超声(MRI-US)活检的放射基因组分析结果,并讨论该技术未来应用的潜力。mpMRI放射组学数据表明,该平台将通过更好地识别惰性疾病与侵袭性疾病,显著改善前列腺栖息地的活检靶向,从而促进更个性化的前列腺癌管理方法。非侵入性识别极有可能存在侵袭性癌症的栖息地的期望将导致更具信息性和可操作性的定向活检。相反,提供无疾病证据将减少无信息活检的发生率。在前列腺癌放疗中,剂量递增已被证明可减少生化失败。仅对确定的前列腺栖息地进行剂量递增有可能在毒性低于整个前列腺剂量递增的情况下改善肿瘤控制。