Mapping PSA density to outcome of MRI-based active surveillance for prostate cancer through joint longitudinal-survival models.
Mapping PSA density to outcome of MRI-based active surveillance for prostate cancer through joint longitudinal-survival models.
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DOI：
10.1038/s41391-021-00373-w
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发表时间：
2021-12
影响因子：
4.8
通讯作者：
Moore CM
中科院分区：
文献类型：
--
作者：
Stavrinides V;Papageorgiou G;Danks D;Giganti F;Pashayan N;Trock B;Freeman A;Hu Y;Whitaker H;Allen C;Kirkham A;Punwani S;Sonn G;Barratt D;Emberton M;Moore CM
多参数磁共振成像（mpMRI）用于局部前列腺癌的主动监测（AS）的使用正在增加，有证据表明，mpMRI有助于选择AS候选人，同时最大限度地减少对随访活检的需求[1]。由于前列腺癌的自然史尚未完全确定，因此许多AS时间表仍然是规定性的，这并不奇怪[2]。定期的、基于方案的活检使参与者符合抽样方案，从而以临床试验设计的方式减少了关于风险因素与疾病进展之间关系的偏倚推断。然而，尽管这种更严格的方法让临床医生放心，但它与个性化医疗的原则背道而驰，其中关于随访或治疗的决定应该由每个患者的唯一纵向轨迹动态地通知。在基于MRI的AS中，这种冲突可以通过前列腺特异性抗原（PSA）或PSA密度（PSAD）来证明：尽管两者都与疾病进展或治疗相关，但现有研究主要关注基线PSA或PSAD值，而不是纵向趋势，这在通常跨越数年的监测期内更具临床相关性。在某种程度上，这种不足可归因于方法学限制;标准逻辑回归对于处理纵向测量并不理想，而扩展的考克斯模型假设时变协变量的分段恒定、无测量误差的轨迹，并且对于建模内源性生物标志物（如PSAD）并不理想[3-5]。动态风险预测方法可以满足这一需求。一个很好的例子是联合生存模型：这些模型比传统的生存分析有明显的优势，因为它们考虑了所有的纵向测量，
The use of multiparametric magnetic resonance imaging (mpMRI) for the active surveillance (AS) of localised prostate cancers is increasing, and evidence suggests that mpMRI facilitates the selection of AS candidates while minimising the need for follow-up biopsies [1]. As the natural history of prostate cancer is not entirely defined, it is unsurprising that that many AS schedules remain prescriptive [2]. Regular, protocol-based biopsies condition participants on sampling scheme, allowing less biased inferences regarding the relationship between risk factors and disease progression in ways reminiscent of clinical trial design.However, although this more rigid approach is reassuring to clinicians, it is antithetical to the principles of personalised medicine, where decisions on follow up or treatment should be dynamically informed by the unique longitudinal trajectory of each patient. In MRI-based AS this conflict can be demonstrated for prostate-specific antigen (PSA) or PSA density (PSAD): although both have been associated with progression or treatment, existing studies predominantly focus on baseline PSA or PSAD values rather than longitudinal trends, which are more clinically relevant over surveillance periods that often span several years. In part, this shortfall can be attributed to methodological limitations; standard logistic regression is not ideal for dealing with longitudinal measurements, whereas extended Cox models assume piecewise-constant, measurement error-free trajectories for time-varying covariates and are not optimal for modelling endogenous biomarkers such as PSAD [3–5]. Dynamic risk prediction methods could address this need. A good example is joint longitudinal-survival models: these have a distinct advantage over traditional survival analyses, as they consider all longitudinal measurements of
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影响因子：
23.4
作者：
Stavrinides, Vasilis;Giganti, Francesco;Moore, Caroline M.
通讯作者：
Moore, Caroline M.
DOI：
10.1146/annurev-statistics-030718-105048
发表时间：
2019-01-01
期刊：
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 6
影响因子：
--
作者：
Papageorgiou, Grigorios;Mauff, Katya;Rizopoulos, Dimitris
通讯作者：
Rizopoulos, Dimitris
影响因子：
2.1
作者：
Fam, Mina M.;Yabes, Jonathan G.;Jacobs, Bruce L.
通讯作者：
Jacobs, Bruce L.
影响因子：
5.8
作者：
Rizopoulos, Dimitris
通讯作者：
Rizopoulos, Dimitris
影响因子：
2.6
作者：
Giganti, Francesco;Stavrinides, Vasilis;Moore, Caroline M.
通讯作者：
Moore, Caroline M.