Memorial Sloan Kettering Cancer Center, New York, NY.
Department of Radiology, New York Presbyterian Hospital/Columbia University Medical Center, New York, NY.
JCO Clin Cancer Inform. 2023 Sep;7:e2200203. doi: 10.1200/CCI.22.00203.
There are multiple approaches to modeling the relationship between longitudinal tumor measurements obtained from serial imaging and overall survival. Many require strong assumptions that are untestable and debatable. We illustrate how to apply a novel, more flexible approach, the partly conditional (PC) survival model, using images acquired during a phase III, randomized clinical trial in colorectal cancer as an example.
PC survival approaches were used to model longitudinal volumetric computed tomography data of 1,025 patients in the completed VELOUR trial, which evaluated adding aflibercept to infusional fluorouracil, leucovorin, and irinotecan for treating metastatic colorectal cancer. PC survival modeling is a semiparametric approach to estimating associations of longitudinal measurements with time-to-event outcomes. Overall survival was our outcome. Covariates included baseline tumor burden, change in tumor burden from baseline to each follow-up time, and treatment. Both unstratified and time-stratified models were investigated.
Without making assumptions about the distribution of the tumor growth process, we characterized associations between the change in tumor burden and survival. This change was significantly associated with survival (hazard ratio [HR], 1.04; 95% CI, 1.02 to 1.05; < .001), suggesting that aflibercept works at least in part by altering the tumor growth trajectory. We also found baseline tumor size prognostic for survival even when accounting for the change in tumor burden over time (HR, 1.02; 95% CI, 1.01 to 1.02; < .001).
The PC modeling approach offers flexible characterization of associations between longitudinal covariates, such as serially assessed tumor burden, and survival time. It can be applied to a variety of data of this nature and used as clinical trials are ongoing to incorporate new disease assessment information as it is accumulated, as indicated by an example from colorectal cancer.
有多种方法可以建立从连续影像学获得的纵向肿瘤测量值与总生存期之间的关系模型。许多方法都需要不可检验和有争议的强假设。我们通过一个结直肠癌 III 期随机临床试验的图像示例来说明如何应用一种新颖、更灵活的方法，即部分条件（PC）生存模型。
使用在 VELOUR 试验中完成的 1025 例患者的纵向容积 CT 数据来应用 PC 生存模型，该试验评估了在转移性结直肠癌中添加阿柏西普与氟尿嘧啶、亚叶酸钙和伊立替康联合治疗。PC 生存模型是一种半参数方法，用于估计纵向测量值与时间事件结局之间的关联。总生存期是我们的结果。协变量包括基线肿瘤负担、从基线到每次随访时间肿瘤负担的变化以及治疗。研究了无分层和时间分层模型。
我们没有对肿瘤生长过程的分布做出假设，而是描述了肿瘤负担变化与生存之间的关联。这种变化与生存显著相关（风险比[HR]，1.04；95%置信区间[CI]，1.02 至 1.05；<0.001），表明阿柏西普至少部分通过改变肿瘤生长轨迹起作用。即使考虑到随时间变化的肿瘤负担，我们还发现基线肿瘤大小对生存有预后意义（HR，1.02；95%CI，1.01 至 1.02；<0.001）。
PC 建模方法提供了对纵向协变量（如连续评估的肿瘤负担）与生存时间之间关联的灵活描述。它可以应用于各种此类性质的数据，并可在临床试验中使用，以在积累新的疾病评估信息时纳入这些信息，结直肠癌的一个示例说明了这一点。
