Wolff Henri B, Alberts Leonie, Kastelijn Elisabeth A, Verstegen Naomi E, El Sharouni Sherif Y, Schramel Franz M N H, Vos Rein, Coupé Veerle M H
Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Location VUmc, 1081 HV Amsterdam, The Netherlands.
Department of Pulmonology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands.
Cancers (Basel). 2021 Apr 14;13(8):1884. doi: 10.3390/cancers13081884.
Metachronous oligo-metastatic disease is variably defined as one to five metastases detected after a disease-free interval and treatment of the primary tumour with curative intent. Oligo-metastases in non-small cell lung cancer (NSCLC) are often treated with curative intent. However additional metastases are often detected later in time, and the 5-year survival is low. Burdensome surgical treatment in patients with undetected metastases may be avoided if patients with a high versus low risk of undetected metastases can be separated. Because there is no clinical data on undetected metastases available, a microsimulation model of the development and detection of metastases in 100,000 hypothetical stage I NSCLC patients with a controlled primary tumour was constructed. The model uses data from the literature as well as patient-level data. Calibration was used for the unobservable model parameters. Metastases can be detected by a scheduled scan, or an unplanned scan when the patient develops symptoms. The observable information at time of detection is used to identify subgroups of patients with a different risk of undetectable metastases. We identified the size and number of detected oligo-metastases, as well as the presence of symptoms that are the most important risk predictors. Based on these predictors, patients could be divided into a low-risk and a high-risk group, having a model-based predicted probability of 8.1% and 89.3% to have undetected metastases, respectively. Currently, the model is based on a synthesis of the literature data and individual patient-level data that were not collected for the purpose of this study. Optimization and validation of the model is necessary to allow clinical usability. We describe the type of data that needs to be collected to update our model, as well as the design of such a validation study.
异时性寡转移疾病的定义不一，通常指在无疾病间隔期且对原发性肿瘤进行了根治性治疗后检测到的1至5处转移灶。非小细胞肺癌（NSCLC）中的寡转移灶通常采用根治性治疗。然而，后续往往会检测到更多转移灶，且5年生存率较低。如果能够区分未检测到转移灶的高风险和低风险患者，就可以避免对存在未检测到转移灶的患者进行繁重的手术治疗。由于缺乏关于未检测到转移灶的临床数据，因此构建了一个微观模拟模型，用于模拟100,000名假设的I期NSCLC患者在原发性肿瘤得到控制后转移灶的发生和检测情况。该模型使用了文献数据以及患者层面的数据。对不可观测的模型参数进行了校准。转移灶可通过定期扫描或患者出现症状时的非计划扫描检测到。检测时的可观测信息用于识别未检测到转移灶风险不同的患者亚组。我们确定了检测到的寡转移灶的大小和数量，以及作为最重要风险预测因素的症状的存在情况。基于这些预测因素，患者可分为低风险组和高风险组，基于模型预测，其未检测到转移灶的概率分别为8.1%和89.3%。目前，该模型基于文献数据和个体患者层面数据的综合，这些数据并非为本研究目的而收集。为了使模型具有临床实用性，有必要对其进行优化和验证。我们描述了更新模型所需收集的数据类型，以及此类验证研究的设计。
