Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076, Tübingen, Germany.
Division of Dermatooncology, Department of Dermatology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, Liebermeisterstr. 25, 72074, Tuebingen, Germany.
Eur J Radiol. 2019 Dec;121:108688. doi: 10.1016/j.ejrad.2019.108688. Epub 2019 Oct 22.
We aimed to identify predictive clinical and CT imaging biomarkers and assess their predictive capacity regarding overall survival (OS) and treatment response in patients with metastatic melanoma undergoing immunotherapy.
The local institutional ethics committee approved this retrospective study and waived informed patient consent. 103 patients with immunotherapy for metastatic melanoma were randomly divided into training (n = 69) and validation cohort (n = 34). Baseline tumor markers (LDH, S100B), baseline CT imaging biomarkers (tumor burden, Choi density) and CT texture parameters (Entropy, Kurtosis, Skewness, uniformity, MPP, UPP) of the largest target lesion were extracted. To identify treatment response predictors, binary logistic regression analysis was performed in the training cohort and tested in the validation cohort. For OS, Cox regression and Kaplan Maier analyses were performed in the training cohort. Bivariate and multivariate models were established. Goodness of fit was assessed with Harrell's C-index. Potential predictors were tested in the validation cohort also using Cox-regression and Kaplan-Meier analyses.
Baseline S100B (Hazard ratio(HR) = 2.543, p0.018), tumor burden (HR = 1.657, p = 0.002) and Kurtosis (HR = 2.484, p < 0.001) were independent predictors of OS and were confirmed in the validation cohort (p < 0.048). Tumor burden and Kurtosis showed incremental predictive capacity allowing a good predictive model when combined with baseline S100B levels (C-index = 0.720). Only S100B was predictive of treatment response (OR ≤ 0.630, p ≤ 0.022). Imaging biomarkers did not predict treatment response.
We identified easily obtainable baseline clinical (S100B) and CT predictors (tumor burden and Kurtosis) of OS in patients with metastatic melanoma undergoing immunotherapy. However, imaging predictors did not predict treatment response.
我们旨在确定预测性临床和 CT 成像生物标志物，并评估它们对接受免疫治疗的转移性黑色素瘤患者的总生存（OS）和治疗反应的预测能力。
当地机构伦理委员会批准了这项回顾性研究，并豁免了患者的知情同意。103 名接受转移性黑色素瘤免疫治疗的患者被随机分为训练队列（n=69）和验证队列（n=34）。提取最大靶病灶的基线肿瘤标志物（LDH、S100B）、基线 CT 成像生物标志物（肿瘤负担、Choi 密度）和 CT 纹理参数（熵、峰度、偏度、均匀性、MPP、UPP）。为了确定治疗反应的预测因素，在训练队列中进行了二元逻辑回归分析，并在验证队列中进行了测试。对于 OS，在训练队列中进行了 Cox 回归和 Kaplan Maier 分析。建立了双变量和多变量模型。通过 Harrell 的 C 指数评估拟合优度。还使用 Cox 回归和 Kaplan-Meier 分析在验证队列中测试了潜在的预测因素。
基线 S100B（风险比（HR）=2.543，p0.018）、肿瘤负担（HR=1.657，p=0.002）和峰度（HR=2.484，p<0.001）是 OS 的独立预测因素，并在验证队列中得到证实（p<0.048）。肿瘤负担和峰度显示出增量预测能力，当与基线 S100B 水平相结合时，可以建立一个良好的预测模型（C 指数=0.720）。只有 S100B 对治疗反应有预测作用（OR≤0.630，p≤0.022）。成像生物标志物不能预测治疗反应。
我们确定了接受免疫治疗的转移性黑色素瘤患者的 OS 易于获得的基线临床（S100B）和 CT 预测因素（肿瘤负担和峰度）。然而，成像预测因素不能预测治疗反应。
