Department of Therapeutic Radiology, Yale School of Medicine, New Haven, USA.
Massachusetts Institute of Technology, Cambridge, USA.
Sci Rep. 2021 May 7;11(1):9758. doi: 10.1038/s41598-021-89114-6.
Radiomic feature analysis has been shown to be effective at analyzing diagnostic images to model cancer outcomes. It has not yet been established how to best combine radiomic features in cancer patients with multifocal tumors. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognosis to better inform treatment. We compared six mathematical methods of combining radiomic features of 3,596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest. Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model. This finding was consistent when evaluating patients with different numbers of brain metastases and different tumor volumes. Radiomic features can be effectively combined to estimate patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and tumor types.
放射组学特征分析已被证明可有效分析诊断图像以建立癌症预后模型。但目前尚不清楚如何将具有多灶性肿瘤的癌症患者的放射组学特征最佳地结合起来。随着具有多灶性转移性癌症的患者数量不断增加，需要改善对患者个体水平预后的预测，以便更好地为治疗提供信息。我们比较了 831 名多发性脑转移患者的 3596 个肿瘤的 6 种数学特征组合方法，并使用 3 种生存模型评估了这些聚合方法的性能：标准 Cox 比例风险模型、具有 LASSO 回归的 Cox 比例风险模型和随机生存森林。在所有三种生存模型中，最大的三个转移灶的加权平均值在 Cox 比例风险模型中的一致性指数（95%置信区间）最高，为 0.627（0.595-0.661），在 Cox 比例风险模型与 LASSO 回归模型中为 0.628（0.591-0.666），在随机生存森林模型中为 0.652（0.565-0.727）。当评估具有不同数量脑转移灶和不同肿瘤体积的患者时，这种发现是一致的。放射组学特征可以有效地结合起来，以估计具有多灶性脑转移的患者的个体水平结局。需要进一步的研究来证实，对最大的三个肿瘤的体积加权平均值是在其他成像方式和肿瘤类型中组合放射组学特征的有效方法。
