DeVries David A, Tang Terence, Alqaidy Ghada, Albweady Ali, Leung Andrew, Laba Joanna, Lagerwaard Frank, Zindler Jaap, Hajdok George, Ward Aaron D
Department of Medical Biophysics, Western University, London, ON, Canada.
Gerald C. Baines Centre, London Health Sciences Centre, London, ON, Canada.
Neurooncol Adv. 2023 May 27;5(1):vdad064. doi: 10.1093/noajnl/vdad064. eCollection 2023 Jan-Dec.
MRI radiomic features and machine learning have been used to predict brain metastasis (BM) stereotactic radiosurgery (SRS) outcomes. Previous studies used only single-center datasets, representing a significant barrier to clinical translation and further research. This study, therefore, presents the first dual-center validation of these techniques.
SRS datasets were acquired from 2 centers ( = 123 BMs and = 117 BMs). Each dataset contained 8 clinical features, 107 pretreatment T1w contrast-enhanced MRI radiomic features, and post-SRS BM progression endpoints determined from follow-up MRI. Random decision forest models were used with clinical and/or radiomic features to predict progression. 250 bootstrap repetitions were used for single-center experiments.
Training a model with one center's dataset and testing it with the other center's dataset required using a set of features important for outcome prediction at both centers, and achieved area under the receiver operating characteristic curve (AUC) values up to 0.70. A model training methodology developed using the first center's dataset was locked and externally validated with the second center's dataset, achieving a bootstrap-corrected AUC of 0.80. Lastly, models trained on pooled data from both centers offered balanced accuracy across centers with an overall bootstrap-corrected AUC of 0.78.
Using the presented validated methodology, radiomic models trained at a single center can be used externally, though they must utilize features important across all centers. These models' accuracies are inferior to those of models trained using each individual center's data. Pooling data across centers shows accurate and balanced performance, though further validation is required.
MRI放射组学特征和机器学习已被用于预测脑转移瘤（BM）立体定向放射外科（SRS）的疗效。以往的研究仅使用单中心数据集，这对临床转化和进一步研究构成了重大障碍。因此，本研究首次对这些技术进行了双中心验证。
从2个中心获取SRS数据集（一个中心有123个BM，另一个中心有117个BM）。每个数据集包含8个临床特征、107个治疗前T1加权对比增强MRI放射组学特征以及根据随访MRI确定的SRS后BM进展终点。使用随机决策森林模型结合临床和/或放射组学特征来预测进展。单中心实验使用250次自助重复抽样。
用一个中心的数据集训练模型并在另一个中心的数据集上进行测试，需要使用对两个中心的疗效预测都很重要的一组特征，并且受试者操作特征曲线（AUC）下面积值高达0.70。使用第一个中心的数据集开发的模型训练方法被锁定，并在第二个中心的数据集上进行外部验证，自助校正后的AUC为0.80。最后，在两个中心的汇总数据上训练的模型在各中心之间提供了平衡的准确性，总体自助校正后的AUC为0.78。
使用所提出的经过验证的方法，在单个中心训练的放射组学模型可以在外部使用，尽管它们必须利用对所有中心都重要的特征。这些模型的准确性低于使用每个中心单独数据训练的模型。跨中心汇总数据显示出准确且平衡的性能，不过还需要进一步验证。
