Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.

可以识别可能对一线治疗无效的弥漫性大 B 细胞淋巴瘤 (DLBCL) 的生物标志物对于在治疗开始前选择该人群以提供可改善预后的替代治疗选择至关重要。我们通过机器学习测试了基于 CT 的放射组学方法从初始成像评估预测主要治疗失败 (PTF)-DLBCL 的能力。26 名难治性患者与 26 名非难治性患者相匹配，产生 180 个淋巴结进行分析。由两个独立的阅读器对总节点体积进行手动 3D 描绘，以测试可重复性。然后，提取了 1218 个手工制作的放射组学特征。随机森林机器学习方法被用作构建预测模型的分类器。70% 的节点被随机分配到一个训练集，剩下的 30% 被分配到一个独立的测试集。最终模型在来自 2 个读者的数据集上进行了测试，显示出区分难治性和非难治性患者的平均准确度、敏感性和特异性分别为 73%、62% 和 82%。两个阅读器的受试者工作特征曲线下面积 (AUC) 分别为 0.83 和 0.79。我们得出结论，基于机器学习 CT 的放射组学分析能够以良好的准确性识别先验的 PTF-DLBCL。用于区分难治性和非难治性患者。两个阅读器的受试者工作特征曲线下面积 (AUC) 分别为 0.83 和 0.79。我们得出结论，基于机器学习 CT 的放射组学分析能够以良好的准确性识别先验的 PTF-DLBCL。用于区分难治性和非难治性患者。两个阅读器的受试者工作特征曲线下面积 (AUC) 分别为 0.83 和 0.79。我们得出结论，基于机器学习 CT 的放射组学分析能够以良好的准确性识别先验的 PTF-DLBCL。