Abstract：
The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.
摘要：
新诊断的弥漫性大B细胞淋巴瘤（GAINED）研究的GA结果证明了18F-FDGPET驱动的方法的成功，可以早期识别弥漫性大B细胞淋巴瘤患者的强化治疗复发风险高。此外,一些研究报告了基线PET影像组学特征(RF)的预后价值.这项工作研究了此类生物标志物对参与GAINED方案的患者生存的附加值。方法：从基线的18F-FDGPET计算常规PET特征和RF，并使用不同的体积定义（患者水平，最大病变,和最热的病变）。模型中还考虑了临床特征和巩固治疗信息。两个机器学习管道对80%的患者进行了训练，并对剩余的20%进行了测试。重复训练100次以突出测试集的可变性。对于2-y无进展生存期(PFS)结果，该管道包括数据增强和弹性净逻辑回归模型。使用平均曲线下面积（AUC）比较不同特征组的结果。为了生存的结果，该管道包括一个Cox单变量模型来选择特征。然后，该模型包括使用基于惩罚Cox多变量方法系数的回归评分中位数对高危和低危患者进行划分.将100个循环的对数秩检验P值与Wilcoxon符号排序检验进行比较。结果：总的来说，纳入545例患者进行2-yPFS分类，纳入561例患者进行生存分析。仅临床特征，单独的整合功能，传统的PET特性，在患者水平提取的RFs的AUC为，分别,0.65±0.07、0.64±0.06、0.60±0.07和0.62±0.07（最大病变为0.62±0.07，最热病变为0.54±0.07）。将临床特征与巩固特征相结合导致最佳AUC(0.72±0.06)。添加常规PET特征或RF没有改善结果。为了生存,涉及临床和巩固特征的模型的log-rankP值均显著小于所有合并特征组(P<0.007).结论：结果表明，多模态特征与简单的机器学习模型的结合似乎并不能改善根据GAINED协议治疗的患者的2-yPFS分类和PFS预测的结果。
