PDF(Pubmed)
Abstract：
Programmed death-ligand 1 (PD-L1) expressions play a crucial role in guiding therapeutic interventions such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional determination of PD-L1 status includes careful surgical or biopsied tumor specimens. These specimens are gathered through invasive procedures, representing a risk of difficulties and potential challenges in getting reliable and representative tissue samples. Using a single center cohort of 189 patients, our objective was to evaluate various fusion methods that used non-invasive computed tomography (CT) and 18 F-FDG positron emission tomography (PET) images as inputs to various deep learning models to automatically predict PD-L1 in non-small cell lung cancer (NSCLC). We compared three different architectures (ResNet, DenseNet, and EfficientNet) and considered different input data (CT only, PET only, PET/CT early fusion, PET/CT late fusion without as well as with partially and fully shared weights to determine the best model performance. Models were assessed utilizing areas under the receiver operating characteristic curves (AUCs) considering their 95% confidence intervals (CI). The fusion of PET and CT images as input yielded better performance for PD-L1 classification. The different data fusion schemes systematically outperformed their individual counterparts when used as input of the various deep models. Furthermore, early fusion consistently outperformed late fusion, probably as a result of its capacity to capture more complicated patterns by merging PET and CT derived content at a lower level. When we looked more closely at the effects of weight sharing in late fusion architectures, we discovered that while it might boost model stability, it did not always result in better results. This suggests that although weight sharing could be beneficial when modality parameters are similar, the anatomical and metabolic information provided by CT and PET scans are too dissimilar to consistently lead to improved PD-L1 status predictions.
摘要：
程序性死亡配体1（PD-L1）表达在指导治疗性干预措施中起着至关重要的作用，例如在肺癌中使用酪氨酸激酶抑制剂（TKIs）和免疫检查点抑制剂（ICIs）。PD-L1状态的常规测定包括仔细的手术或活检肿瘤标本。这些标本是通过侵入性程序收集的，在获得可靠和有代表性的组织样本方面存在困难和潜在挑战的风险。使用189名患者的单中心队列，我们的目标是评估各种融合方法,这些方法使用非侵入性计算机断层扫描(CT)和18F-FDG正电子发射断层扫描(PET)图像作为各种深度学习模型的输入,以自动预测非小细胞肺癌(NSCLC)中的PD-L1.我们比较了三种不同的架构(ResNet、DenseNet,和EfficientNet)，并考虑不同的输入数据(仅限CT，仅PET,PET/CT早期融合,PET/CT后期融合，无需部分和完全共享权重，以确定最佳模型性能。考虑其95%置信区间(CI),利用接受者工作特征曲线(AUC)下的面积评估模型。PET和CT图像作为输入的融合对于PD-L1分类产生了更好的性能。当用作各种深度模型的输入时，不同的数据融合方案系统地优于其各自的对应物。此外，早期融合始终优于晚期融合，可能是由于其通过在较低水平合并PET和CT衍生内容来捕获更复杂模式的能力。当我们更仔细地研究后期融合架构中重量分担的影响时，我们发现，虽然它可以提高模型的稳定性，它并不总是带来更好的结果。这表明，尽管当模态参数相似时，权重共享可能是有益的，CT和PET扫描所提供的解剖和代谢信息相差太大,无法持续改善PD-L1状态预测.
