The RADLER framework introduced in this study aims at the integration of deep and radiomics features for medical image analysis and classification. Its first application in a prognostic task of locoregional recurrence (LR task) of head and neck cancer improves with respect to the state of art [38], both in terms of sensitivity (0.94 vs 0.56) and specificity (0.95 vs 0.67). Moreover, the DAP included in the framework is used to evaluate variability due to resampling and control for selection bias in the model selection phase. As assessed by the DAP , the feature set integrating radiomics and deep features is more effective in predicting LR than only one of the feature types. 解释

这段话大概是在说明一个研究的框架RADLER旨在将深度学习和放射学特征集成到医学图像分析和分类中。其首个应用于头颈癌局部复发（LR任务）的预后任务中，在灵敏度（0.94 vs 0.56）和特异度（0.95 vs 0.67）方面都比现有技术有所提高。此外，框架中包含的DAP用于评估由于重采样引起的变异性，并控制模型选择阶段中的选择偏差。通过DAP评估，将放射学和深度特征集成到特征集中比仅使用其中一种特征类型更有效地预测LR。