Objective: Accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. Methods: This work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy based termination criterion (METC) that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. Results: We evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. Conclusion: The experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. Significance: The proposed method is general and more effective radiomic feature selection strategy.
翻译:目标:精确地分类筛选扫描中发现的损害的恶性,对于减少假正数至关重要。放射学极有可能通过提取和分析大量定量图像特征,将恶性肿瘤与良性肿瘤区别开来。由于并非所有放射特征都有助于有效分类模型,因此选择一个最佳的子集至关重要。方法:这项工作提出了一个新的基于多目标的特征选择算法(MO-FS),该算法同时考虑敏感性和特殊性,作为特征选择期间的客观功能。对MO-FS来说,我们开发了一个基于昆虫的终止标准(METC),自动停止算法,而不是依赖数代数预设的代数。我们还设计了多种客观学习的解决方案选择方法,该方法使用证据推理法(SMOOLER)自动从Pareto-Opatimal集中选择最佳的解决方案。此外,我们开发了一个适应性突变操作法,以自动产生MO-FS的突变概率。结果:我们评估了用于低剂量CT和乳腺癌恶性肿瘤的终止标准,而不是依赖数代对代之间细胞的预设数。我们还设计了一种多目标选择方法。通过常规的实验性方法展示了其他选择方法,结果:通过常规选择了其他选择方法所选制结果。