IEEE J Biomed Health Inform. 2020 Jan;24(1):194-204. doi: 10.1109/JBHI.2019.2902298. Epub 2019 Feb 28.
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.
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 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.
we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis.
the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods.
the proposed method is general and more effective radiomic feature selection strategy.
准确分类筛查扫描中检测到的病变的恶性程度对于减少假阳性至关重要。通过提取和分析大量定量图像特征，放射组学具有从良性肿瘤中区分恶性肿瘤的巨大潜力。由于并非所有放射组学特征都有助于构建有效的分类模型，因此选择最优的特征子集至关重要。
本研究提出了一种新的基于多目标的特征选择（MO-FS）算法，该算法在特征选择过程中同时将敏感性和特异性作为目标函数。对于 MO-FS，我们开发了一种改进的基于熵的终止准则，该准则可以自动停止算法，而无需依赖预设的几代。我们还为多目标学习设计了一种解决方案选择方法，使用证据推理方法（SMOLER）自动从帕累托最优集中选择最佳解决方案。此外，我们开发了一种自适应突变操作，可自动生成 MO-FS 中的突变概率。
我们评估了 MO-FS 在低剂量 CT 中肺结节恶性程度分类和数字乳腺断层合成中乳腺病变恶性程度分类中的性能。
实验结果表明，MO-FS 选择的特征集比其他常用方法选择的特征集具有更好的分类性能。
该方法具有通用性，是一种更有效的放射组学特征选择策略。
