Multi-Objective-Based Radiomic Feature Selection for Lesion Malignancy Classification

作者：Zhiguo Zhou, Shulong Li, Genggeng Qin, Michael Folkert, Steve Jiang, Jing Wang · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2019 · DOI：10.1109/jbhi.2019.2902298 · 被引用次数：36 · 研究领域：Radiomics and Machine Learning in Medical Imaging、AI in cancer detection、MRI in cancer diagnosis

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 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 effectiv...