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

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 1; pp. 194 - 204
• Main Authors: Zhou, Zhiguo, Li, Shulong, Qin, Genggeng, Folkert, Michael, Jiang, Steve, Wang, Jing
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Breast; Breast cancer; Classification; Databases, Factual; Entropy; Evidential reasoning; Feature extraction; feature selection; Humans; Image classification; Informatics; lesion malignancy classification; Lesions; Linear programming; Malignancy; multi-objective evolutionary algorithm; Multiple objective analysis; Mutation; Neoplasms - diagnostic imaging; Radiographic Image Interpretation, Computer-Assisted - methods; Radiomics; Sociology; Statistics; Tomography, X-Ray Computed; Tumors
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2902298

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 effective radiomic feature selection strategy.