Similarity-Based Fuzzy Fusion for Predicting Gene Mutation in Non-Small Cell Lung Cancer

The precise mutation prediction of the Epidermal Growth Factor Receptor (EGFR) holds paramount importance in clinical practice. Nevertheless, the persisting challenge lies in accurately conducting genomic profiling of lung cancer using a single biopsy sample, given the inherent tumor heterogeneity. To address this issue, an innovative approach using similarity-based multimodal data fuzzy fusion was presented to predict EGFR mutation. Initially, radiomics features were extracted from computerized tomography scans to quantitatively characterize tumors within the region of interest. Subsequently, three independent fundamental learners were trained based on preprocessed multimodal medical data. Once these fundamental learners generate membership degrees, fuzzy sets for EGFR genotyping were established. The Tanimoto coefficient was then employed to evaluate the similarity between the membership degrees of observed cases and ideal solutions. Ultimately, de-fuzzification through similarity ranking yielded a robust prediction for the EGFR mutation. The proposed multimodal medical data fuzzy fusion demonstrates promising predictive performance, achieving an area under curve value of 0.8878 in an independent test cohort. The proposed work has the potential to serve as a robust and intelligent decision-making system for clinicians.