Stacking Approach for Lung Cancer EGFR Mutation Status Prediction from CT Scans

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Ventura, A (Author) Other; Pereira, T (Author) Other; Silva, F (Author) Other; Freitas, C (Author) Other; Cunha, A (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Houston, TX, USA, December 9-12, 2021

Pages: 3099-3105

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00V-ZMN

DOI: 10.1109/bibm52615.2021.9669429

Abstract (EN): Due to the huge mortality rate of lung cancer, there is a strong need for developing solutions that help with the early diagnosis and the definition of the most appropriate treatment. In the particular case of target therapy, effective genotyping of the tumor is fundamental since this treatment uses targeted drugs that can induce death in cancer cells. The biopsy is the traditional method to assess the genotype information but it is extremely invasive and painful. Medical imaging is a valuable alternative to biopsies, considering the potential to extract imaging features correlated with specific genomic alterations. Regarding the limitations of single model approaches for gene mutation status predictions, ensemble strategies might bring valuable benefits by combining the strengths and weaknesses of the aggregated methods. This preliminary work aims to provide further advances in the radiogenomics field by studying the use of ensemble methods to predict the Epidermal Growth Factor Receptor (EGFR) mutation status in lung cancer. The best result obtained for the proposed ensemble approach was an AUC of 0.706 (± 0.122). However, the ensemble did not outperform the single models with AUC values of 0.712 (± 0.119) for Logistic Regression, 0.711 (± 0.119) for Support Vector Machine and 0.712 (± 0.120) for Elastic Net. The high correlation found on the decisions of each single model might be a plausible explanation for this behavior, which caused the ensemble to misclassify the same examples as the single models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6