Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced dataset.

Autor: Wu WF; Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, 600, Taiwan., Lai KM; Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, 600, Taiwan.; Central Taiwan University of Science and Technology Institute of Radiological Science, Taichung, 406, Taiwan., Chen CH; Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, 600, Taiwan.; Central Taiwan University of Science and Technology Institute of Radiological Science, Taichung, 406, Taiwan., Wang BC; Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan., Chen YJ; Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan., Shen CW; Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan., Chen KY; Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan., Lin EC; Department of Chemistry and Biochemistry, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, 62102, Taiwan. cheel@ccu.edu.tw.; Center for Nano Bio-Detection, National Chung Cheng University, Chiayi, 621, Taiwan. cheel@ccu.edu.tw., Chen CC; Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, No. 539, Zhongxiao Rd., East Dist., Chiayi City, 60002, Taiwan. hlmarkc@gmail.com.; Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, 402, Taiwan. hlmarkc@gmail.com.; Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, 701, Taiwan. hlmarkc@gmail.com.; Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan, 717, Taiwan. hlmarkc@gmail.com.
Jazyk: angličtina
Zdroj: Discover oncology [Discov Oncol] 2024 Sep 14; Vol. 15 (1), pp. 447. Date of Electronic Publication: 2024 Sep 14.
DOI: 10.1007/s12672-024-01333-1
Abstrakt: Background: Early detection of T790M mutation in exon 20 of epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) patients with brain metastasis is crucial for optimizing treatment strategies. In this study, we developed radiomics models to distinguish NSCLC patients with T790M-positive mutations from those with T790M-negative mutations using multisequence MR images of brain metastasis despite an imbalanced dataset. Various resampling techniques and classifiers were employed to identify the most effective strategy.
Methods: Radiomic analyses were conducted on a dataset comprising 125 patients, consisting of 18 with EGFR T790M-positive mutations and 107 with T790M-negative mutations. Seventeen first- and second-order statistical features were selected from CET1WI, T2WI, T2FLAIR, and DWI images. Four classifiers (logistic regression, support vector machine, random forest [RF], and extreme gradient boosting [XGBoost]) were evaluated under 13 different resampling conditions.
Results: The area under the curve (AUC) value achieved was 0.89, using the SVM-SMOTE oversampling method in combination with the XGBoost classifier. This performance was measured against the AUC reported in the literature, serving as an upper-bound reference. Additionally, comparable results were observed with other oversampling methods paired with RF or XGBoost classifiers.
Conclusions: Our study demonstrates that, even when dealing with an imbalanced EGFR T790M dataset, reasonable predictive outcomes can be achieved by employing an appropriate combination of resampling techniques and classifiers. This approach has significant potential for enhancing T790M mutation detection in NSCLC patients with brain metastasis.
(© 2024. The Author(s).)
Databáze: MEDLINE
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