Comparison of the radiomics-based predictive models using machine learning and nomogram for epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma

Autor: Yusuke Kawazoe, Takehiro Shiinoki, Koya Fujimoto, Yuki Yuasa, Tsunahiko Hirano, Kazuto Matsunaga, Hidekazu Tanaka
Rok vydání: 2023
Předmět:
Zdroj: Physical and Engineering Sciences in Medicine. 46:395-403
ISSN: 2662-4737
2662-4729
DOI: 10.1007/s13246-023-01232-9
Popis: Introduction: The purpose of this study is to develop the predictive models for epidermal growth factor receptor (EGFR) mutation status and subtypes [exon 21-point mutation (L858R) and exon 19 deletion mutation (19Del)] and validate the clinical effectiveness.Methods: Total 172 patients with lung adenocarcinoma were enrolled. The analysis of variance and the least absolute shrinkage were used feature selection from plain computed tomography images. Then, radiomic score (rad-score) was calculated for the training and test cohorts. Two machine learning (ML) models with 5-fold were applied to construct the predictive models with rad-score and clinical features and evaluated by the area under the curve (AUC) of receiver operating characteristic curve. The nomogram was developed using rad-score and clinical features, then evaluated by the C-index. Finally, decision curve analysis (DCA) was performed using the best ML and nomogram models.Results: The AUC of the best ML models and the C-index of the nomogram models were 0.82, 0.73 and 0.84, 0.78 in the EGFR mutation groups, 0.83, 0.83 and 0.83, 0.80 in the L858R mutation groups, and 0.84, 0.77 and 0.85, 0.69 in the 19Del groups, respectively in the training and test cohorts. DCA showed that nomogram models have more benefits than ML models.Conclusion: We constructed two predictive models for EGFR mutation status and subtypes. The nomogram models had more clinical usefulness than ML models.
Databáze: OpenAIRE
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