Translational lung cancer research · 2025
Development of a multimodal fully automated ensemble model to predict EGFR mutation and efficacy of EGFR-TKI in non-small cell lung cancer.
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Xu Shiting, Miyawaki Taichi, Shukuya Takehito, Suzuki Kazuhiro, Shimamura Shoko Sonobe, Matsuda Hironari, Kanemaru Ryota, Asao Tetsuhiko, Mimori Tomoyasu, Otsuka Yujiro, Takahashi Kazuhisa
Аннотация
BACKGROUND: Drugs for systemic therapy to advanced or recurrent non-small cell lung cancer (NSCLC) are determined by the type of driver oncogenes and programmed death-ligand 1 (PD-L1) immunostaining. In this study, we investigated the potential for artificial intelligence to assist in detecting epidermal growth factor receptor (EGFR) mutation, which is one of the most frequent and important driver oncogenes, and predicting the efficacy of EGFR-tyrosine kinase inhibitors (TKIs) in patients with NSCLC.
METHODS: We built an ensemble model that combined machine learning models (logistic regression, Bernoulli naive Bayes and Gaussian naive Bayes) for clinical information and tumor characteristics analysis, and a deep convolutional neural network for analyzing computed tomography (CT) images. The convolutional neural network was trained from scratch on our imaging dataset. Clinical and imaging features were analyzed separately within their respective models and subsequently integrated in the ensemble framework. The models were trained to predict EGFR mutation and the response of EGFR-TKI using cross-entropy loss. The performance of the proposed model was evaluated by 5-fold cross-validation.
RESULTS: One hundred and fifty consecutive evaluable patients were included in this study. Among them, 61 patients had EGFR mutation, including 59 patients with common EGFR mutation and 2 patients with uncommon EGFR mutation. The ensemble model predicted EGFR mutation with an area under the curve (AUC) of 0.88 [95% confidence interval (CI): 0.79-0.97]. Each sub-model integrated into the ensemble model predicted EGFR mutation with AUC of 0.83 (95% CI: 0.71-0.95), 0.88 (95% CI: 0.79-0.97), 0.87 (95% CI: 0.79-0.94), and 0.84 (95% CI: 0.72-0.96) for logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes and deep neural network respectively. The predictive accuracy of the ensemble model was 0.56. Specifically, the ensemble model reported that the predictive accuracy of the Del19 EGFR mutation was higher than that of the L858R EGFR mutation, with overall response rate predictive accuracy (AUC) of 0.67 versus 0.52, and disease control rate AUC of 0.88 versus 0.53.
CONCLUSIONS: The ensemble model demonstrated strong performance in predicting EGFR mutations and showed higher predictive treatment response for EGFR exon 19 deletions than for L858R mutations. These findings suggest that ensemble learning may enhance the non-invasive prediction of EGFR mutation status and EGFR genotype-specific therapeutic outcomes in NSCLC, although further validation in larger cohorts is warranted.
METHODS: We built an ensemble model that combined machine learning models (logistic regression, Bernoulli naive Bayes and Gaussian naive Bayes) for clinical information and tumor characteristics analysis, and a deep convolutional neural network for analyzing computed tomography (CT) images. The convolutional neural network was trained from scratch on our imaging dataset. Clinical and imaging features were analyzed separately within their respective models and subsequently integrated in the ensemble framework. The models were trained to predict EGFR mutation and the response of EGFR-TKI using cross-entropy loss. The performance of the proposed model was evaluated by 5-fold cross-validation.
RESULTS: One hundred and fifty consecutive evaluable patients were included in this study. Among them, 61 patients had EGFR mutation, including 59 patients with common EGFR mutation and 2 patients with uncommon EGFR mutation. The ensemble model predicted EGFR mutation with an area under the curve (AUC) of 0.88 [95% confidence interval (CI): 0.79-0.97]. Each sub-model integrated into the ensemble model predicted EGFR mutation with AUC of 0.83 (95% CI: 0.71-0.95), 0.88 (95% CI: 0.79-0.97), 0.87 (95% CI: 0.79-0.94), and 0.84 (95% CI: 0.72-0.96) for logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes and deep neural network respectively. The predictive accuracy of the ensemble model was 0.56. Specifically, the ensemble model reported that the predictive accuracy of the Del19 EGFR mutation was higher than that of the L858R EGFR mutation, with overall response rate predictive accuracy (AUC) of 0.67 versus 0.52, and disease control rate AUC of 0.88 versus 0.53.
CONCLUSIONS: The ensemble model demonstrated strong performance in predicting EGFR mutations and showed higher predictive treatment response for EGFR exon 19 deletions than for L858R mutations. These findings suggest that ensemble learning may enhance the non-invasive prediction of EGFR mutation status and EGFR genotype-specific therapeutic outcomes in NSCLC, although further validation in larger cohorts is warranted.