Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.

Objectives: To investigate the value of multi-model based on preoperative CT scans in predicting EGFR/TP53 co-mutation status.

Methods: We retrospectively included 2171 patients with non-small cell lung cancer (NSCLC) with pre-treatment computed tomography (CT) scans and predicting epidermal growth factor receptor (EGFR) gene sequencing from West China Hospital between January 2013 and April 2024. The deep-learning model was built for predicting EGFR / tumor protein 53 (TP53) co-occurrence status. The model performance was evaluated by area under the curve (AUC) and Kaplan-Meier analysis. We further compared multi-dimension model with three one-dimension models separately, and we explored the value of combining clinical factors with machine-learning factors. Additionally, we investigated 546 patients with 56-panel next-generation sequencing and low-dose computed tomography (LDCT) to explore the biological mechanisms of radiomics.

Results: In our cohort of 2171 patients (1,153 males, 1,018 females; median age 60 years), single-dimensional models were developed using data from 1,055 eligible patients. The multi-dimensional model utilizing a Random Forest classifier achieved superior performance, yielding the highest AUC of 0.843 for predicting EGFR/TP53 co-mutations in the test set.

Conclusion: The multi-dimensional model demonstrates promising potential for non-invasive prediction of EGFR and TP53 co-mutations, facilitating early and informed clinical decision-making in NSCLC patients at risk of treatment resistance.