Radiogenomics-based prediction of and gene mutation in non-small cell lung cancer patients.

OBJECTIVES

Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the and genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility. This study aims to develop machine learning models based on radiomic features to predict and gene mutation status in NSCLC patients, thereby providing a reference for precision oncology.

METHODS

Imaging and mutation data from eligible NSCLC patients were obtained from the publicly available Lung-PET-CT-Dx dataset in The Cancer Imaging Archive (TCIA). A three-dimensional-convolutional neural network (3D-CNN) was used to extract imaging features from the regions of interest (ROI). The LightGBM algorithm was employed to build classification models for predicting and gene mutation status. Model performance was evaluated using 5-fold cross-validation, with receiver operator characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity used for validation.

RESULTS

The models effectively predicted and mutations in NSCLC patients, achieving an AUC of 0.95 for mutations and 0.90 for . The models also demonstrated high accuracy ( 89.66%; 87.10%), sensitivity ( 93.33%; 87.50%), and specificity ( 85.71%; 86.67%).

CONCLUSIONS

A radiogenomics-machine learning predictive model can serve as a non-invasive tool for anticipating and gene mutation status in NSCLC patients.