Prediction of EGFR Mutations in Lung Adenocarcinoma via CT Images: A Comparative Study of Intratumoral and Peritumoral Radiomics, Deep Learning, and Fusion Models.

RATIONALE AND OBJECTIVES

This study aims to analyze the intratumoral and peritumoral characteristics of lung adenocarcinoma patients on the basis of chest CT images via radiomic and deep learning methods and to develop and validate a multimodel fusion strategy for predicting epidermal growth factor receptor (EGFR) mutation statuses.

MATERIALS AND METHODS

Retrospective data from 826 lung adenocarcinoma patients across two hospitals were collected. Data from center1 were used for model training and internal validation, while data from center2 were reserved for external validation. Tumor segmentation was performed using the nnUNet network, and volumes of interest (VOIs) for the tumor and its peritumoral regions (2 mm, 4 mm, 6 mm, 8 mm, 10 mm) were subsequently derived.Radiomics features were extracted from various VOIs using PyRadiomics, and radiomics models were developed using Lasso and multiple machine learning algorithms.Using 2D, 2.5D, and 3D images derived from different VOIs as inputs, multiple deep learning models were trained and their performances compared.The radiomics and deep learning models demonstrating the best predictive performance were selected and integrated with clinical models for model fusion.Multi-model fusion of clinical, radiomics, and deep learning features was achieved using feature-level fusion and various decision-level fusion strategies, including hard voting, soft voting, and stacking ensemble.The predictive performances of various fusion models were evaluated and compared systematically.

RESULTS

Among the available radiomic models, the model based on intratumoral and peritumoral 2-mm regions (VOI_Comb2) achieved the best performance on the internal and external validation sets (AUC=0.843 and 0.803, respectively). Compared with 2D and 2.5D deep learning models, the 3D deep learning model demonstrated superior predictive performance. The 3D deep model based on the VOI_Comb2 region achieved the highest AUC among all the deep learning models on the internal and external validation sets (AUC=0.839 and 0.814, respectively). Among the fusion models, the soft voting strategy achieved the highest AUC on the internal and external validation sets, reaching 0.925 and 0.889, respectively. On the external validation set, the AUC of the soft voting model was significantly greater than that of the hard voting model, early fusion model, or any single modality model.

CONCLUSION

This study demonstrates that combining radiomic and deep learning models based on intratumoral and peritumoral regions is an effective method for capturing comprehensive imaging features in lung adenocarcinoma. The multimodal fusion approach using soft voting leverages the strengths of each modality and provides a robust framework for advanced image feature extraction to support personalized treatment.