Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT.

Purpose: To investigate the performance of deep learning and radiomics features of intra-tumoral region (ITR) and peri-tumoral region (PTR) in the diagnosing of breast cancer lung metastasis (BCLM) and primary lung cancer (PLC) with low-dose CT (LDCT).

Methods: We retrospectively collected the LDCT images of 100 breast cancer patients with lung lesions, comprising 60 cases of BCLM and 40 cases of PLC. We proposed a fusion model that combined deep learning features extracted from ResNet18-based multi-input residual convolution network with traditional radiomics features. Specifically, the fusion model adopted a multi-region strategy, incorporating the aforementioned features from both the ITR and PTR. Then, we randomly divided the dataset into training and validation sets using fivefold cross-validation approach. Comprehensive comparative experiments were performed between the proposed fusion model and other eight models, including the intra-tumoral deep learning model, the intra-tumoral radiomics model, the intra-tumoral deep-learning radiomics model, the peri-tumoral deep learning model, the peri-tumoral radiomics model, the peri-tumoral deep-learning radiomics model, the multi-region radiomics model, and the multi-region deep-learning model.

Results: The fusion model developed using deep-learning radiomics feature sets extracted from the ITR and PTR had the best classification performance, with the area under the curve of 0.913 (95% CI 0.840-0.960). This was significantly higher than that of the single region's radiomics model or deep learning model.

Conclusions: The combination of radiomics and deep learning features was effective in discriminating BCLM and PLC. Additionally, the analysis of the PTR can mine more comprehensive tumor information.