A CT-based deep learning approach to differentiate multiple primary lung cancers, metastases, and benign nodules

The MambaOut Kobe model demonstrates outstanding potential for clinical application in classifying MPLC, IPM, and MBPL. Its combination of high accuracy and computational efficiency makes it a promising tool for lung cancer diagnosis and treatment planning. This automated approach could reduce diagnostic uncertainty, minimize unnecessary invasive procedures, and facilitate timely, personalized treatment decisions for patients with multiple lung lesions. Future studies should focus on validating the model on larger, multicenter datasets and enhancing its discriminatory capacity between MPLC and IPM.