Investigation of small lung lesion detection for lung cancer screening in low dose FDG PET imaging by deep neural networks.

Purpose: FDG PET imaging is often recommended for the diagnosis of pulmonary nodules after indeterminate low dose CT lung cancer screening. Lowering FDG injecting is desirable for PET imaging. In this work, we aimed to investigate the performance of a deep learning framework in the automatic diagnoses of pulmonary nodules at different count levels of PET imaging. Materials and methods: Twenty patients with 18F-FDG-avid pulmonary nodules were included and divided into independent training (60%), validation (20%), and test (20%) subsets. We trained a convolutional neural network (ResNet-50) on original DICOM images and used ImageNet pre-trained weight to fine-tune the model. Simulated low-dose PET images at the 9 count levels (20 × 10 Results: The AUC values were >0.98 for all count levels except for 0.5 and 0.25 million true counts (0.975 (CL 95%, 0.953-0.992) and 0.963 (CL 95%, 0.941-0.982), respectively). The AUC values were 0.941(CL 95%, 0.923-0.956), 0.993(CL 95%, 0.990-0.996) and 0.998(CL 95%, 0.996-0.999) for different groups of lesion size with effective diameter (R) <10 mm, 10-20 mm, and >20 mm, respectively. The count limit for achieving high AUC (≥0.96) for lesions with size Conclusion: All of the above results suggest that the proposed deep learning based method may detect small lesions <10 mm at an effective radiation dose <0.1 mSv. Advances in knowledge: We investigated the advantages and limitations of a fully automated lung cancer detection method based on deep learning models for data with different lesion sizes and different count levels, and gave guidance for clinical application.