Purpose: Radioproteomics studies investigating the relationship between lesion phenotype and proteins have been progressed. The purpose of this study was to develop a radioproteomics method for discriminating between active and inactive immune checkpoint molecules based on lesion phenotype. Methods: From the public database TCGA-BRCA, mRNA and fat suppression contrast-enhanced T₁-weighted images of 49 patients with breast cancer were selected for the experiment. Using mRNA, we defined cases with active (10 cases) and inactive (39 cases) immune checkpoint molecules. To discriminate these cases using lesion phenotype, 275 radiomics features were measured from the tumor area. After selecting 3 radiomics features by using Lasso, logistic regression was employed to discriminate between active and inactive cases of immune checkpoint molecules. Results: Evaluation of ROC analysis showed that the AUC was 0.81. Conclusion: Patients whose immune cell function is being braked by immune checkpoint molecules are likely to respond to immune checkpoint inhibitors when their activity is inhibited. Therefore, our results may be applied to predict the effects of immune checkpoint inhibitors in breast cancer treatment.

Purpose: In this study, we propose a system that combines a depth camera with a deep learning model for estimating the human skeleton and a depth camera to estimate the shooting part to be radiographed and to acquire the thickness of the subject, thereby providing optimized X-ray imaging conditions. Methods: We propose a system that provides optimized X-ray imaging conditions by estimating the shooting part and measuring the thickness of the subject using an RGB camera and a depth camera. The system uses OpenPose, a posture estimation library, to estimate the shooting part. Results: The recognition rate of the shooting part was 15.38% for the depth camera and 84.62% for the RGB camera at a distance of 100 cm, and 42.31% for the depth camera and 100% for the RGB camera at a distance of 120 cm. The measurement accuracy of the subject thickness was within ±10 mm except for a few cases, indicating that the X-ray imaging conditions were optimized for the subject thickness. Conclusion: The implementation of this system in an X-ray system is expected to enable automatic setting of X-ray imaging conditions. The system is also useful in preventing increased exposure dose due to excessive dose or decreased image quality due to insufficient dose caused by incorrect setting of X-ray imaging conditions.