Japanese Journal of Radiological Technology

Winner of Segi Award (Best Paper in 2024)

Volume 79 (2023) Issue 10 Pages 1136-1143

Radioproteomics for Discriminating the Activity and Inactivity of Immune Checkpoint Molecules in Breast Cancer

Fuyu Harada, Toru Fukuda, Yoshikazu Uchiyama

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.

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2025 Volume 81 Issue 12

Temporal Changes in Volume CT Dose Index in Radiation Dose Management for X-ray CT Scanners Yuki Yamaguchi, Masaaki Fukunaga, Yuse Shono, Misaki Fujita, Maho Kato, Kota Inoki
Development of the CT Scan Attitude Scale and Evaluation of Its Reliability and Validity Tomonari Tomitaka, Kohsuke Yamamoto, Rikuta Ishigaki, Kentaro Inomata, Takashi Bo, Natsumi Kuwabara
Evaluation of the Infant Skull via Low-dose CT Images Using an Sn Filter and the Iterative Reconstruction Method Yasuhiro Kawahara, Ken Yamamoto, Kyo Noguchi, Mariko Doai, Ichiro Toyota, Hidenori Tannai, Kosuke Matsubara, Taketoshi Yoshida
Development of an AI-based Positioning Technical Assistance System for Mammography Aika Kawasaki, Kenichi Inoue, Takako Doi, Norimitsu Shinohara

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