Nagase, Hiroki

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer

Iwatate, Y., Hoshino, I., Yokota, H., Ishige, F., Itami, M., Mori, Y., Chiba, S., Arimitsu, H., Yanagibashi, H., Nagase, H., et al. (2020) · British Journal of Cancer

Radiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology Research

Cited by 101

Preclinical Study of Novel Gene Silencer Pyrrole-Imidazole Polyamide Targeting Human TGF-β1 Promoter for Hypertrophic Scars in a Common Marmoset Primate Model

Igarashi, J., Fukuda, N., Inoue, T., Nakai, S., Saito, K., Fujiwara, K., Matsuda, H., Ueno, T., Matsumoto, Y., Watanabe, T., et al. (2015) · PLOS ONE

Dermatologic Treatments and ResearchRNA Interference and Gene Delivery

Cited by 39

Radiogenomics predicts the expression of microRNA-1246 in the serum of esophageal cancer patients

Hoshino, I., Yokota, H., Ishige, F., Iwatate, Y., Takeshita, N., Nagase, H., Uno, T., & Matsubara, H. (2020) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingMicroRNA in disease regulation

Cited by 19

Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic cancer

Iwatate, Y., Yokota, H., Hoshino, I., Ishige, F., Kuwayama, N., Itami, M., Mori, Y., Chiba, S., Arimitsu, H., Yanagibashi, H., et al. (2022) · International Journal of Oncology

Radiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology Research

Cited by 11