@article{oai:repo.qst.go.jp:00084708,
 author = {Liu, Jing and Cabral, Horacio and Song, Bin and Ichio, Aoki and Chen, Zhouyun and Nishiyama, Nobuhiro and Huang, Yuan and Kataoka, Kazunori and Mi, Peng and Ichio, Aoki},
 issue = {8},
 journal = {ACS Nano},
 month = {Aug},
 note = {Accurate diagnosis of tumors and predicting the therapeutic responses are highly demanded in the clinic to improve the treatment efficacy and survival rates. Since hypoxia develops in the progression of tumors and inversely correlates with prognosis and promotes resistance to radiotherapies and immunotherapies, it is a potential marker for therapeutic prediction. Therefore, effective discrimination of tumor hypoxia for predicting therapeutic outcomes is critical. Here, a magnetic resonance imaging (MRI)-based diagnosis strategy using contrast-amplifying nanoprobes that sense tumor acidosis and real-time observation of hypoxic conditions in tumors has been developed, aiming at accurate detection of pancreatic tumors and prediction of therapeutic effects. Our approach selectively probed xenograft, allograft, and transgenic spontaneous models of intractable pancreatic cancer, which lacks standardized predictive markers to identify patients who benefit most from treatments, and effectively discriminated the intratumoral hypoxia levels. By stratification of pancreatic tumors based on quantitative MR imaging of hypoxia, it enabled prediction of the responses to radiotherapy and immune checkpoint inhibitors. Moreover, the nanoprobe-based MRI could monitor hypoxia reduction by tumor normalization treatments, which permits visualizing pancreatic tumors that will respond to immune checkpoint blockade therapy, enhancing the response rate. The results demonstrate the potential of our strategy for accurate tumor diagnosis, patient stratification, and effective therapy.},
 pages = {13526--13538},
 title = {Nanoprobe-Based Magnetic Resonance Imaging of Hypoxia Predicts Responses to Radiotherapy, Immunotherapy, and Sensitizing Treatments in Pancreatic Tumors},
 volume = {15},
 year = {2021}
}