Jin Qi
Impact in
- Oncology top 5%
- Cancer Immunotherapy and Biomarkers
- CAR-T cell therapy research
- Pancreatic and Hepatic Oncology Research
- Cancer Cells and Metastasis
-
- Radiomics and Machine Learning in Medical Imaging
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 9
- Medical Imaging Techniques and Applications 3
- Oncology 10
- Cancer Immunotherapy and Biomarkers 8
- CAR-T cell therapy research 2
- Co-authors
- Robert J. GilliesMatthew B. SchabathIlke TunaliJhanelle E. GrayBo MarelliHuakui YuYan LanKin-Ming Lo
- Journals
- Radiology Artificial Intelligence (1 paper)Clinical Cancer Research (1 paper)Scientific Reports (1 paper)Journal of Thoracic Oncology (1 paper)Leukemia (1 paper)
- Partner nations
- United StatesChinaTürkiye
In The Last Decade
Jin Qi
17 papers receiving 911 citations
Hit Papers
Peers
Comparison fields: 5 of 55
- Oncology 631
- Radiology, Nuclear Medicine and Imaging 351
- Immunology 297
- Pulmonary and Respiratory Medicine 240
- Cancer Research 71
Countries citing papers authored by Jin Qi
This map shows the geographic impact of Jin Qi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jin Qi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin Qi more than expected).
Fields of papers citing papers by Jin Qi
This network shows the impact of papers produced by Jin Qi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jin Qi. The network helps show where Jin Qi may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jin Qi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 2 | |
| 3 | 2023 | 2 | |
| 4 | 2023 | 0 | |
| 5 | 2021 | 31 | |
| 6 | 2021 | 12 | |
| 7 | 2020 | 37 | |
| 8 | 2020 | 29 | |
| 9 | 2019 | 64 | |
| 10 | 2019 | 132 | |
| 11 | 2019 | 108 | |
| 12 | Enhanced preclinical antitumor activity of M7824, a bifunctional fusion protein simultaneously targeting PD-L1 and TGF-β Hit paper breakdown → | 2018 | 405 |
| 13 | 2018 | 10 | |
| 14 | 2017 | 58 | |
| 15 | 2017 | 19 | |
| 16 | 2017 | 4 | |
| 17 | 2017 | 1 | |
| 18 | 2002 | 4 | |
| 19 | 2002 | 6 |
About Jin Qi
Jin Qi is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology, Biotechnology, Immunology and Gastroenterology, having authored 19 papers that have together received 924 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (9 papers), Cancer Immunotherapy and Biomarkers (8 papers), Medical Imaging Techniques and Applications (3 papers), Immunotherapy and Immune Responses (3 papers), Cancer Research and Treatments (2 papers), CAR-T cell therapy research (2 papers), Lung Cancer Diagnosis and Treatment (2 papers) and Acute Myeloid Leukemia Research (1 paper). The work is most often cited by research in Oncology (631 citations), Radiology, Nuclear Medicine and Imaging (351 citations), Immunology (297 citations), Pulmonary and Respiratory Medicine (240 citations) and Cancer Research (71 citations). Jin Qi has collaborated with scholars based in United States, China and Türkiye. Frequent co-authors include Robert J. Gillies, Matthew B. Schabath, Ilke Tunali, Jhanelle E. Gray, Bo Marelli, Huakui Yu, Yan Lan, Kin-Ming Lo, Chunxiao Xu and Guozhong Qin. Their work appears in journals such as Radiology Artificial Intelligence, Clinical Cancer Research, Scientific Reports, Journal of Thoracic Oncology and Leukemia.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.