Yuki Shinno

1.9k total citations
72 papers, 671 citations indexed

About

Yuki Shinno is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Molecular Biology. According to data from OpenAlex, Yuki Shinno has authored 72 papers receiving a total of 671 indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Oncology, 46 papers in Pulmonary and Respiratory Medicine and 10 papers in Molecular Biology. Recurrent topics in Yuki Shinno's work include Lung Cancer Treatments and Mutations (38 papers), Cancer Immunotherapy and Biomarkers (27 papers) and Lung Cancer Research Studies (16 papers). Yuki Shinno is often cited by papers focused on Lung Cancer Treatments and Mutations (38 papers), Cancer Immunotherapy and Biomarkers (27 papers) and Lung Cancer Research Studies (16 papers). Yuki Shinno collaborates with scholars based in Japan, United States and United Kingdom. Yuki Shinno's co-authors include Yuichiro Ohe, Yasushi Goto, Hidehito Horinouchi, Noboru Yamamoto, Yuji Matsumoto, Tatsuya Yoshida, Yusuke Okuma, Ken Masuda, Noriko Motoi and Shigehiro Yagishita and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Yuki Shinno

65 papers receiving 665 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yuki Shinno Japan 14 379 322 145 132 87 72 671
Yu Jeong Bang South Korea 11 468 1.2× 380 1.2× 180 1.2× 309 2.3× 57 0.7× 46 857
Takashi Ichimura Japan 12 361 1.0× 274 0.9× 96 0.7× 143 1.1× 108 1.2× 52 579
So Nakamura Japan 15 240 0.6× 169 0.5× 120 0.8× 329 2.5× 106 1.2× 58 604
Eisaku Ito Japan 15 157 0.4× 151 0.5× 85 0.6× 171 1.3× 43 0.5× 33 520
Hongjing Jiang China 14 262 0.7× 334 1.0× 98 0.7× 418 3.2× 64 0.7× 61 635
Eric R. Sargent United States 11 144 0.4× 206 0.6× 195 1.3× 236 1.8× 82 0.9× 12 543
Rajkumar Vajpeyi Canada 12 236 0.6× 125 0.4× 71 0.5× 246 1.9× 68 0.8× 31 466
Jatin Gandhi United States 11 121 0.3× 148 0.5× 90 0.6× 107 0.8× 43 0.5× 55 352
Ioannis Efstratiou Greece 12 188 0.5× 108 0.3× 165 1.1× 199 1.5× 71 0.8× 44 605
Kouichiro Tsugawa Japan 10 196 0.5× 173 0.5× 94 0.6× 214 1.6× 59 0.7× 19 476

Countries citing papers authored by Yuki Shinno

Since Specialization
Citations

This map shows the geographic impact of Yuki Shinno'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 Yuki Shinno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuki Shinno more than expected).

Fields of papers citing papers by Yuki Shinno

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuki Shinno. 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 Yuki Shinno. The network helps show where Yuki Shinno may publish in the future.

Co-authorship network of co-authors of Yuki Shinno

This figure shows the co-authorship network connecting the top 25 collaborators of Yuki Shinno. A scholar is included among the top collaborators of Yuki Shinno based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yuki Shinno. Yuki Shinno is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Horinouchi, Hidehito, Ken Takasawa, Ken Masuda, et al.. (2025). A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records. BMC Medical Informatics and Decision Making. 25(1). 85–85. 1 indexed citations
2.
Satoh, Hironori, Yusuke Okuma, Yuki Shinno, et al.. (2025). Evolving treatments and prognosis in Stage IV non-small cell lung cancer: 20 years of progress of novel therapies. Lung Cancer. 202. 108453–108453.
3.
Tanaka, Tomohiro, Yasushi Goto, Ken Masuda, et al.. (2024). The remarkable antitumor efficacy of corticosteroid treatment in patients with refractory thymomas. Respiratory Investigation. 62(5). 766–772. 1 indexed citations
4.
Yamamoto, Gaku, Kosuke Tanaka, Ryo Kamata, et al.. (2024). WEE1 confers resistance to KRASG12C inhibitors in non-small cell lung cancer. Cancer Letters. 611. 217414–217414. 4 indexed citations
5.
Horinouchi, Hidehito, Yuki Shinno, Yusuke Okuma, et al.. (2024). Impact of Durvalumab on the Duration and Complexity of Corticosteroid Therapy for Pneumonitis After Chemoradiotherapy. Clinical Lung Cancer. 25(7). e369–e378.e3. 2 indexed citations
6.
Matsumoto, Yuji, Jumpei Kashima, Tatsuya Imabayashi, et al.. (2023). Concordance between cryobiopsy and forceps biopsy specimens in assessment of immunohistochemistry staining for non-small cell lung carcinoma. Translational Lung Cancer Research. 12(6). 1245–1255. 4 indexed citations
7.
Tanaka, Tomohiro, Yasushi Goto, Masafumi Horie, et al.. (2023). Whole Exome Sequencing of Thymoma Patients Exhibiting Exceptional Responses to Pemetrexed Monotherapy. Cancers. 15(16). 4018–4018. 2 indexed citations
8.
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Yoshida, Tatsuya, Shigehiro Yagishita, Yuji Matsumoto, et al.. (2023). Clinical Characteristics and Pharmacokinetics Change of Long-Term Responders to Antiprogrammed Cell Death Protein 1 Inhibitor Among Patients With Advanced NSCLC. JTO Clinical and Research Reports. 4(4). 100474–100474. 3 indexed citations
10.
Fujiwara, Yutaka, Tetsunari Hase, Naozumi Hashimoto, et al.. (2023). Pharmacokinetic and dose‐finding study of osimertinib in patients with impaired renal function and low body weight. Cancer Science. 114(5). 2087–2097. 7 indexed citations
11.
Shinno, Yuki & Yuichiro Ohe. (2023). Thoracic SMARCA4-deficient undifferentiated tumor: current knowledge and future perspectives. Japanese Journal of Clinical Oncology. 54(3). 265–270. 8 indexed citations
12.
Tanaka, Tomohiro, Tatsuya Yoshida, Ken Masuda, et al.. (2022). Prognostic role of modified Glasgow Prognostic score in elderly non-small cell lung cancer patients treated with anti-PD-1 antibodies. Respiratory Investigation. 61(1). 74–81. 13 indexed citations
13.
Shinno, Yuki, Akihiko Yoshida, Ken Masuda, et al.. (2022). Efficacy of Immune Checkpoint Inhibitors in SMARCA4-Deficient Thoracic Tumor. Clinical Lung Cancer. 23(5). 386–392. 33 indexed citations
14.
Horinouchi, Hidehito, Tatsuya Yoshida, Ken Masuda, et al.. (2021). Correlation between body mass index and efficacy of anti-PD-1 inhibitor in patients with non-small cell lung cancer. Respiratory Investigation. 60(2). 234–240. 10 indexed citations
15.
Horinouchi, Hidehito, Ken Masuda, Yuki Shinno, et al.. (2021). Comparison of time to failure of pembrolizumab plus chemotherapy versus pembrolizumab monotherapy: a consecutive analysis of patients having NSCLC with high PD-L1 expression. Cancer Immunology Immunotherapy. 71(3). 737–746. 6 indexed citations
16.
Shiraishi, Kouya, Yuki Shinno, Yoshihisa Kobayashi, et al.. (2021). An Alert to Possible False Positives With a Commercial Assay for MET Exon 14 Skipping. Journal of Thoracic Oncology. 16(12). 2133–2138. 12 indexed citations
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19.
Masuda, Ken, Hidehito Horinouchi, Midori Tanaka, et al.. (2020). Efficacy of anti-PD-1 antibodies in NSCLC patients with an EGFR mutation and high PD-L1 expression. Journal of Cancer Research and Clinical Oncology. 147(1). 245–251. 53 indexed citations
20.

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.

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