Shingo Matsudaira

505 total citations
17 papers, 241 citations indexed

About

Shingo Matsudaira is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Shingo Matsudaira has authored 17 papers receiving a total of 241 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Oncology, 7 papers in Pulmonary and Respiratory Medicine and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Shingo Matsudaira's work include Colorectal Cancer Surgical Treatments (9 papers), Colorectal Cancer Treatments and Studies (6 papers) and Gastric Cancer Management and Outcomes (6 papers). Shingo Matsudaira is often cited by papers focused on Colorectal Cancer Surgical Treatments (9 papers), Colorectal Cancer Treatments and Studies (6 papers) and Gastric Cancer Management and Outcomes (6 papers). Shingo Matsudaira collaborates with scholars based in Japan, Singapore and United Kingdom. Shingo Matsudaira's co-authors include Kunihiko Wakamura, Masashi Misawa, Yuta Kouyama, Takemasa Hayashi, Hideyuki Miyachi, Yuichi Mori, Katsuro Ichimasa, Fumio Ishida, Toyoki Kudo and Shin‐ei Kudo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Annals of Oncology and Gastrointestinal Endoscopy.

In The Last Decade

Shingo Matsudaira

11 papers receiving 232 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shingo Matsudaira Japan 6 188 141 86 59 23 17 241
Tomoyuki Ishigaki Japan 6 125 0.7× 120 0.9× 80 0.9× 76 1.3× 22 1.0× 15 244
Mahsa Taghiakbari Canada 9 193 1.0× 165 1.2× 44 0.5× 42 0.7× 6 0.3× 39 254
Athanasia Mitsala Greece 4 106 0.6× 46 0.3× 56 0.7× 79 1.3× 28 1.2× 11 223
Haoda Chen China 9 156 0.8× 77 0.5× 104 1.2× 66 1.1× 17 0.7× 35 227
Noboru Yatagai Japan 10 117 0.6× 156 1.1× 164 1.9× 43 0.7× 9 0.4× 28 309
Joel Troya Germany 11 207 1.1× 101 0.7× 42 0.5× 112 1.9× 23 1.0× 18 263
Shunsuke Kamba Japan 7 138 0.7× 119 0.8× 79 0.9× 73 1.2× 9 0.4× 27 222
Yusuke Yagawa Japan 3 89 0.5× 78 0.6× 47 0.5× 49 0.8× 21 0.9× 6 153
Margaret Vance United Kingdom 9 467 2.5× 380 2.7× 173 2.0× 68 1.2× 6 0.3× 14 517
Yusaku Shimamoto Japan 9 48 0.3× 135 1.0× 114 1.3× 35 0.6× 8 0.3× 20 185

Countries citing papers authored by Shingo Matsudaira

Since Specialization
Citations

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

Fields of papers citing papers by Shingo Matsudaira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shingo Matsudaira

This figure shows the co-authorship network connecting the top 25 collaborators of Shingo Matsudaira. A scholar is included among the top collaborators of Shingo Matsudaira 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 Shingo Matsudaira. Shingo Matsudaira is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Kouyama, Yuta, Shin‐ei Kudo, Katsuro Ichimasa, et al.. (2023). Endoscopic resection alone as a potential treatment method for low-risk deep invasive T1 colorectal cancer. PubMed. 2(4). 503–509.e1.
2.
Kudo, Shin‐ei, Katsuro Ichimasa, Yuta Kouyama, et al.. (2023). Differentiation grade as a risk factor for lymph node metastasis in T1 colorectal cancer. SHILAP Revista de lepidopterología. 4(1). e324–e324. 2 indexed citations
3.
Kudo, Shin‐ei, Masashi Misawa, Yuichi Mori, et al.. (2021). ID: 3521790 DOES ARTIFICIAL INTELLIGENCE IMPROVE NEOPLASMS DETECTION RATE FOR COLONOSCOPY? - A SINGLE CENTER PILOT STUDY. Gastrointestinal Endoscopy. 93(6). AB191–AB192.
4.
Misawa, Masashi, Shin‐ei Kudo, Yuichi Mori, et al.. (2021). ID: 3526637 ARTIFICIAL INTELLIGENCE-ASSISTED DIAGNOSTIC SYSTEM FOR NARROW-BAND IMAGING FOR COLORECTAL LESIONS.. Gastrointestinal Endoscopy. 93(6). AB198–AB199.
5.
Mochizuki, Kenichi, Shin‐ei Kudo, Katsuro Ichimasa, et al.. (2020). Left-sided location is a risk factor for lymph node metastasis of T1 colorectal cancer: a single-center retrospective study. International Journal of Colorectal Disease. 35(10). 1911–1919. 26 indexed citations
6.
Ichimasa, Katsuro, Shin‐ei Kudo, Yuichi Mori, et al.. (2019). 475 ARTIFICIAL INTELLIGENCE WILL HELP IN DETERMINING THE NEED FOR ADDITIONAL SURGERY AFTER ENDOSCOPIC RESECTION OF T1 COLORECTAL CANCER –ANALYSIS BASED ON A BIG DATA FOR MACHINE LEARNING. Gastrointestinal Endoscopy. 89(6). AB85–AB86. 2 indexed citations
7.
Kouyama, Yuta, Shin‐ei Kudo, Hideyuki Miyachi, et al.. (2018). Risk factors of recurrence in T1 colorectal cancers treated by endoscopic resection alone or surgical resection with lymph node dissection. International Journal of Colorectal Disease. 33(8). 1029–1038. 23 indexed citations
8.
Ichimasa, Katsuro, Shin‐ei Kudo, Yuichi Mori, et al.. (2017). Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy. 50(3). 230–240. 111 indexed citations
9.
Ichimasa, Katsuro, Shin‐ei Kudo, Hideyuki Miyachi, et al.. (2017). Patient gender as a factor associated with lymph node metastasis in T1 colorectal cancer: A systematic review and meta-analysis. Molecular and Clinical Oncology. 6(4). 517–524. 18 indexed citations
10.
Ichimasa, Katsuro, Shin‐ei Kudo, Yuichi Mori, et al.. (2017). Su1643 Artificial Intelligence Can Accurately Predict the Presence of Lymph Node Metastasis in Pt1 Colorectal Cancers. Gastrointestinal Endoscopy. 85(5). AB377–AB377. 1 indexed citations
11.
Ichimasa, Katsuro, Shin‐ei Kudo, Hideyuki Miyachi, et al.. (2016). Comparative clinicopathological characteristics of colon and rectal T1 carcinoma. Oncology Letters. 13(2). 805–810. 11 indexed citations
12.
Ichimasa, Katsuro, Shin‐ei Kudo, Hideyuki Miyachi, et al.. (2016). Su1611 Does Gender Predict Lymph Node Metastasis in pT1 Colorectal Cancer? A Systematic Review and Meta-Analysis. Gastrointestinal Endoscopy. 83(5). AB363–AB364.
13.
Mori, Yuichi, Shin‐ei Kudo, Kunihiko Wakamura, et al.. (2015). Prolonged Bradycardia Induced by Bevacizumab-Associated Infusion Reaction in Advanced Metastatic Colon Cancer. Journal of Medical Cases. 6(5). 194–197. 1 indexed citations
14.
Kouyama, Yuta, Shin‐ei Kudo, Hideyuki Miyachi, et al.. (2015). Practical problems of measuring depth of submucosal invasion in T1 colorectal carcinomas. International Journal of Colorectal Disease. 31(1). 137–146. 45 indexed citations
15.
Mori, Yuichi, Shin‐ei Kudo, Kunihiko Wakamura, et al.. (2015). Prolonged Bradycardia Induced by Bevacizumab-Associated Infusion Reaction in Advanced Metastatic Colon Cancer. Journal of Medical Cases. 6(5). 194–197. 1 indexed citations
16.
Matsudaira, Shingo, et al.. (2014). Early Onset of Ventilatory and Airway Response to Hypercapnia is Mediated by Medullary 5-HT<sub>1A</sub> Receptors in Infant Rats. The Showa University Journal of Medical Sciences. 26(3). 211–217.
17.
Mori, Yuichi, Shin‐ei Kudo, Kunihiko Wakamura, et al.. (2013). Case of Prolonged Bradycardia Induced by Bevacizumab-Associated Infusion Reaction in Advanced Metastatic Colon Cancer. Annals of Oncology. 24. ix82–ix82.

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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