Hidetoshi Ikeda

3.4k citations
124 papers · 2.1k indexed · h-index 25

Impact in

Papers in

Hidetoshi Ikeda

121 papers receiving 2.0k citations

Peers

Hidetoshi Ikeda
Comparison fields: 5 of 92
  • Endocrinology, Diabetes and Metabolism 827
  • Rheumatology 504
  • Genetics 349
  • Neurology 447
  • Psychiatry and Mental health 279
Replace Josefina F. Llena with:
Josefina F. Llena United States
Sakamuri V. Reddy United States
B Pasquier France
Francesca Andreetta Italy
Éric Bieth France
M. L. Chu United States
Matthew J. Simmonds United Kingdom
Hernando Mena United States
Gleb N. Budzilovich United States
Irwin Feigin United States
Hidetoshi Ikeda relative to Josefina F. Llena United States Josefina F. Llena's profile →
Citations per field
00.5×3.8×
Josefina F. Llena · 1×
Citations per year

Countries citing papers authored by Hidetoshi Ikeda

Since Specialization
Citations

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

Fields of papers citing papers by Hidetoshi Ikeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Hidetoshi Ikeda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hidetoshi Ikeda Line = papers co-authored together Hidetoshi Ikeda links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20206
2 201521
3
Demonstration of improved surgical outcome in patients with both ACTH and GH secreting adenomas diagnosed by a new sensitive method of MET-PET fusion 3T-MRI
20131
4 201333
5 20123
6 20082
7 20087
8 2008122
9 20078
10 20064
11 20067
12 200513
13 200240
14 1999199
15 199743
16 199518
17 19958
18 19929
19 1990105
20 198868

About Hidetoshi Ikeda

Hidetoshi Ikeda is a scholar working on Endocrinology, Diabetes and Metabolism, Genetics, Neurology, Virology and Rheumatology, having authored 124 papers that have together received 2.1k indexed citations. Recurring topics across this work include Pituitary Gland Disorders and Treatments (65 papers), Growth Hormone and Insulin-like Growth Factors (35 papers), Glioma Diagnosis and Treatment (15 papers), Adrenal and Paraganglionic Tumors (13 papers), Virus-based gene therapy research (7 papers), Hedgehog Signaling Pathway Studies (6 papers), TGF-β signaling in diseases (6 papers) and Cerebrospinal fluid and hydrocephalus (6 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (827 citations), Rheumatology (504 citations), Genetics (349 citations), Neurology (447 citations) and Psychiatry and Mental health (279 citations). Hidetoshi Ikeda has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Takashi Yoshimoto, Tadao Arinami, Toru Sasaki, Masashi Fukui, Takeshi Odaka, Taichiro Yoshimoto, Jirô Suzuki, Teiji Tominaga, Hiroshi Niizuma and Akio Matsuzawa. Their work appears in journals such as Neurologia medico-chirurgica, European Radiology, International Journal of Cancer, Clinical Neurology and Neurosurgery and Acta Neuropathologica.

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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