Megumi Jinguji

878 total citations
58 papers, 671 citations indexed

About

Megumi Jinguji is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Megumi Jinguji has authored 58 papers receiving a total of 671 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Surgery and 17 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Megumi Jinguji's work include Radiomics and Machine Learning in Medical Imaging (25 papers), Medical Imaging Techniques and Applications (20 papers) and Adrenal and Paraganglionic Tumors (10 papers). Megumi Jinguji is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (25 papers), Medical Imaging Techniques and Applications (20 papers) and Adrenal and Paraganglionic Tumors (10 papers). Megumi Jinguji collaborates with scholars based in Japan, Germany and Netherlands. Megumi Jinguji's co-authors include Masatoyo Nakajo, Takashi Yoshiura, Masayuki Nakajo, Atsushi Tani, Yoshiaki Nakabeppu, Yoshihiko Fukukura, Yoriko Kajiya, Yasuto Uchikado, Ken Sasaki and Shoji Natsugoe and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Radiology.

In The Last Decade

Megumi Jinguji

57 papers receiving 668 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Megumi Jinguji Japan	14	427	212	181	121	118	58	671
G. Bonniaud France	13	297 0.7×	185 0.9×	139 0.8×	245 2.0×	91 0.8×	25	670
Francesco Dondi Italy	14	319 0.7×	86 0.4×	189 1.0×	94 0.8×	37 0.3×	80	566
Annemarie F. Shepherd United States	15	170 0.4×	170 0.8×	328 1.8×	128 1.1×	70 0.6×	57	644
Hueisch‐Jy Ding Taiwan	14	318 0.7×	190 0.9×	239 1.3×	121 1.0×	33 0.3×	37	694
H. J. Gallowitsch Austria	16	422 1.0×	200 0.9×	230 1.3×	292 2.4×	64 0.5×	37	816
Jong-Ryool Oh South Korea	12	322 0.8×	134 0.6×	226 1.2×	165 1.4×	53 0.4×	19	583
Seiji Kurata Japan	15	256 0.6×	109 0.5×	192 1.1×	89 0.7×	90 0.8×	50	586
Hitoshi Ikushima Japan	16	208 0.5×	319 1.5×	295 1.6×	31 0.3×	78 0.7×	91	921
Çiğdem Soydal Türkiye	16	305 0.7×	197 0.9×	293 1.6×	75 0.6×	46 0.4×	98	730
Lise Bettman Israel	9	303 0.7×	157 0.7×	135 0.7×	122 1.0×	18 0.2×	12	551

Countries citing papers authored by Megumi Jinguji

Since Specialization

Citations

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

Fields of papers citing papers by Megumi Jinguji

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Megumi Jinguji

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

All Works

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20 of 20 papers shown

Nakajo, Masatoyo, Megumi Jinguji, Atsushi Tani, et al.. (2024). Applying deep learning-based ensemble model to [18F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases. Japanese Journal of Radiology. 43(1). 91–100. 3 indexed citations

Nakajo, Masatoyo, Megumi Jinguji, Tetsuya Idichi, et al.. (2024). Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [18F]-FDG-PET-radiomic features. Japanese Journal of Radiology. 43(5). 864–874.

Nakajo, Masatoyo, Megumi Jinguji, Atsushi Tani, et al.. (2024). Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Japanese Journal of Radiology. 42(7). 744–752. 6 indexed citations

Nakajo, Masatoyo, et al.. (2023). Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology. Japanese Journal of Radiology. 42(1). 28–55. 19 indexed citations

Nakajo, Masatoyo, Yoshiaki Shinden, Megumi Jinguji, et al.. (2023). Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer. Molecular Imaging and Biology. 25(5). 923–934. 6 indexed citations

Jinguji, Megumi, et al.. (2022). I-131 false-positive uptake in a thymic cyst with expression of the sodium-iodide symporter: A case report. Medicine. 101(26). e29282–e29282. 4 indexed citations

Nakajo, Masatoyo, Megumi Jinguji, Hiroshi Kawabata, et al.. (2022). The Usefulness of Machine Learning–Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer. Molecular Imaging and Biology. 25(2). 303–313. 11 indexed citations

Ueno, Kentaro, et al.. (2021). Case Report: 18F-FDG PET-CT for Diagnosing Prosthetic Device-Related Infection in an Infant With CHD. Frontiers in Pediatrics. 9. 584741–584741. 5 indexed citations

Nakajo, Masatoyo, Megumi Jinguji, Atsushi Tani, et al.. (2018). A Pilot Study of Texture Analysis of Primary Tumor [18F]FDG Uptake to Predict Recurrence in Surgically Treated Patients with Non-small Cell Lung Cancer. Molecular Imaging and Biology. 21(4). 771–780. 13 indexed citations

10.

Nakajo, Masatoyo, Megumi Jinguji, Atsushi Tani, et al.. (2018). ¹⁸F-FDG-PET/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: potential usefulness of combination of the SUV-related, volumetric, and heterogeneous texture parameters. British Journal of Radiology. 92(1094). 20180620–20180620. 11 indexed citations

11.

Inaki, Anri, Kenichi Yoshimura, Toshinori Murayama, et al.. (2017). A phase I clinical trial for [<sup>131</sup>I]meta-iodobenzylguanidine therapy in patients with refractory pheochromocytoma and paraganglioma: a study protocol. The Journal of Medical Investigation. 64(3.4). 205–209. 8 indexed citations

12.

Nakajo, Masatoyo, Yoriko Kajiya, Atsushi Tani, et al.. (2016). A pilot study of the diagnostic and prognostic values of FLT-PET/CT for pancreatic cancer: comparison with FDG-PET/CT. Abdominal Radiology. 42(4). 1210–1221. 4 indexed citations

13.

Nakajo, Masatoyo, et al.. (2016). FLT-PET/CT diagnosis of primary and metastatic nodal lesions of gastric cancer: comparison with FDG-PET/CT. Abdominal Radiology. 41(10). 1891–1898. 10 indexed citations

14.

Nakajo, Masatoyo, Yoriko Kajiya, Megumi Jinguji, et al.. (2016). Current clinical status of 18F-FLT PET or PET/CT in digestive and abdominal organ oncology. Abdominal Radiology. 42(3). 951–961. 8 indexed citations

15.

Nakajo, Masatoyo, Masayuki Nakajo, Megumi Jinguji, et al.. (2015). The value of intratumoral heterogeneity of¹⁸F-FDG uptake to differentiate between primary benign and malignant musculoskeletal tumours on PET/CT. British Journal of Radiology. 88(1055). 20150552–20150552. 9 indexed citations

16.

Kinuya, Seigo, Keiichiro Yoshinaga, Tetsuya Higuchi, et al.. (2015). Draft guidelines regarding appropriate use of 131I-MIBG radiotherapy for neuroendocrine tumors. Annals of Nuclear Medicine. 29(6). 543–552. 17 indexed citations

17.

Nakajo, Masatoyo, Masayuki Nakajo, Yoriko Kajiya, et al.. (2013). Diagnostic performance of 18F-fluorothymidine PET/CT for primary colorectal cancer and its lymph node metastasis: comparison with 18F-fluorodeoxyglucose PET/CT. European Journal of Nuclear Medicine and Molecular Imaging. 40(8). 1223–1232. 12 indexed citations

18.

Nakajo, Masatoyo, Masayuki Nakajo, Megumi Jinguji, et al.. (2013). Diagnosis of Metastases from Postoperative Differentiated Thyroid Cancer: Comparison between FDG and FLT PET/CT Studies. Radiology. 267(3). 891–901. 10 indexed citations

19.

Nakajo, Masatoyo, Masayuki Nakajo, Yoriko Kajiya, et al.. (2012). High FDG and Low FLT Uptake in a Thyroid Papillary Carcinoma Incidentally Discovered by FDG PET/CT. Clinical Nuclear Medicine. 37(6). 607–608. 7 indexed citations

20.

Nakajo, Masayuki, Shinsaku Tsuchimochi, Hiroaki Tanabe, Yoshiaki Nakabeppu, & Megumi Jinguji. (2005). Three basic patterns of changes in serum thyroid hormone levels in Graves’ disease during the one-year period after radioiodine therapy. Annals of Nuclear Medicine. 19(4). 297–308. 9 indexed citations

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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