Hiroto Hakamada

537 total citations
18 papers, 383 citations indexed

About

Hiroto Hakamada is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Hiroto Hakamada has authored 18 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Oncology and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Hiroto Hakamada's work include Pancreatic and Hepatic Oncology Research (9 papers), MRI in cancer diagnosis (9 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). Hiroto Hakamada is often cited by papers focused on Pancreatic and Hepatic Oncology Research (9 papers), MRI in cancer diagnosis (9 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). Hiroto Hakamada collaborates with scholars based in Japan, United States and Switzerland. Hiroto Hakamada's co-authors include Takashi Yoshiura, Yoshihiko Fukukura, Koji Takumi, Yuichi Kumagae, Masanori Nakajo, Michiyo Higashi, Masatoyo Nakajo, Kiyohisa Kamimura, Kosei Maemura and Shiho Arima and has published in prestigious journals such as Medicine, Journal of Magnetic Resonance Imaging and European Radiology.

In The Last Decade

Hiroto Hakamada

17 papers receiving 383 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hiroto Hakamada Japan 12 234 202 120 108 52 18 383
Raymond Endozo United Kingdom 13 281 1.2× 101 0.5× 39 0.3× 79 0.7× 84 1.6× 32 455
Siya Shi China 8 357 1.5× 137 0.7× 36 0.3× 184 1.7× 70 1.3× 13 515
Martin Barrio United States 9 134 0.6× 177 0.9× 176 1.5× 63 0.6× 17 0.3× 16 449
Jang Yoo South Korea 12 165 0.7× 92 0.5× 43 0.4× 67 0.6× 15 0.3× 41 332
Yuntai Cao China 9 224 1.0× 62 0.3× 95 0.8× 24 0.2× 59 1.1× 38 310
Chiara Pozzessere Italy 12 117 0.5× 132 0.7× 37 0.3× 84 0.8× 40 0.8× 31 338
Mehmet Ö. Öksüz Germany 8 95 0.4× 128 0.6× 178 1.5× 80 0.7× 11 0.2× 10 341
Fahad Marafi Kuwait 10 223 1.0× 86 0.4× 32 0.3× 55 0.5× 26 0.5× 57 367
R.B.J. de Bondt Netherlands 7 190 0.8× 133 0.7× 36 0.3× 193 1.8× 14 0.3× 9 467
Zhaoxia Yang China 9 204 0.9× 54 0.3× 67 0.6× 58 0.5× 25 0.5× 19 329

Countries citing papers authored by Hiroto Hakamada

Since Specialization
Citations

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

Fields of papers citing papers by Hiroto Hakamada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hiroto Hakamada

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

All Works

18 of 18 papers shown
1.
Takumi, Koji, Hiroto Hakamada, Hiroaki Nagano, et al.. (2025). Postoperative prognostic assessment using ECV fraction derived from equilibrium contrast-enhanced CT in thymomas. European Journal of Radiology. 184. 111978–111978.
2.
Takumi, Koji, Hiroaki Nagano, Hiroto Hakamada, et al.. (2024). Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions. Japanese Journal of Radiology. 42(9). 983–992. 3 indexed citations
3.
Ueda, Kazuhiro, et al.. (2021). Intraoperative computed tomography of a resected lung inflated with air to verify safety surgical margin. Quantitative Imaging in Medicine and Surgery. 12(2). 1281–1289. 3 indexed citations
4.
Fukukura, Yoshihiko, Yuichi Kumagae, Hiroto Hakamada, et al.. (2020). Visual enhancement pattern during the delayed phase of enhanced CT as an independent prognostic factor in stage IV pancreatic ductal adenocarcinoma. Pancreatology. 20(6). 1155–1163. 6 indexed citations
5.
Takumi, Koji, Hiroto Hakamada, Hiroaki Nagano, et al.. (2020). Usefulness of dual-layer spectral CT in follow-up examinations: diagnosing recurrent squamous cell carcinomas in the head and neck. Japanese Journal of Radiology. 39(4). 324–332. 4 indexed citations
6.
Fukukura, Yoshihiko, Yuichi Kumagae, Hiroto Hakamada, et al.. (2019). Extracellular volume fraction determined by equilibrium contrast-enhanced dual-energy CT as a prognostic factor in patients with stage IV pancreatic ductal adenocarcinoma. European Radiology. 30(3). 1679–1689. 46 indexed citations
7.
Fukukura, Yoshihiko, Yuichi Kumagae, Hiroto Hakamada, et al.. (2019). CT and MRI features of undifferentiated carcinomas with osteoclast-like giant cells of the pancreas: a case series. Abdominal Radiology. 44(4). 1246–1255. 18 indexed citations
8.
Takumi, Koji, Yoshihiko Fukukura, Hiroto Hakamada, et al.. (2019). CT features of parathyroid carcinomas: comparison with benign parathyroid lesions. Japanese Journal of Radiology. 37(5). 380–389. 11 indexed citations
9.
Fukukura, Yoshihiko, Yuichi Kumagae, Hiroto Hakamada, et al.. (2019). Estimation of Extracellular Volume Fraction With Routine Multiphasic Pancreatic Computed Tomography to Predict the Survival of Patients With Stage IV Pancreatic Ductal Adenocarcinoma. Pancreas. 48(10). 1360–1366. 13 indexed citations
10.
11.
12.
Nakajo, Masanori, Yoshihiko Fukukura, Hiroto Hakamada, et al.. (2018). Whole‐tumor apparent diffusion coefficient (ADC) histogram analysis to differentiate benign peripheral neurogenic tumors from soft tissue sarcomas. Journal of Magnetic Resonance Imaging. 48(3). 680–686. 14 indexed citations
13.
Fukukura, Yoshihiko, Yuichi Kumagae, Hiroto Hakamada, et al.. (2017). Computed diffusion-weighted MR imaging for visualization of pancreatic adenocarcinoma: Comparison with acquired diffusion-weighted imaging. European Journal of Radiology. 95. 39–45. 16 indexed citations
14.
Takumi, Koji, et al.. (2017). Value of diffusion tensor imaging in differentiating malignant from benign parotid gland tumors. European Journal of Radiology. 95. 249–256. 27 indexed citations
15.
Fukukura, Yoshihiko, Koji Takumi, Hiroto Hakamada, et al.. (2016). Histogram Analysis of Apparent Diffusion Coefficient in Differentiating Pancreatic Adenocarcinoma and Neuroendocrine Tumor. Medicine. 95(4). e2574–e2574. 41 indexed citations
16.
Fukukura, Yoshihiko, et al.. (2016). Diffusion-weighted MR imaging of the pancreas: optimizing b-value for visualization of pancreatic adenocarcinoma. European Radiology. 26(10). 3419–3427. 22 indexed citations
17.
Fukukura, Yoshihiko, Yuichi Kumagae, Masatoyo Nakajo, et al.. (2016). ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma. Journal of Magnetic Resonance Imaging. 45(4). 1195–1203. 43 indexed citations
18.
Takumi, Koji, et al.. (2015). Pancreatic neuroendocrine tumors: Correlation between the contrast-enhanced computed tomography features and the pathological tumor grade. European Journal of Radiology. 84(8). 1436–1443. 74 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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