Goshi Oda

1.1k total citations
63 papers, 796 citations indexed

About

Goshi Oda is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research and Surgery. According to data from OpenAlex, Goshi Oda has authored 63 papers receiving a total of 796 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Radiology, Nuclear Medicine and Imaging, 18 papers in Cancer Research and 15 papers in Surgery. Recurrent topics in Goshi Oda's work include Breast Cancer Treatment Studies (16 papers), Radiomics and Machine Learning in Medical Imaging (14 papers) and Breast Lesions and Carcinomas (13 papers). Goshi Oda is often cited by papers focused on Breast Cancer Treatment Studies (16 papers), Radiomics and Machine Learning in Medical Imaging (14 papers) and Breast Lesions and Carcinomas (13 papers). Goshi Oda collaborates with scholars based in Japan, United States and Sweden. Goshi Oda's co-authors include Tsuyoshi Nakagawa, Tomoyuki Fujioka, M. Mori, Kazunori Kubota, Ukihide Tateishi, Leona Katsuta, Yuka Kikuchi, Yoshio Kitazume, Toshiyuki Ishiba and Emi Yamaga and has published in prestigious journals such as SHILAP Revista de lepidopterología, Cancer Research and International Journal of Molecular Sciences.

In The Last Decade

Goshi Oda

59 papers receiving 786 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Goshi Oda Japan 15 504 376 105 95 91 63 796
Anurag Vaidya United States 8 407 0.8× 480 1.3× 61 0.6× 104 1.1× 107 1.2× 10 865
Benoît Schmauch France 8 448 0.9× 370 1.0× 35 0.3× 126 1.3× 134 1.5× 14 763
Arash Mohtashamian United States 4 312 0.6× 386 1.0× 58 0.6× 63 0.7× 57 0.6× 11 567
Xiangxue Wang United States 13 444 0.9× 339 0.9× 58 0.6× 241 2.5× 117 1.3× 31 759
Si‐Wa Chan Taiwan 15 530 1.1× 369 1.0× 78 0.7× 96 1.0× 98 1.1× 44 879
Isabel Schobert Germany 9 410 0.8× 167 0.4× 98 0.9× 127 1.3× 89 1.0× 18 694
Xuxin Chen United States 11 400 0.8× 307 0.8× 146 1.4× 57 0.6× 29 0.3× 40 858
Hans Pinckaers Netherlands 12 477 0.9× 608 1.6× 61 0.6× 150 1.6× 102 1.1× 20 899
Sepp de Raedt Denmark 12 407 0.8× 321 0.9× 84 0.8× 241 2.5× 110 1.2× 27 899
Qiuchang Sun China 11 649 1.3× 177 0.5× 115 1.1× 93 1.0× 70 0.8× 15 755

Countries citing papers authored by Goshi Oda

Since Specialization
Citations

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

Fields of papers citing papers by Goshi Oda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Goshi Oda

This figure shows the co-authorship network connecting the top 25 collaborators of Goshi Oda. A scholar is included among the top collaborators of Goshi Oda 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 Goshi Oda. Goshi Oda 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.
Katsuta, Leona, Tomoyuki Fujioka, Kazunori Kubota, et al.. (2024). Comparison of state-of-the-art biopsy systems for ultrasound-guided breast biopsy using a chicken breast phantom. Journal of Medical Ultrasonics. 51(4). 627–633.
2.
UCHIDA, Y., Ryota Kurimoto, Tomoki Chiba, et al.. (2024). RNA binding protein ZCCHC24 promotes tumorigenicity in triple-negative breast cancer. EMBO Reports. 25(12). 5352–5382. 1 indexed citations
3.
Oda, Goshi, Masatake Hara, Yuichi Kumaki, et al.. (2024). A Case of Autoimmune Neutropenia in a Patient Undergoing Breast Cancer Surgery. SHILAP Revista de lepidopterología. 2024(1). 5354241–5354241.
4.
Hayashi, Kumiko, Goshi Oda, Tsuyoshi Nakagawa, Tomoyuki Fujioka, & Iichiroh Onishi. (2024). A Case of Squamous Cell Carcinoma of the Breast Occurring in Benign Phyllodes Tumor. Cureus. 16(11). e73943–e73943. 1 indexed citations
5.
Mori, M., Tomoyuki Fujioka, Leona Katsuta, et al.. (2023). Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer. Diagnostics. 13(4). 794–794. 2 indexed citations
6.
7.
Kumaki, Yuichi, Goshi Oda, & Sadakatsu Ikeda. (2023). Targeting MET Amplification: Opportunities and Obstacles in Therapeutic Approaches. Cancers. 15(18). 4552–4552. 9 indexed citations
8.
Nakagawa, Tsuyoshi, Kumiko Hayashi, Ayumi Ogawa, et al.. (2022). Bone Marrow Carcinomatosis in a Stage IV Breast Cancer Patient Treated by Letrozole as First-Line Endocrine Therapy. SHILAP Revista de lepidopterología. 15(1). 436–441. 6 indexed citations
9.
Fujioka, Tomoyuki, Emi Yamaga, Atsushi Hayashi, et al.. (2022). Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography. Japanese Journal of Radiology. 40(8). 814–822. 29 indexed citations
10.
Kobayashi, Maki, Kazuishi Kubota, Goshi Oda, et al.. (2022). Quantitative high-throughput analysis of tumor infiltrating lymphocytes in breast cancer. Frontiers in Oncology. 12. 901591–901591. 8 indexed citations
11.
Ogawa, Ayumi, Tsuyoshi Nakagawa, Goshi Oda, et al.. (2021). Study of the protocol used to evaluate skin-flap perfusion in mastectomy based on the characteristics of indocyanine green. Photodiagnosis and Photodynamic Therapy. 35. 102401–102401. 6 indexed citations
12.
Oda, Goshi, Minato Yokoyama, Kumiko Hayashi, et al.. (2021). Hydronephrosis Caused by Metastatic Breast Cancer. Case Reports in Oncology. 14(1). 378–385. 3 indexed citations
13.
Mori, M., Tomoyuki Fujioka, Leona Katsuta, et al.. (2021). Clinical usefulness of the fast protocol of breast diffusion-weighted imaging using 3T magnetic resonance imaging with a 16-channel breast coil. Clinical Imaging. 78. 217–222. 1 indexed citations
14.
Ishiba, Toshiyuki, Tomoyuki Aruga, Yuichi Kumaki, et al.. (2021). Short- and long-term outcomes of immediate breast reconstruction surgery after neoadjuvant chemotherapy. Surgery Today. 52(1). 129–136. 4 indexed citations
15.
Fujioka, Tomoyuki, M. Mori, Kazunori Kubota, et al.. (2020). The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review. Diagnostics. 10(12). 1055–1055. 63 indexed citations
16.
Kikuchi, Yuka, M. Mori, Tomoyuki Fujioka, et al.. (2020). Feasibility of ultrafast dynamic magnetic resonance imaging for the diagnosis of axillary lymph node metastasis: A case report. European Journal of Radiology Open. 7. 100261–100261. 5 indexed citations
17.
Mori, M., Tomoyuki Fujioka, Leona Katsuta, et al.. (2020). Feasibility of new fat suppression for breast MRI using pix2pix. Japanese Journal of Radiology. 38(11). 1075–1081. 25 indexed citations
18.
Fujioka, Tomoyuki, Kazunori Kubota, M. Mori, et al.. (2019). Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. Japanese Journal of Radiology. 37(6). 466–472. 136 indexed citations
19.
Ishiba, Toshiyuki, Kathleen D. Danenberg, Takayuki Nakagawa, et al.. (2017). Frequencies and expression levels of programmed death ligand 1 (PD-L1) in circulating tumor RNA (ctRNA) in various cancer types. Annals of Oncology. 28. x12–x13. 5 indexed citations
20.
Oda, Goshi, Takanobu Sato, Toshiaki Ishikawa, et al.. (2012). Significance of stromal decorin expression during the progression of breast cancer. Oncology Reports. 28(6). 2003–2008. 41 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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