Ayaka Katayama

809 total citations
32 papers, 378 citations indexed

About

Ayaka Katayama is a scholar working on Cancer Research, Oncology and Artificial Intelligence. According to data from OpenAlex, Ayaka Katayama has authored 32 papers receiving a total of 378 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Cancer Research, 17 papers in Oncology and 7 papers in Artificial Intelligence. Recurrent topics in Ayaka Katayama's work include Breast Cancer Treatment Studies (11 papers), AI in cancer detection (7 papers) and Cancer Cells and Metastasis (6 papers). Ayaka Katayama is often cited by papers focused on Breast Cancer Treatment Studies (11 papers), AI in cancer detection (7 papers) and Cancer Cells and Metastasis (6 papers). Ayaka Katayama collaborates with scholars based in Japan, United Kingdom and Egypt. Ayaka Katayama's co-authors include Tetsunari Oyama, Emad A. Rakha, Jun Horiguchi, Michael S. Toss, Masahiko Nishiyama, Tadashi Handa, Takehiko Yokobori, Ken Shirabe, Kyoichi Kaira and Takaaki Fujii and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Scientific Reports.

In The Last Decade

Ayaka Katayama

30 papers receiving 375 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ayaka Katayama Japan 12 164 154 150 65 50 32 378
Tianjie Pu China 13 174 1.1× 199 1.3× 203 1.4× 44 0.7× 96 1.9× 29 450
Rebecca Millican‐Slater United Kingdom 14 168 1.0× 204 1.3× 220 1.5× 110 1.7× 51 1.0× 29 548
Maciej P. Zerkowski United States 8 303 1.8× 184 1.2× 377 2.5× 93 1.4× 74 1.5× 13 635
Mansour Alsaleem United Kingdom 15 233 1.4× 191 1.2× 258 1.7× 70 1.1× 93 1.9× 32 526
Paul Jank Germany 11 137 0.8× 85 0.6× 203 1.4× 25 0.4× 56 1.1× 37 425
Javier I. J. Orozco United States 13 202 1.2× 221 1.4× 309 2.1× 19 0.3× 105 2.1× 41 557
Yu‐Chen Pei China 9 158 1.0× 228 1.5× 300 2.0× 35 0.5× 100 2.0× 14 502
Çiğdem Selli Türkiye 11 171 1.0× 125 0.8× 205 1.4× 26 0.4× 61 1.2× 30 498
Eldo T. Verghese United Kingdom 15 198 1.2× 258 1.7× 322 2.1× 59 0.9× 112 2.2× 29 610

Countries citing papers authored by Ayaka Katayama

Since Specialization
Citations

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

Fields of papers citing papers by Ayaka Katayama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ayaka Katayama

This figure shows the co-authorship network connecting the top 25 collaborators of Ayaka Katayama. A scholar is included among the top collaborators of Ayaka Katayama 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 Ayaka Katayama. Ayaka Katayama 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.
Katayama, Ayaka, et al.. (2024). Nuclear morphological characterisation of lobular carcinoma variants: a morphometric study. Histopathology. 86(5). 813–823. 3 indexed citations
2.
Katayama, Ayaka, et al.. (2024). Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers. International Journal of Clinical Oncology. 29(11). 1648–1668. 7 indexed citations
3.
Kurozumi, Sasagu, Ayaka Katayama, Yukio Koibuchi, et al.. (2024). Utility of human epidermal growth factor 2 heterogeneity as a prognostic factor in triple-negative breast cancer. Medical Molecular Morphology. 57(3). 177–184.
4.
Kurozumi, Sasagu, Naohiko Seki, Chikako Honda, et al.. (2023). Identification of MicroRNAs Associated with Histological Grade in Early-Stage Invasive Breast Cancer. International Journal of Molecular Sciences. 25(1). 35–35. 3 indexed citations
5.
Nakazawa, Nobuhiro, Makoto Sohda, Ayaka Katayama, et al.. (2023). Infiltration of Gastric Cancer Stroma by Tumor-Infiltrating Lymphocytes Correlates with Mechanistic Target of Rapamycin Signaling. Oncology. 101(8). 520–526. 1 indexed citations
6.
Ibrahim, Asmaa, Mostafa Jahanifar, Noorul Wahab, et al.. (2023). Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application. Modern Pathology. 37(3). 100416–100416. 11 indexed citations
7.
8.
Katayama, Ayaka, Jane Starczynski, Michael S. Toss, et al.. (2022). The frequency and clinical significance of centromere enumeration probe 17 alterations in human epidermal growth factor receptor 2 immunohistochemistry‐equivocal invasive breast cancer. Histopathology. 81(4). 511–519. 2 indexed citations
9.
Kurozumi, Sasagu, Kyoichi Kaira, Hiroshi Matsumoto, et al.. (2022). Association of L-type amino acid transporter 1 (LAT1) with the immune system and prognosis in invasive breast cancer. Scientific Reports. 12(1). 2742–2742. 29 indexed citations
10.
Katayama, Ayaka, et al.. (2021). Atypia in breast pathology: what pathologists need to know. Pathology. 54(1). 20–31. 15 indexed citations
11.
Katayama, Ayaka, Michael S. Toss, Takaaki Sano, et al.. (2021). Nuclear morphology in breast lesions: refining its assessment to improve diagnostic concordance. Histopathology. 80(3). 515–528. 10 indexed citations
12.
Lashen, Ayat, Michael S. Toss, Ayaka Katayama, et al.. (2021). Assessment of proliferation in breast cancer: cell cycle or mitosis? An observational study. Histopathology. 79(6). 1087–1098. 18 indexed citations
13.
Lashen, Ayat, Asmaa Ibrahim, Ayaka Katayama, et al.. (2021). Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images. Histopathology. 79(6). 913–925. 11 indexed citations
14.
15.
Ibrahim, Asmaa, Ayat Lashen, Ayaka Katayama, et al.. (2021). Defining the area of mitoses counting in invasive breast cancer using whole slide image. Modern Pathology. 35(6). 739–748. 17 indexed citations
16.
Chang, Aeson, et al.. (2021). Doxorubicin sensitizes breast cancer cells to stress signaling. Brain Behavior and Immunity. 98. 9–9. 1 indexed citations
17.
Kurozumi, Sasagu, Kyoichi Kaira, Hiroshi Matsumoto, et al.. (2019). β2-Adrenergic receptor expression is associated with biomarkers of tumor immunity and predicts poor prognosis in estrogen receptor-negative breast cancer. Breast Cancer Research and Treatment. 177(3). 603–610. 26 indexed citations
18.
Katayama, Ayaka, et al.. (2017). Expression patterns of claudins in patients with triple‐negative breast cancer are associated with nodal metastasis and worse outcome. Pathology International. 67(8). 404–413. 25 indexed citations
19.
Bai, Tuya, Takehiko Yokobori, Bolag Altan, et al.. (2017). High STMN1 level is associated with chemo-resistance and poor prognosis in gastric cancer patients. British Journal of Cancer. 116(9). 1177–1185. 49 indexed citations
20.
Horiguchi, Jun, Toru Higuchi, Ayaka Katayama, et al.. (2017). Stathmin1 expression is associated with aggressive phenotypes and cancer stem cell marker expression in breast cancer patients. International Journal of Oncology. 51(3). 781–790. 38 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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