Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Design of a novel oral fluoropyrimidine carbamate, capecitabine, which generates 5-fluorouracil selectively in tumours by enzymes concentrated in human liver and cancer tissue
19981.0k citationsMasanori Miwa, Masako Ura et al.European Journal of Cancerprofile →
Citations per year, relative to M Nishida M Nishida (= 1×)
peers
T. Griffin
Countries citing papers authored by M Nishida
Since
Specialization
Citations
This map shows the geographic impact of M Nishida'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 M Nishida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M Nishida more than expected).
This network shows the impact of papers produced by M Nishida. 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 M Nishida. The network helps show where M Nishida may publish in the future.
Co-authorship network of co-authors of M Nishida
This figure shows the co-authorship network connecting the top 25 collaborators of M Nishida.
A scholar is included among the top collaborators of M Nishida 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 M Nishida. M Nishida is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yasuda, Makoto, Eizo Kimura, Kazunori Ochiai, et al.. (2001). [Dose finding study of paclitaxel and carboplatin for ovarian cancer (JKTB)].. PubMed. 28(4). 493–8.3 indexed citations
5.
Maeda, Yoshitaka, M Nishida, Toshihiro Takao, et al.. (1999). [A case of multiple liver metastases from breast cancer successfully treated with intra-arterial administration of docetaxel].. PubMed. 26(12). 1951–4.5 indexed citations
6.
Miwa, Masanori, Masako Ura, M Nishida, et al.. (1998). Design of a novel oral fluoropyrimidine carbamate, capecitabine, which generates 5-fluorouracil selectively in tumours by enzymes concentrated in human liver and cancer tissue. European Journal of Cancer. 34(8). 1274–1281.1039 indexed citations breakdown →
7.
Sawada, Noriaki, T Ishikawa, Yu Fukase, et al.. (1998). Induction of thymidine phosphorylase activity and enhancement of capecitabine efficacy by taxol/taxotere in human cancer xenografts.. PubMed. 4(4). 1013–9.372 indexed citations
8.
Takao, Tetsuya, M Nishida, Y. Maeda, Kenji Takao, & Oka M. (1997). [The study of continuous infusion chemotherapy with low-dose cisplatin and 5-fluorouracil for patients with primary liver cancer].. PubMed. 24(12). 1724–7.2 indexed citations
9.
Tanaka, Yumiko, et al.. (1994). Ovarian dysgerminoma: MR and CT appearance.. PubMed. 18(3). 443–8.41 indexed citations
10.
Tsunoda, Hajime, M Nishida, Yoshito Arisawa, et al.. (1994). [Treatment of stage Ia ovarian cancer].. PubMed. 46(10). 1027–32.1 indexed citations
Suzuki, Motofumi, et al.. (1989). [Level of inhibition of thymidylate synthase activity and 5-fluorouracil in tumor tissues after administration of UFT or tegafur].. PubMed. 24(7). 1399–404.1 indexed citations
Hanai, Aya, et al.. (1977). Cancer registries in Japan: activities and incidence data.. PubMed. 47. 7–15.
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.