M. Fang

888 total citations · 2 hit papers
7 papers, 620 citations indexed

About

M. Fang is a scholar working on Pulmonary and Respiratory Medicine, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, M. Fang has authored 7 papers receiving a total of 620 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Pulmonary and Respiratory Medicine, 3 papers in Oncology and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in M. Fang's work include Gastric Cancer Management and Outcomes (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Gastrointestinal Tumor Research and Treatment (2 papers). M. Fang is often cited by papers focused on Gastric Cancer Management and Outcomes (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Gastrointestinal Tumor Research and Treatment (2 papers). M. Fang collaborates with scholars based in China, Italy and United Kingdom. M. Fang's co-authors include Di Dong, Jie Tian, Xiuhong Shan, Jia Fu, Jianbo Gao, Jiafu Ji, Xiaping Chen, Z Y Li, Zhenkun Li and J. Li and has published in prestigious journals such as Cancer Research, Annals of Oncology and Medicine.

In The Last Decade

M. Fang

6 papers receiving 612 citations

Hit Papers

Development and validation of an individualized nomogram ... 2019 2026 2021 2023 2019 2020 100 200 300
What are hit papers?

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.

  1. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer
    2019 305 citations Di Dong, Lei Tang et al. Annals of Oncology profile →
  2. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study
    2020 284 citations Di Dong, M. Fang et al. Annals of Oncology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Fang China 4 408 363 158 130 98 7 620
Chuanli Chen China 13 469 1.1× 389 1.1× 233 1.5× 157 1.2× 119 1.2× 20 718
Zepang Sun China 14 349 0.9× 368 1.0× 218 1.4× 158 1.2× 100 1.0× 23 632
Zhe Jin China 14 283 0.7× 161 0.4× 185 1.2× 88 0.7× 21 0.2× 37 525
Z Y Li China 3 196 0.5× 194 0.5× 80 0.5× 77 0.6× 61 0.6× 9 333
Zhenpeng Peng China 15 393 1.0× 166 0.5× 120 0.8× 162 1.2× 19 0.2× 48 689
Weicai Huang China 9 233 0.6× 180 0.5× 114 0.7× 67 0.5× 39 0.4× 14 397
Ko Woon Park South Korea 14 411 1.0× 186 0.5× 100 0.6× 220 1.7× 26 0.3× 33 725
Haitao Zhu China 13 755 1.9× 170 0.5× 520 3.3× 157 1.2× 28 0.3× 53 1.0k

Countries citing papers authored by M. Fang

Since Specialization
Citations

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

Fields of papers citing papers by M. Fang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Fang

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

All Works

7 of 7 papers shown
1.
Fang, M., et al.. (2023). Prediction model of liver metastasis risk in patients with gastric cancer: A population-based study. Medicine. 102(39). e34702–e34702. 1 indexed citations
2.
Li, Jiangfeng, et al.. (2020). [The value of spectral CT-based radiomics in preoperative prediction of lymph node metastasis of advanced gastric cancer].. PubMed. 100(21). 1617–1622. 6 indexed citations
3.
Dong, Di, M. Fang, Lingyun Tang, et al.. (2020). Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Annals of Oncology. 31(7). 912–920. 284 indexed citations breakdown →
4.
Dong, Di, Lei Tang, Z Y Li, et al.. (2019). Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Annals of Oncology. 30(3). 431–438. 305 indexed citations breakdown →
5.
Xu, Cunshuan, et al.. (2018). P3.CR-12 A Novel Oncogenic Driver in a Lung Adenocarcinoma Patient Harboring an EGFR-KDD and Response to Afatinib. Journal of Thoracic Oncology. 13(10). S1032–S1033. 1 indexed citations
6.
7.
Ju, Yu‐Ming, et al.. (2007). Theissenia rogersii sp. nov. and phylogenetic position of Theissenia. Mycologia. 99(4). 612–621. 23 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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