Xiaochun Meng

2.3k total citations · 1 hit paper
63 papers, 1.3k citations indexed

About

Xiaochun Meng is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Surgery. According to data from OpenAlex, Xiaochun Meng has authored 63 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Oncology, 23 papers in Radiology, Nuclear Medicine and Imaging and 18 papers in Surgery. Recurrent topics in Xiaochun Meng's work include Radiomics and Machine Learning in Medical Imaging (19 papers), Colorectal Cancer Surgical Treatments (17 papers) and Liver Disease and Transplantation (8 papers). Xiaochun Meng is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), Colorectal Cancer Surgical Treatments (17 papers) and Liver Disease and Transplantation (8 papers). Xiaochun Meng collaborates with scholars based in China, United States and United Kingdom. Xiaochun Meng's co-authors include Kangshun Zhu, Xin Gao, Wensou Huang, Peiyi Xie, Zhenhui Li, Fei Xiong, Wei Xia, Rui Zhang, Hong Shan and Mingyue Cai and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Xiaochun Meng

60 papers receiving 1.3k citations

Hit Papers

A CT-based deep learning radiomics nomogram for predictin... 2022 2026 2023 2024 2022 25 50 75 100

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaochun Meng China 18 648 425 296 262 259 63 1.3k
Todd Schlachter United States 16 605 0.9× 260 0.6× 207 0.7× 293 1.1× 634 2.4× 43 1.3k
Lynn Jeanette Savic Germany 22 780 1.2× 309 0.7× 194 0.7× 296 1.1× 722 2.8× 62 1.6k
Kenshiro Shiraishi Japan 23 525 0.8× 307 0.7× 271 0.9× 557 2.1× 109 0.4× 70 1.4k
Dengbin Wang China 19 452 0.7× 241 0.6× 192 0.6× 111 0.4× 96 0.4× 48 891
Bruno Vanderlinden Belgium 17 862 1.3× 446 1.0× 135 0.5× 495 1.9× 188 0.7× 38 1.4k
Hubing Wu China 21 547 0.8× 493 1.2× 343 1.2× 338 1.3× 138 0.5× 100 1.4k
Nalee Kim South Korea 17 293 0.5× 248 0.6× 174 0.6× 242 0.9× 192 0.7× 76 884
Junlin Zhou China 18 668 1.0× 200 0.5× 158 0.5× 353 1.3× 56 0.2× 159 1.3k
Ji Hye Min South Korea 23 649 1.0× 473 1.1× 524 1.8× 491 1.9× 911 3.5× 117 1.8k

Countries citing papers authored by Xiaochun Meng

Since Specialization
Citations

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

Fields of papers citing papers by Xiaochun Meng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaochun Meng

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaochun Meng. A scholar is included among the top collaborators of Xiaochun Meng 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 Xiaochun Meng. Xiaochun Meng 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.
3.
Xie, Peiyi, Qitong Huang, Li Zheng, et al.. (2024). Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study. European Radiology. 35(3). 1382–1393. 4 indexed citations
4.
Liu, Dongyu, Jinming Zhang, Qi Yu, et al.. (2024). Tetrandrine Alleviates Pulmonary Fibrosis by Modulating Lung Microbiota‐Derived Metabolism and Ameliorating Alveolar Epithelial Cell Senescence. Phytotherapy Research. 39(1). 298–314. 3 indexed citations
6.
Yao, Jiayin, Jie Zhou, Min Zhang, et al.. (2023). Computed tomography‐based radiomics nomogram using machine learning for predicting 1‐year surgical risk after diagnosis of Crohn's disease. Medical Physics. 50(6). 3862–3872. 10 indexed citations
7.
Xie, Yumo, Jinxin Lin, Ning Zhang, et al.. (2023). Prevalent Pseudoprogression and Pseudoresidue in Patients With Rectal Cancer Treated With Neoadjuvant Immune Checkpoint Inhibitors. Journal of the National Comprehensive Cancer Network. 21(2). 133–142.e3. 23 indexed citations
8.
Zhang, Fangling, Ling Wang, Zhiming Zeng, et al.. (2023). Residual abnormalities on CTE predict adverse outcomes in Crohn's disease with endoscopic healing. Digestive and Liver Disease. 56(2). 248–257. 2 indexed citations
9.
Pang, Xiaolin, Peiyi Xie, Yu Li, et al.. (2022). A new magnetic resonance imaging tumour response grading scheme for locally advanced rectal cancer. British Journal of Cancer. 127(2). 268–277. 10 indexed citations
11.
Liu, Zongchao, Juan Li, Xiaolin Wang, et al.. (2021). Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer. Journal of Translational Medicine. 19(1). 256–256. 17 indexed citations
12.
Chen, Yonghe, Kaikai Wei, Dan Liu, et al.. (2021). A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer. Frontiers in Oncology. 11. 675458–675458. 27 indexed citations
13.
Cao, Wuteng, Qi Zou, Yandong Zhao, et al.. (2020). Application of liver acquisition with volume acceleration enhanced sequence in improving the accuracy of reassessing organ-invasive rectal mucinous adenocarcinoma after chemoradiation. European Journal of Radiology. 133. 109368–109368. 1 indexed citations
14.
Zhao, Xingyu, Peiyi Xie, Mengmeng Wang, et al.. (2020). Deep learning–based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: A multicentre study. EBioMedicine. 56. 102780–102780. 68 indexed citations
15.
Yu, Hua, Xiaoqiang Yao, & Xiaochun Meng. (2019). A novel LncRNA (LOC105371049) regulates colorectal cancer proliferation, metastasis and metabolism. Annals of Oncology. 30. iv35–iv36. 4 indexed citations
16.
Meng, Xiaochun, Wei Xia, Peiyi Xie, et al.. (2018). Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. European Radiology. 29(6). 3200–3209. 114 indexed citations
17.
Meng, Xiaochun, Binghui Chen, Jingjun Huang, et al.. (2018). Early prediction of survival in hepatocellular carcinoma patients treated with transarterial chemoembolization plus sorafenib. World Journal of Gastroenterology. 24(4). 484–493. 8 indexed citations
18.
Zheng, Bowen, Binsheng Fu, Tao Wu, et al.. (2017). Tardus parvus waveforms in Doppler ultrasonography for hepatic artery stenosis after liver transplantation: can a new cut-off value guide the next step?. Abdominal Radiology. 43(7). 1634–1641. 7 indexed citations
19.
Zhu, Kangshun, Junwei Chen, Lisha Lai, et al.. (2014). Hepatocellular Carcinoma with Portal Vein Tumor Thrombus: Treatment with Transarterial Chemoembolization Combined with Sorafenib—A Retrospective Controlled Study. Radiology. 272(1). 284–293. 132 indexed citations
20.
Zhu, Kangshun, Xiaochun Meng, Bin Zhou, et al.. (2013). Percutaneous Transsplenic Portal Vein Catheterization: Technical Procedures, Safety, and Clinical Applications. Journal of Vascular and Interventional Radiology. 24(4). 518–527. 51 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026