Shunli Liu

1.1k total citations
35 papers, 765 citations indexed

About

Shunli Liu is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Oncology. According to data from OpenAlex, Shunli Liu has authored 35 papers receiving a total of 765 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Pulmonary and Respiratory Medicine and 9 papers in Oncology. Recurrent topics in Shunli Liu's work include Radiomics and Machine Learning in Medical Imaging (16 papers), Gastric Cancer Management and Outcomes (10 papers) and Sarcoma Diagnosis and Treatment (4 papers). Shunli Liu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (16 papers), Gastric Cancer Management and Outcomes (10 papers) and Sarcoma Diagnosis and Treatment (4 papers). Shunli Liu collaborates with scholars based in China, United States and Australia. Shunli Liu's co-authors include Zhengyang Zhou, Song Liu, Yue Guan, Jian He, Wenxian Guan, Changfeng Ji, Ling Chen, Hua Shi, Jian He and Dapeng Hao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nano Letters and Cancer Research.

In The Last Decade

Shunli Liu

33 papers receiving 759 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shunli Liu China 16 382 318 168 130 116 35 765
Wenpeng Huang China 14 179 0.5× 122 0.4× 105 0.6× 179 1.4× 160 1.4× 108 688
M.J. Scott United States 16 126 0.3× 302 0.9× 112 0.7× 52 0.4× 279 2.4× 41 1.0k
Shenghan Lou China 16 65 0.2× 146 0.5× 127 0.8× 230 1.8× 162 1.4× 49 885
Leonora S. F. Boogerd Netherlands 19 152 0.4× 304 1.0× 424 2.5× 331 2.5× 284 2.4× 32 1.1k
Weigang Yan China 18 267 0.7× 510 1.6× 86 0.5× 113 0.9× 244 2.1× 85 1.1k
Joan Gretton United States 9 325 0.9× 289 0.9× 133 0.8× 70 0.5× 149 1.3× 11 825
Hong‐Wei Gao Taiwan 18 77 0.2× 168 0.5× 36 0.2× 180 1.4× 345 3.0× 85 963
Juno Deguchi Japan 15 80 0.2× 370 1.2× 78 0.5× 56 0.4× 367 3.2× 52 937
Gyung Mo Son South Korea 17 85 0.2× 288 0.9× 185 1.1× 893 6.9× 800 6.9× 76 1.3k

Countries citing papers authored by Shunli Liu

Since Specialization
Citations

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

Fields of papers citing papers by Shunli Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shunli Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Shunli Liu. A scholar is included among the top collaborators of Shunli Liu 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 Shunli Liu. Shunli Liu 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.
Wang, Zhibo, Shunli Liu, Dong‐Zhi Li, et al.. (2025). Development and validation of a CT-based radiomic nomogram for predicting surgical resection risk in patients with adhesive small bowel obstruction. BMC Medical Imaging. 25(1). 46–46. 1 indexed citations
2.
Qiu, Bingjiang, Shunli Liu, Cheng Lu, et al.. (2025). Multitask Deep Learning Based on Longitudinal CT Images Facilitates Prediction of Lymph Node Metastasis and Survival in Chemotherapy-Treated Gastric Cancer. Cancer Research. 85(13). 2527–2536. 2 indexed citations
3.
Liu, Shunli, Jian Jiao, Wushuai Zhang, et al.. (2024). Niche adaptation strategies of comammox Nitrospira in response to nitrogen addition in different types of soil. Applied Soil Ecology. 203. 105682–105682. 1 indexed citations
4.
5.
Liu, Shunli, et al.. (2024). An Integrated Radiopathomics Machine Learning Model to Predict Pathological Response to Preoperative Chemotherapy in Gastric Cancer. Academic Radiology. 32(1). 134–145. 3 indexed citations
6.
Zhang, Xiaoya, Yuanxiang Gao, Lili Wang, et al.. (2024). Diagnostic performance of EfficientNetV2-S method for staging liver fibrosis based on multiparametric MRI. Heliyon. 10(15). e35115–e35115. 2 indexed citations
7.
Zhang, Fen, Kai Sun, Shunli Liu, et al.. (2024). Comparative metabolomics reveals complex metabolic shifts associated with nitrogen-induced color development in mature pepper fruit. Frontiers in Plant Science. 15. 1319680–1319680. 5 indexed citations
8.
Liu, Shunli, et al.. (2024). Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease. Abdominal Radiology. 49(4). 1165–1174. 3 indexed citations
9.
10.
Liu, Ruiqing, Jing Yang, Shunli Liu, et al.. (2023). A Novel Radiomics Model Integrating Luminal and Mesenteric Features to Predict Mucosal Activity and Surgery Risk in Crohn's Disease Patients: A Multicenter Study. Academic Radiology. 30. S207–S219. 20 indexed citations
11.
Wang, Tongyu, Peng Zhang, Hexiang Wang, et al.. (2022). An MRI‐Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors. Journal of Magnetic Resonance Imaging. 58(2). 520–531. 9 indexed citations
12.
Xu, Binjie, Bang Chen, Xiaoli Qi, et al.. (2022). Genome-wide Identification and Expression Analysis of RcMYB Genes in Rhodiola crenulata. Frontiers in Genetics. 13. 831611–831611. 8 indexed citations
13.
Liu, Shunli, Chencui Huang, Jingxu Xu, et al.. (2021). Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. European Radiology. 32(2). 793–805. 39 indexed citations
14.
Liu, Shunli, Jia Guo, Song Liu, et al.. (2021). A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours. Cancer Imaging. 21(1). 20–20. 34 indexed citations
15.
Wang, Hexiang, Dapeng Hao, Yaqiong Ge, et al.. (2020). AnMRI‐Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors. Journal of Magnetic Resonance Imaging. 53(1). 141–151. 22 indexed citations
16.
17.
Liu, Shihe, Shunli Liu, Chuanyu Zhang, et al.. (2020). Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-small-Cell Lung Cancer. Frontiers in Oncology. 10. 1268–1268. 31 indexed citations
18.
Liu, Shunli, Song Liu, Changfeng Ji, et al.. (2018). Radiomics Analysis Using Contrast-Enhanced CT for Preoperative Prediction of Occult Peritoneal Metastasis in Advanced Gastric Cancer. SSRN Electronic Journal. 1 indexed citations
19.
Liu, Shunli, Lijing Zhu, Li Zhu, et al.. (2018). Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT. Scientific Reports. 8(1). 11399–11399. 40 indexed citations
20.
Jiang, Bo, Guohao Liu, Jiashuo Zheng, et al.. (2016). Hephaestin and ceruloplasmin facilitate iron metabolism in the mouse kidney. Scientific Reports. 6(1). 39470–39470. 45 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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