Qingtao Qiu

842 total citations
51 papers, 614 citations indexed

About

Qingtao Qiu is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Qingtao Qiu has authored 51 papers receiving a total of 614 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Radiology, Nuclear Medicine and Imaging, 26 papers in Pulmonary and Respiratory Medicine and 13 papers in Biomedical Engineering. Recurrent topics in Qingtao Qiu's work include Radiomics and Machine Learning in Medical Imaging (38 papers), Advanced X-ray and CT Imaging (13 papers) and Medical Imaging Techniques and Applications (10 papers). Qingtao Qiu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (38 papers), Advanced X-ray and CT Imaging (13 papers) and Medical Imaging Techniques and Applications (10 papers). Qingtao Qiu collaborates with scholars based in China, United States and France. Qingtao Qiu's co-authors include Yong Yin, Jinghao Duan, Guanzhong Gong, Ligang Xing, Qiang Wen, Jian Zhu, Alei Feng, Changsheng Ma, Dengwang Li and Jie Lu and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Qingtao Qiu

47 papers receiving 607 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingtao Qiu China 16 495 273 134 118 97 51 614
Gretchen Hermann United States 6 551 1.1× 371 1.4× 143 1.1× 204 1.7× 59 0.6× 10 696
Mahmoud A. Abdalah United States 9 627 1.3× 258 0.9× 185 1.4× 226 1.9× 55 0.6× 19 733
Mi Huang United States 10 403 0.8× 195 0.7× 84 0.6× 119 1.0× 85 0.9× 10 505
M Jermoumi United States 4 586 1.2× 241 0.9× 99 0.7× 228 1.9× 48 0.5× 9 668
Jessica Goya-Outi France 4 864 1.7× 317 1.2× 180 1.3× 189 1.6× 99 1.0× 5 964
R. Berenguer Spain 10 470 0.9× 197 0.7× 88 0.7× 208 1.8× 81 0.8× 20 632
Sarah A. Mattonen Canada 13 796 1.6× 457 1.7× 138 1.0× 214 1.8× 81 0.8× 36 963
Wen Yu China 15 417 0.8× 501 1.8× 207 1.5× 92 0.8× 192 2.0× 60 826
N. Albarghach France 4 806 1.6× 285 1.0× 132 1.0× 144 1.2× 181 1.9× 5 890
Julian M.M. Rogasch Germany 15 360 0.7× 217 0.8× 70 0.5× 68 0.6× 69 0.7× 63 570

Countries citing papers authored by Qingtao Qiu

Since Specialization
Citations

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

Fields of papers citing papers by Qingtao Qiu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingtao Qiu

This figure shows the co-authorship network connecting the top 25 collaborators of Qingtao Qiu. A scholar is included among the top collaborators of Qingtao Qiu 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 Qingtao Qiu. Qingtao Qiu 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.
He, Rui, et al.. (2025). Proton stereotactic centralized ablative radiation therapy for treating bulky tumor: a treatment plan study. Frontiers in Oncology. 15. 1474327–1474327.
2.
Qiu, Qingtao, et al.. (2024). Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma. BMC Cancer. 24(1). 363–363. 8 indexed citations
3.
4.
Zhu, Chao, et al.. (2024). Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram. European journal of medical research. 29(1). 217–217. 1 indexed citations
5.
Qiu, Qingtao, et al.. (2023). Deep learning‐based combination of [18F]‐FDG PET and CT images for producing pulmonary perfusion image. Medical Physics. 50(12). 7779–7790. 2 indexed citations
6.
Qiu, Qingtao, et al.. (2023). Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model. Cancers. 15(18). 4443–4443. 6 indexed citations
7.
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9.
Gong, Guanzhong, et al.. (2020). Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging. BMC Medical Imaging. 20(1). 12–12. 21 indexed citations
10.
Chen, Xia, Xin Tong, Qingtao Qiu, et al.. (2020). Radiomics Nomogram for Predicting Locoregional Failure in Locally Advanced Non–small Cell Lung Cancer Treated with Definitive Chemoradiotherapy. Academic Radiology. 29. S53–S61. 5 indexed citations
11.
12.
Chen, Weidong, et al.. (2020). Application of CT radiomics features to predict the EGFR mutation status and therapeutic sensitivity to TKIs of advanced lung adenocarcinoma. Translational Cancer Research. 9(11). 6683–6690. 15 indexed citations
13.
14.
Qiu, Qingtao, et al.. (2019). Feasibility of Automatic Segmentation of Hippocampus Based on Deep Learning in Hippocampus-Sparing Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 105(1). E137–E138. 2 indexed citations
15.
Zhang, Jing, et al.. (2019). Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma. Translational Cancer Research. 8(1). 130–140. 4 indexed citations
16.
Gong, Guanzhong, et al.. (2019). Identifying pathological subtypes of non-small-cell lung cancer by using the radiomic features of 18F-fluorodeoxyglucose positron emission computed tomography. Translational Cancer Research. 8(5). 1741–1749. 21 indexed citations
17.
Zhao, Shuliang, Yi Su, Jinghao Duan, et al.. (2019). Radiomics signature extracted from diffusion-weighted magnetic resonance imaging predicts outcomes in osteosarcoma. Journal of bone oncology. 19. 100263–100263. 48 indexed citations
18.
Qiu, Qingtao, Jinghao Duan, Changsheng Ma, et al.. (2019). Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability. Quantitative Imaging in Medicine and Surgery. 9(3). 453–464. 55 indexed citations
19.
Qiu, Qingtao, Jinghao Duan, Guanzhong Gong, et al.. (2017). Reproducibility of radiomic features with GrowCut and GraphCut semiautomatic tumor segmentation in hepatocellular carcinoma. Translational Cancer Research. 6(5). 940–948. 10 indexed citations
20.
Qiu, Qingtao, Jinghao Duan, Guanzhong Gong, et al.. (2017). Reproducibility of radiomic features with GrowCut and GraphCut semiautomatic tumor segmentation in hepatocellular carcinoma. Translational Cancer Research. 6(5). 940–948. 16 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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