Yahong Luo

2.0k total citations
62 papers, 1.4k citations indexed

About

Yahong Luo is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Obstetrics and Gynecology. According to data from OpenAlex, Yahong Luo has authored 62 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Radiology, Nuclear Medicine and Imaging, 28 papers in Pulmonary and Respiratory Medicine and 15 papers in Obstetrics and Gynecology. Recurrent topics in Yahong Luo's work include Radiomics and Machine Learning in Medical Imaging (42 papers), MRI in cancer diagnosis (19 papers) and Endometrial and Cervical Cancer Treatments (15 papers). Yahong Luo is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (42 papers), MRI in cancer diagnosis (19 papers) and Endometrial and Cervical Cancer Treatments (15 papers). Yahong Luo collaborates with scholars based in China and United States. Yahong Luo's co-authors include Xiran Jiang, Tao Yu, Wenyan Jiang, Shandong Wu, Yue Dong, Aly A. Mohamed, Rachel C. Jankowitz, Hong Peng, Yangyang Kan and Wendie A. Berg and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Epidemiology and Physics in Medicine and Biology.

In The Last Decade

Yahong Luo

60 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yahong Luo China 20 1.1k 474 464 281 175 62 1.4k
Elaine Johanna Limkin France 12 1.4k 1.3× 578 1.2× 202 0.4× 616 2.2× 139 0.8× 26 1.8k
Zhouyang Lian China 14 780 0.7× 242 0.5× 149 0.3× 148 0.5× 123 0.7× 37 1.1k
Stefano Trebeschi Netherlands 16 1.1k 1.1× 370 0.8× 201 0.4× 534 1.9× 55 0.3× 47 1.5k
Nathaniel Braman United States 9 1.5k 1.4× 496 1.0× 349 0.8× 282 1.0× 30 0.2× 22 1.6k
Silvia Obenauer Germany 21 569 0.5× 585 1.2× 417 0.9× 244 0.9× 51 0.3× 66 1.2k
Lianzhen Zhong China 15 711 0.7× 389 0.8× 155 0.3× 208 0.7× 83 0.5× 22 934
Alberto Traverso Netherlands 20 1.4k 1.3× 482 1.0× 343 0.7× 271 1.0× 56 0.3× 68 1.7k
Shufang Pei China 12 833 0.8× 212 0.4× 229 0.5× 134 0.5× 30 0.2× 23 1.0k
Margarita Kirienko Italy 28 1.8k 1.6× 913 1.9× 255 0.5× 489 1.7× 33 0.2× 69 2.3k

Countries citing papers authored by Yahong Luo

Since Specialization
Citations

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

Fields of papers citing papers by Yahong Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yahong Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Yahong Luo. A scholar is included among the top collaborators of Yahong Luo 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 Yahong Luo. Yahong Luo 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.
Hu, Yue, Xiaoyu Wang, Hongbo Wang, et al.. (2024). Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study. Cancer Imaging. 24(1). 119–119. 4 indexed citations
2.
Fan, Ying, et al.. (2023). Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC. European Radiology. 33(11). 7902–7912. 12 indexed citations
3.
Fan, Ying, Huanhuan Chen, Xiaoyu Wang, et al.. (2023). Brain‐Tumor Interface‐Based MRI Radiomics Models to Determine EGFR Mutation, Response to EGFR‐TKI and T790M Resistance Mutation in Non‐Small Cell Lung Carcinoma Brain Metastasis. Journal of Magnetic Resonance Imaging. 58(6). 1838–1847. 10 indexed citations
4.
Song, Qingling, Yanmei Zhu, Yahong Luo, et al.. (2022). MRI outcome evaluation in patients with IB2 and IIA2 squamous cervical cancer stages: preliminary results. Insights into Imaging. 13(1). 148–148.
5.
Jiang, Tao, Huazhe Yang, Yue Dong, et al.. (2022). Multiparametric MRI-based radiomics for the prediction of microvascular invasion in hepatocellular carcinoma. Acta Radiologica. 64(2). 456–466. 10 indexed citations
6.
Shi, Jiaxin, Zilong Zhao, Tao Jiang, et al.. (2022). A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor. Frontiers in Neuroinformatics. 16. 973698–973698. 5 indexed citations
7.
Fan, Ying, Yue Dong, Hongbo Wang, et al.. (2022). Development and externally validate MRI-based nomogram to assess EGFR and T790M mutations in patients with metastatic lung adenocarcinoma. European Radiology. 32(10). 6739–6751. 15 indexed citations
8.
Wang, Xiaoyu, Guanyu Liu, Wenwen Jing, et al.. (2022). Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor. Magnetic Resonance Imaging. 91. 91–99. 10 indexed citations
9.
Yu, Tao, et al.. (2022). Multi-parametric MRI-based peritumoral radiomics on prediction of lymph-vascular space invasion in early-stage cervical cancer. SHILAP Revista de lepidopterología. 28(4). 312–321. 22 indexed citations
10.
11.
Shi, Jiaxin, Yue Dong, Wenyan Jiang, et al.. (2021). MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study. Magnetic Resonance Imaging. 88. 1–8. 38 indexed citations
12.
Liu, Liying, Shuo Wang, Tao Yu, et al.. (2021). Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer. Annals of Translational Medicine. 10(2). 120–120. 4 indexed citations
13.
Liu, Ying, Minghao Wu, Yuwei Zhang, et al.. (2021). Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer. Frontiers in Oncology. 11. 657615–657615. 51 indexed citations
14.
Jiang, Tao, Jiangdian Song, Xiaoyu Wang, et al.. (2021). Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study. Molecular Imaging and Biology. 24(4). 550–559. 30 indexed citations
15.
Jiang, Xiran, Yangyang Kan, Tao Yu, et al.. (2020). MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(3). 995–1002. 61 indexed citations
16.
17.
Zhang, Xuan, et al.. (2019). A study of dynamic contrast-enhanced MR imaging features and influence factors of pelvic bone marrow in adult females. Osteoporosis International. 30(12). 2469–2476. 1 indexed citations
18.
Li, Qiang, et al.. (2019). Clinical and epidemiologic factors associated with breast cancer and its subtypes among Northeast Chinese women. Cancer Medicine. 8(17). 7431–7445. 12 indexed citations
19.
20.
Dai, Hong, Ye Yan, Peishan Wang, et al.. (2014). Distribution of mammographic density and its influential factors among Chinese women. International Journal of Epidemiology. 43(4). 1240–1251. 65 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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