Lianzhen Zhong

1.4k total citations · 1 hit paper
22 papers, 934 citations indexed

About

Lianzhen Zhong is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Otorhinolaryngology. According to data from OpenAlex, Lianzhen Zhong has authored 22 papers receiving a total of 934 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Pulmonary and Respiratory Medicine and 6 papers in Otorhinolaryngology. Recurrent topics in Lianzhen Zhong's work include Radiomics and Machine Learning in Medical Imaging (19 papers), Head and Neck Cancer Studies (6 papers) and Gastric Cancer Management and Outcomes (6 papers). Lianzhen Zhong is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), Head and Neck Cancer Studies (6 papers) and Gastric Cancer Management and Outcomes (6 papers). Lianzhen Zhong collaborates with scholars based in China, Italy and United Kingdom. Lianzhen Zhong's co-authors include Jie Tian, Di Dong, Mengjie Fang, Zaiyi Liu, Xiuhong Shan, Francesco Giganti, Diego Palumbo, Jiafu Ji, Jianbo Gao and Francesco De Cobelli and has published in prestigious journals such as JNCI Journal of the National Cancer Institute, Cancer Research and Annals of Oncology.

In The Last Decade

Lianzhen Zhong

21 papers receiving 915 citations

Hit Papers

align trajectories

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.

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						Papers	Cites
Lianzhen Zhong China	15	711	389	208	155	122	22	934
Shufang Pei China	12	833 1.2×	212 0.5×	134 0.6×	229 1.5×	118 1.0×	23	1.0k
Fusheng Ouyang China	13	836 1.2×	281 0.7×	186 0.9×	125 0.8×	145 1.2×	30	1.1k
Xinming Zhao China	17	1.0k 1.5×	248 0.6×	252 1.2×	213 1.4×	93 0.8×	51	1.3k
Sugama Chicklore United Kingdom	10	944 1.3×	437 1.1×	174 0.8×	85 0.5×	145 1.2×	18	1.1k
Mariyah Selmi United Kingdom	3	719 1.0×	202 0.5×	181 0.9×	101 0.7×	96 0.8×	4	834
Michele Fiore Italy	18	300 0.4×	473 1.2×	323 1.6×	52 0.3×	229 1.9×	92	990
Qingyu Yuan China	15	669 0.9×	397 1.0×	242 1.2×	106 0.7×	136 1.1×	23	878
Hailiang Li China	10	697 1.0×	254 0.7×	170 0.8×	113 0.7×	133 1.1×	31	819
Emanuele Voulaz Italy	15	630 0.9×	814 2.1×	185 0.9×	73 0.5×	174 1.4×	53	1.2k
Yahong Luo China	20	1.1k 1.5×	474 1.2×	281 1.4×	464 3.0×	138 1.1×	62	1.4k

Countries citing papers authored by Lianzhen Zhong

Since Specialization

Citations

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

Fields of papers citing papers by Lianzhen Zhong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lianzhen Zhong

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhang, Liwen, Lianzhen Zhong, Ling‐Long Tang, et al.. (2024). TripleSurv: Triplet Time-Adaptive Coordinate Learning Approach for Survival Analysis. IEEE Transactions on Knowledge and Data Engineering. 36(12). 9464–9475.

Huang, Kai, Lianzhen Zhong, Yuan Gao, et al.. (2024). RemixFormer++: A Multi-Modal Transformer Model for Precision Skin Tumor Differential Diagnosis With Memory-Efficient Attention. IEEE Transactions on Medical Imaging. 44(1). 320–337. 1 indexed citations

Lin, Da-Feng, Hailin Li, Ting Liu, et al.. (2024). Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma. JNCI Journal of the National Cancer Institute. 116(8). 1294–1302. 14 indexed citations

Tang, Lin‐Quan, Lianzhen Zhong, Hailin Li, et al.. (2023). Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review. IEEE Reviews in Biomedical Engineering. 17. 118–135. 29 indexed citations

Deng, Jingyu, Di Dong, Zhaoxiang Ye, et al.. (2023). Deep learning‐based radiomics model can predict extranodal soft tissue metastasis in gastric cancer. Medical Physics. 51(1). 267–277. 9 indexed citations

Zhang, Liwen, Lianzhen Zhong, Cong Li, et al.. (2022). Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images. Neural Networks. 152. 394–406. 9 indexed citations

Tang, Lei, Lianzhen Zhong, Siwen Wang, et al.. (2022). Artificial intelligence in gastric cancer: applications and challenges. Gastroenterology report. 10. goac064–goac064. 28 indexed citations

Wang, Xiaoxiao, Liwen Zhang, Lianzhen Zhong, et al.. (2021). Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer. BMC Medical Imaging. 21(1). 58–58. 12 indexed citations

Zhong, Lianzhen, Di Dong, Xueliang Fang, et al.. (2021). A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study. EBioMedicine. 70. 103522–103522. 87 indexed citations

10.

Zhang, Liwen, Di Dong, Lianzhen Zhong, et al.. (2021). Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients. IEEE Journal of Biomedical and Health Informatics. 25(10). 3933–3942. 15 indexed citations

11.

He, Bingxi, Li Li, Di Dong, et al.. (2020). CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study. Science China Information Sciences. 63(7). 45 indexed citations

12.

Hu, Hao, Lixin Gong, Di Dong, et al.. (2020). Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study. Gastrointestinal Endoscopy. 93(6). 1333–1341.e3. 62 indexed citations

13.

Fang, Mengjie, Yangyang Kan, Di Dong, et al.. (2020). Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer. Frontiers in Oncology. 10. 563–563. 50 indexed citations

14.

Zhong, Lianzhen, Xueliang Fang, Di Dong, et al.. (2020). A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0. Radiotherapy and Oncology. 151. 1–9. 45 indexed citations

15.

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 →

16.

Zhong, Lianzhen, Di Dong, Ling‐Long Tang, Shuyan Han, & Jie Tian. (2020). Abstract 5430: Deep learning-based prognosis prediction in T3N1 nasopharyngeal carcinoma patients treated with induction chemotherapy followed by concurrent chemoradiotherapy. Cancer Research. 80(16_Supplement). 5430–5430. 1 indexed citations

17.

Lu, Wei, Lianzhen Zhong, Di Dong, et al.. (2019). Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma. European Journal of Radiology. 118. 231–238. 66 indexed citations

18.

Li, Hailin, Siwen Wang, Mengjie Fang, et al.. (2019). Multiplanar MRI-Based Predictive Model for Preoperative Assessment of Lymph Node Metastasis in Endometrial Cancer. Frontiers in Oncology. 9. 1007–1007. 51 indexed citations

19.

Xu, Min, Mengjie Fang, Jian Zou, et al.. (2019). Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions. European Journal of Radiology. 114. 38–44. 45 indexed citations

20.

Chen, Bin, Lianzhen Zhong, Di Dong, et al.. (2019). Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma. Frontiers in Oncology. 9. 829–829. 22 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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