Tsung‐Ying Ho

1.1k total citations
24 papers, 592 citations indexed

About

Tsung‐Ying Ho is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Oncology. According to data from OpenAlex, Tsung‐Ying Ho has authored 24 papers receiving a total of 592 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Surgery and 7 papers in Oncology. Recurrent topics in Tsung‐Ying Ho's work include Radiomics and Machine Learning in Medical Imaging (5 papers), Head and Neck Cancer Studies (5 papers) and Thyroid Cancer Diagnosis and Treatment (5 papers). Tsung‐Ying Ho is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), Head and Neck Cancer Studies (5 papers) and Thyroid Cancer Diagnosis and Treatment (5 papers). Tsung‐Ying Ho collaborates with scholars based in Taiwan and United States. Tsung‐Ying Ho's co-authors include Chi‐Tung Cheng, Chien‐Hung Liao, I‐Fang Chung, C. C. Chang, Chih-Chi Chen, Kun‐Ju Lin, Dakai Jin, Dazhou Guo, Le Lü and Jing Xiao and has published in prestigious journals such as PLoS ONE, Medicine and Journal of Nuclear Medicine.

In The Last Decade

Tsung‐Ying Ho

23 papers receiving 584 citations

Peers

Tsung‐Ying Ho
Houman Sotoudeh United States
Laurent Létourneau‐Guillon Canada
Yangsean Choi South Korea
Jason Cai United States
Nitamar Abdala Brazil
Huishu Yuan China
Min Kyoung Lee South Korea
Benjamin Wildman‐Tobriner United States
Donghyun Kim South Korea
Beomseok Sohn South Korea
Houman Sotoudeh United States
Tsung‐Ying Ho
Citations per year, relative to Tsung‐Ying Ho Tsung‐Ying Ho (= 1×) peers Houman Sotoudeh

Countries citing papers authored by Tsung‐Ying Ho

Since Specialization
Citations

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

Fields of papers citing papers by Tsung‐Ying Ho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tsung‐Ying Ho. 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 Tsung‐Ying Ho. The network helps show where Tsung‐Ying Ho may publish in the future.

Co-authorship network of co-authors of Tsung‐Ying Ho

This figure shows the co-authorship network connecting the top 25 collaborators of Tsung‐Ying Ho. A scholar is included among the top collaborators of Tsung‐Ying Ho 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 Tsung‐Ying Ho. Tsung‐Ying Ho 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.
Huang, Shu‐Hua, Chi‐Wei Huang, Shih‐Wei Hsu, et al.. (2025). Development and validation of global tau severity score in Alzheimer's disease using Florzolotau (18F) PET. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 138. 111343–111343.
2.
3.
Huang, Chi‐Wei, et al.. (2023). Gray matter reserve determines glymphatic system function in young‐onset Alzheimer's disease: Evidenced by DTI‐ALPS and compared with age‐matched controls. Psychiatry and Clinical Neurosciences. 77(7). 401–409. 44 indexed citations
4.
Lin, Kun‐Ju, Kuo‐Lun Huang, Shih-Hsin Chen, et al.. (2023). Visual reading for [18F]Florzolotau ([18F]APN-1607) tau PET imaging in clinical assessment of Alzheimer’s disease. Frontiers in Neuroscience. 17. 1148054–1148054. 7 indexed citations
5.
Lin, Yu‐Chun, Gigin Lin, Sumit Pandey, et al.. (2023). Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning. European Radiology. 33(9). 6548–6556. 22 indexed citations
6.
Chen, Ko‐Ting, et al.. (2022). Individual cerebrocerebellar functional network analysis decoding symptomatologic dynamics of postoperative cerebellar mutism syndrome. Cerebral Cortex Communications. 3(1). tgac008–tgac008. 2 indexed citations
7.
Ho, Tsung‐Ying, Kai‐Ping Chang, Wen‐Chi Chou, et al.. (2020). Prognostic significance of the preoperative systemic immune‐inflammation index in patients with oral cavity squamous cell carcinoma treated with curative surgery and adjuvant therapy. Cancer Medicine. 10(2). 649–658. 14 indexed citations
8.
Guo, Dazhou, Dakai Jin, Zhuotun Zhu, et al.. (2020). Organ at Risk Segmentation for Head and Neck Cancer Using Stratified Learning and Neural Architecture Search. 4222–4231. 33 indexed citations
9.
Ho, Tsung‐Ying, et al.. (2020). Classifying Neck Lymph Nodes of Head and Neck Squamous Cell Carcinoma in MRI Images with Radiomic Features. Journal of Digital Imaging. 33(3). 613–618. 30 indexed citations
10.
Jin, Dakai, Dazhou Guo, Tsung‐Ying Ho, et al.. (2020). DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy. Medical Image Analysis. 68. 101909–101909. 46 indexed citations
11.
Ho, Tsung‐Ying, et al.. (2020). Primary lung cancer with radioiodine avidity: A thyroid cancer cohort study. World Journal of Clinical Cases. 9(1). 71–80. 4 indexed citations
12.
Ho, Tsung‐Ying, et al.. (2019). Expressional Profiling of Carpet Glia in the Developing Drosophila Eye Reveals Its Molecular Signature of Morphology Regulators. Frontiers in Neuroscience. 13. 244–244. 8 indexed citations
13.
Cheng, Chi‐Tung, Tsung‐Ying Ho, C. C. Chang, et al.. (2019). Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. European Radiology. 29(10). 5469–5477. 224 indexed citations
14.
Chang, John Wen‐Cheng, Yenlin Huang, Yung‐Feng Lo, et al.. (2019). <p>Sentinel Lymph Node Biopsy Was Associated With Favorable Survival Outcomes For Patients With Clinically Node-Negative Asian Melanoma</p>. Cancer Management and Research. Volume 11. 9655–9664. 7 indexed citations
15.
Kuo, Sheng‐Fong, Tsung‐Ying Ho, Miaw‐Jene Liou, et al.. (2017). Higher body weight and distant metastasis are associated with higher radiation exposure to the household environment from patients with thyroid cancer after radioactive iodine therapy. Medicine. 96(35). e7942–e7942. 4 indexed citations
16.
Ho, Kung‐Chu, Yu-Hua Fang, Hsiao‐Wen Chung, et al.. (2016). A preliminary investigation into textural features of intratumoral metabolic heterogeneity in (18)F-FDG PET for overall survival prognosis in patients with bulky cervical cancer treated with definitive concurrent chemoradiotherapy.. PubMed Central. 6(3). 166–75. 36 indexed citations
17.
Li, Yanrong, et al.. (2015). Risk factors of distant metastasis in the follicular variant of papillary thyroid carcinoma. Journal of the Formosan Medical Association. 115(8). 665–671. 10 indexed citations
18.
Kang, Chung‐Jan, Chien‐Yu Lin, Lan‐Yan Yang, et al.. (2014). Positive Clinical Impact of an Additional PET/CT Scan Before Adjuvant Radiotherapy or Concurrent Chemoradiotherapy in Patients with Advanced Oral Cavity Squamous Cell Carcinoma. Journal of Nuclear Medicine. 56(1). 22–30. 12 indexed citations
19.
Huang, Chung‐Huei, Tzu‐Chieh Chao, Kun‐Ju Lin, et al.. (2012). Therapeutic outcome and prognosis in young patients with papillary and follicular thyroid cancer. Pediatric Surgery International. 28(5). 489–494. 13 indexed citations
20.
Ho, Tsung‐Ying, Miaw‐Jene Liou, Kun‐Ju Lin, & Tzu‐Chen Yen. (2011). Prevalence and significance of thyroid uptake detected by 18F-FDG PET. Endocrine. 40(2). 297–302. 40 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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