Ya‐Ting Jan

609 total citations
22 papers, 449 citations indexed

About

Ya‐Ting Jan is a scholar working on Physiology, Surgery and Obstetrics and Gynecology. According to data from OpenAlex, Ya‐Ting Jan has authored 22 papers receiving a total of 449 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Physiology, 9 papers in Surgery and 9 papers in Obstetrics and Gynecology. Recurrent topics in Ya‐Ting Jan's work include Nutrition and Health in Aging (12 papers), Endometrial and Cervical Cancer Treatments (9 papers) and Cancer survivorship and care (5 papers). Ya‐Ting Jan is often cited by papers focused on Nutrition and Health in Aging (12 papers), Endometrial and Cervical Cancer Treatments (9 papers) and Cancer survivorship and care (5 papers). Ya‐Ting Jan collaborates with scholars based in Taiwan and Macao. Ya‐Ting Jan's co-authors include Jie Lee, Chih‐Long Chang, Meng‐Hao Wu, Jhen‐Bin Lin, Fang‐Ju Sun, Yu-Jen Chen, Yu‐Jen Chen, Jon‐Kway Huang, Yuh‐Cheng Yang and Jen‐Ruei Chen and has published in prestigious journals such as Nutrients, Medicine and The American Journal of Surgery.

In The Last Decade

Ya‐Ting Jan

22 papers receiving 440 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ya‐Ting Jan Taiwan 12 169 158 128 113 73 22 449
Lishuang Wei China 13 168 1.0× 75 0.5× 9 0.1× 221 2.0× 18 0.2× 44 460
Cheng-Chou Lai Taiwan 10 74 0.4× 137 0.9× 5 0.0× 249 2.2× 27 0.4× 29 386
Yusuke Suwa Japan 17 63 0.4× 674 4.3× 33 0.3× 773 6.8× 45 0.6× 85 1.0k
Yi‐Ting Huang Taiwan 11 7 0.0× 125 0.8× 127 1.0× 38 0.3× 89 1.2× 33 339
Amit K. Chowdhry United States 12 23 0.1× 77 0.5× 26 0.2× 34 0.3× 20 0.3× 27 278
Clay Cauthen United States 7 29 0.2× 91 0.6× 8 0.1× 128 1.1× 21 0.3× 15 459
Jonathan B. Yuval Israel 14 97 0.6× 527 3.3× 7 0.1× 289 2.6× 42 0.6× 46 723
Brodie Loudon United Kingdom 11 37 0.2× 54 0.3× 21 0.2× 8 0.1× 36 0.5× 14 470
Maciej Bobowicz Poland 12 112 0.7× 283 1.8× 5 0.0× 76 0.7× 46 0.6× 47 469
Andrew Allison United Kingdom 12 42 0.2× 369 2.3× 16 0.1× 247 2.2× 15 0.2× 16 486

Countries citing papers authored by Ya‐Ting Jan

Since Specialization
Citations

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

Fields of papers citing papers by Ya‐Ting Jan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ya‐Ting Jan. 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 Ya‐Ting Jan. The network helps show where Ya‐Ting Jan may publish in the future.

Co-authorship network of co-authors of Ya‐Ting Jan

This figure shows the co-authorship network connecting the top 25 collaborators of Ya‐Ting Jan. A scholar is included among the top collaborators of Ya‐Ting Jan 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 Ya‐Ting Jan. Ya‐Ting Jan 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.
Lee, Jie, et al.. (2024). Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning. European Radiology. 35(7). 4289–4299. 4 indexed citations
2.
Jan, Ya‐Ting, et al.. (2024). Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer. Supportive Care in Cancer. 32(8). 544–544. 4 indexed citations
3.
Lin, Jhen‐Bin, et al.. (2024). Thresholds of Body Composition Changes Associated with Survival During Androgen Deprivation Therapy in Prostate Cancer. European Urology Open Science. 70. 99–108. 1 indexed citations
4.
Chang, Chih‐Long, et al.. (2023). Association of malignant ascites with systemic inflammation and muscle loss after treatment in advanced‐stage ovarian cancer. Journal of Cachexia Sarcopenia and Muscle. 14(5). 2114–2125. 9 indexed citations
5.
Chang, Chih‐Long, et al.. (2023). Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer. Journal of Cachexia Sarcopenia and Muscle. 14(5). 2044–2053. 24 indexed citations
6.
7.
Jan, Ya‐Ting, Pei‐Shan Tsai, Wenhui Huang, et al.. (2023). Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors. Insights into Imaging. 14(1). 68–68. 34 indexed citations
8.
Tsai, Pei‐Shan, et al.. (2022). Lower-extremity muscle wasting in patients with peripheral arterial disease: quantitative measurement and evaluation with CT. European Radiology. 33(6). 4063–4072. 15 indexed citations
9.
Lee, Jie, Jhen‐Bin Lin, Tze-Chien Chen, et al.. (2022). Progressive Skeletal Muscle Loss After Surgery and Adjuvant Radiotherapy Impact Survival Outcomes in Patients With Early Stage Cervical Cancer. Frontiers in Nutrition. 8. 773506–773506. 13 indexed citations
10.
Lee, Jie, Jhen‐Bin Lin, Chih‐Long Chang, et al.. (2022). Optimal prophylactic para-aortic radiotherapy in locally advanced cervical cancer: anatomy-based versus margin-based delineation. International Journal of Gynecological Cancer. 32(5). 606–612. 4 indexed citations
11.
Lee, Jie, Jhen‐Bin Lin, Meng‐Hao Wu, et al.. (2021). Association of bowel radiation dose-volume with skeletal muscle loss during pelvic intensity-modulated radiotherapy in cervical cancer. Supportive Care in Cancer. 29(9). 5497–5505. 9 indexed citations
12.
13.
Yang, Yuh‐Cheng, Jen‐Ruei Chen, Yu‐Jen Chen, et al.. (2020). Muscle loss during primary debulking surgery and chemotherapy predicts poor survival in advanced‐stage ovarian cancer. Journal of Cachexia Sarcopenia and Muscle. 11(2). 534–546. 63 indexed citations
14.
Lee, Jie, Jhen‐Bin Lin, Fang‐Ju Sun, et al.. (2017). Safety and efficacy of semiextended field intensity-modulated radiation therapy and concurrent cisplatin in locally advanced cervical cancer patients. Medicine. 96(10). e6158–e6158. 21 indexed citations
15.
Lee, Jie, Jhen‐Bin Lin, Chih‐Long Chang, et al.. (2017). Impact of para-aortic recurrence risk-guided intensity-modulated radiotherapy in locally advanced cervical cancer with positive pelvic lymph nodes. Gynecologic Oncology. 148(2). 291–298. 47 indexed citations
16.
17.
Jan, Ya‐Ting, et al.. (2016). The air matters – sleeve air cavity as a marker guiding image-guided helical tomotherapy to target cervical cancer. Journal of Contemporary Brachytherapy. 1(1). 82–87. 4 indexed citations
18.
Jan, Ya‐Ting, et al.. (2008). Phlebosclerotic Colitis. Journal of the American College of Surgeons. 207(5). 785–785. 8 indexed citations
19.
Jan, Ya‐Ting, et al.. (2008). Internal supravesical hernia. The American Journal of Surgery. 196(4). e27–e28. 19 indexed citations
20.
Jan, Ya‐Ting, et al.. (2005). Visualization Rate and Pattern of Normal Appendix on Multidetector Computed Tomography by Using Multiplanar Reformation Display. Journal of Computer Assisted Tomography. 29(4). 446–451. 38 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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