William T. Tran

3.8k total citations
86 papers, 1.9k citations indexed

About

William T. Tran is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, William T. Tran has authored 86 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Radiology, Nuclear Medicine and Imaging, 26 papers in Biomedical Engineering and 23 papers in Artificial Intelligence. Recurrent topics in William T. Tran's work include Radiomics and Machine Learning in Medical Imaging (30 papers), AI in cancer detection (22 papers) and Ultrasound and Hyperthermia Applications (21 papers). William T. Tran is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (30 papers), AI in cancer detection (22 papers) and Ultrasound and Hyperthermia Applications (21 papers). William T. Tran collaborates with scholars based in Canada, United Kingdom and United States. William T. Tran's co-authors include Gregory J. Czarnota, Ali Sadeghi‐Naini, Lakshmanan Sannachi, Sonal Gandhi, Hadi Tadayyon, Anoja Giles, Gregory J. Czarnota, Mehrdad J. Gangeh, Azza Al‐Mahrouki and Belinda Curpen and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

William T. Tran

82 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
William T. Tran Canada	27	1.1k	700	486	315	302	86	1.9k
Ali Sadeghi‐Naini Canada	28	1.6k 1.5×	750 1.1×	672 1.4×	159 0.5×	281 0.9×	109	2.1k
Julius Chapiro United States	30	1.4k 1.3×	377 0.5×	356 0.7×	544 1.7×	594 2.0×	144	3.1k
Jeroen Veltman Netherlands	23	1.5k 1.4×	329 0.5×	289 0.6×	308 1.0×	1.5k 5.0×	49	2.7k
Sylvia H. Heywang‐Köbrunner Germany	29	2.3k 2.2×	312 0.4×	736 1.5×	328 1.0×	409 1.4×	96	3.4k
Pallavi Tiwari United States	20	1.8k 1.7×	409 0.6×	416 0.9×	247 0.8×	661 2.2×	71	2.2k
Chantal Van Ongeval Belgium	18	637 0.6×	187 0.3×	320 0.7×	266 0.8×	524 1.7×	97	1.3k
Christopher Abbosh United Kingdom	5	864 0.8×	212 0.3×	339 0.7×	333 1.1×	555 1.8×	6	1.6k
Weijun Peng China	24	1.1k 1.0×	174 0.2×	186 0.4×	262 0.8×	449 1.5×	67	1.5k
Francesca Botta Italy	25	1.7k 1.6×	339 0.5×	218 0.4×	408 1.3×	622 2.1×	62	2.2k
Gordon E. Mawdsley Canada	19	594 0.6×	343 0.5×	448 0.9×	274 0.9×	723 2.4×	50	1.2k

Countries citing papers authored by William T. Tran

Since Specialization

Citations

This map shows the geographic impact of William T. Tran'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 William T. Tran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William T. Tran more than expected).

Fields of papers citing papers by William T. Tran

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William T. Tran. 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 William T. Tran. The network helps show where William T. Tran may publish in the future.

Co-authorship network of co-authors of William T. Tran

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

All Works

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20 of 20 papers shown

Kennedy, Samantha, Patries M. Herst, Kimberly S. Corbin, et al.. (2024). Patient-reported experience with the use of Mepitel Film for prevention of acute radiation dermatitis in breast cancer. Supportive Care in Cancer. 32(1). 89–89. 2 indexed citations

Lagree, Andrew, et al.. (2024). Precision in Parsing: Evaluation of an Open‐Source Named Entity Recognizer (NER) in Veterinary Oncology. Veterinary and Comparative Oncology. 23(1). 102–108. 1 indexed citations

Kiss, Alex, Katarzyna J. Jerzak, Sonal Gandhi, et al.. (2023). Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning. Genes. 14(9). 1768–1768. 3 indexed citations

Tramm, Trine, Shane R. Stecklein, Navita Somaiah, et al.. (2023). Individualising radiation therapy decisions in breast cancer patients based on tumour infiltrating lymphocytes and genomic biomarkers. The Breast. 71. 13–21. 5 indexed citations

Tran, William T., et al.. (2022). Automatic characterization of breast lesions using multi-scale attention-guided deep learning of digital histology images. Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization. 11(1). 103–111. 2 indexed citations

Lagree, Andrew, Fang‐I Lu, Jonathan Klein, et al.. (2022). Comparative Evaluation of Tumor-Infiltrating Lymphocytes in Companion Animals: Immuno-Oncology as a Relevant Translational Model for Cancer Therapy. Cancers. 14(20). 5008–5008. 13 indexed citations

Lagree, Andrew, et al.. (2022). Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. Scientific Reports. 12(1). 9690–9690. 24 indexed citations

Lagree, Andrew, Fang‐I Lu, David W. Dodington, et al.. (2021). Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Current Oncology. 28(6). 4298–4316. 11 indexed citations

Sannachi, Lakshmanan, Archya Dasgupta, Daniel DiCenzo, et al.. (2021). A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods. Oncotarget. 12(2). 81–94. 9 indexed citations

10.

Dasgupta, Archya, Daniel DiCenzo, Irene Karam, et al.. (2021). Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics. Scientific Reports. 11(1). 6117–6117. 11 indexed citations

11.

Lagree, Andrew, Nicholas Meti, Fang‐I Lu, et al.. (2021). A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Scientific Reports. 11(1). 8025–8025. 48 indexed citations

12.

Dodington, David W., Andrew Lagree, Sami Tabbarah, et al.. (2021). Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Research and Treatment. 186(2). 379–389. 26 indexed citations

13.

Tran, William T., Ali Sadeghi‐Naini, Fang‐I Lu, et al.. (2020). Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence. Canadian Association of Radiologists Journal. 72(1). 98–108. 48 indexed citations

14.

Tran, William T., Daniel DiCenzo, Irene Karam, et al.. (2020). Quantitative Ultrasound Delta-Radiomics During Radiotherapy for Monitoring Treatment Responses in Head and Neck Malignancies. Future Science OA. 6(9). FSO624–FSO624. 19 indexed citations

15.

Lin, Victor S.‐Y., Tina Wu, E. Garcı́a, et al.. (2019). Quantitative Thermal Imaging Using Grey-level Run Length Matrix Texture Features Correlate to Radiation-Induced Skin Toxicity. Journal of medical imaging and radiation sciences. 50(2). S6–S7. 4 indexed citations

16.

Sannachi, Lakshmanan, Mehrdad J. Gangeh, Hadi Tadayyon, et al.. (2019). Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models. Translational Oncology. 12(10). 1271–1281. 30 indexed citations

17.

Tran, William T., Katarzyna J. Jerzak, Fang-I Lu, et al.. (2019). Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics. Journal of medical imaging and radiation sciences. 50(4). S32–S41. 60 indexed citations

18.

Sharma, Deepa, Anoja Giles, William T. Tran, et al.. (2019). Ultrasound microbubble potentiated enhancement of hyperthermia-effect in tumours. PLoS ONE. 14(12). e0226475–e0226475. 16 indexed citations

19.

Sadeghi‐Naini, Ali, et al.. (2017). Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps. Scientific Reports. 7(1). 13638–13638. 48 indexed citations

20.

Sadeghi‐Naini, Ali, Lakshmanan Sannachi, Hadi Tadayyon, et al.. (2017). Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities. Scientific Reports. 7(1). 10352–10352. 42 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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