Daniel DiCenzo

462 total citations
18 papers, 209 citations indexed

About

Daniel DiCenzo is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Otorhinolaryngology. According to data from OpenAlex, Daniel DiCenzo has authored 18 papers receiving a total of 209 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Artificial Intelligence and 6 papers in Otorhinolaryngology. Recurrent topics in Daniel DiCenzo's work include Radiomics and Machine Learning in Medical Imaging (17 papers), MRI in cancer diagnosis (9 papers) and AI in cancer detection (8 papers). Daniel DiCenzo is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (17 papers), MRI in cancer diagnosis (9 papers) and AI in cancer detection (8 papers). Daniel DiCenzo collaborates with scholars based in Canada, United States and United Kingdom. Daniel DiCenzo's co-authors include Lakshmanan Sannachi, Gregory J. Czarnota, Archya Dasgupta, William T. Tran, Frances C. Wright, Sonal Gandhi, Irene Karam, Ali Sadeghi‐Naini, Maureen Trudeau and Belinda Curpen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Oncotarget.

In The Last Decade

Daniel DiCenzo

16 papers receiving 209 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel DiCenzo Canada 11 177 89 57 32 28 18 209
Sami Tabbarah Canada 6 142 0.8× 100 1.1× 25 0.4× 12 0.4× 44 1.6× 9 190
Xinzhi Teng Hong Kong 12 281 1.6× 49 0.6× 76 1.3× 38 1.2× 12 0.4× 41 356
Aditi Iyer United States 9 236 1.3× 49 0.6× 58 1.0× 15 0.5× 16 0.6× 16 291
Yushi Chang United States 12 265 1.5× 48 0.5× 87 1.5× 17 0.5× 14 0.5× 21 316
Kenta Ninomiya Japan 9 216 1.2× 66 0.7× 58 1.0× 16 0.5× 11 0.4× 29 283
Manasa Vulchi United States 4 248 1.4× 74 0.8× 35 0.6× 8 0.3× 36 1.3× 5 276
Zongrui Ma China 8 192 1.1× 34 0.4× 58 1.0× 27 0.8× 8 0.3× 13 236
Aasheesh Kanwar United States 4 148 0.8× 30 0.3× 25 0.4× 78 2.4× 13 0.5× 7 190
Kui‐Yuan Liu China 9 177 1.0× 69 0.8× 14 0.2× 86 2.7× 27 1.0× 12 299
Yunxia Huang China 7 156 0.9× 41 0.5× 32 0.6× 5 0.2× 34 1.2× 13 208

Countries citing papers authored by Daniel DiCenzo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel DiCenzo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel DiCenzo

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

All Works

18 of 18 papers shown
1.
2.
Sannachi, Lakshmanan, Joanne Yip, Daniel DiCenzo, et al.. (2025). Quantitative Ultrasound Texture Analysis of Breast Tumors: A Comparison of a Cart-Based and a Wireless Ultrasound Scanner. Journal of Imaging. 11(5). 146–146.
3.
Dasgupta, Archya, Daniel DiCenzo, Irene Karam, et al.. (2024). Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma. Radiology Imaging Cancer. 6(2). e230029–e230029. 3 indexed citations
4.
Dasgupta, Archya, Daniel DiCenzo, Lakshmanan Sannachi, et al.. (2024). Quantitative ultrasound radiomics guided adaptive neoadjuvant chemotherapy in breast cancer: early results from a randomized feasibility study. Frontiers in Oncology. 14. 1273437–1273437. 5 indexed citations
5.
Moore-Palhares, Daniel, Daniel DiCenzo, Archya Dasgupta, et al.. (2024). Predicting Tumor Progression in Patients with Cervical Cancer Using Computer Tomography Radiomic Features. SHILAP Revista de lepidopterología. 4(4). 355–368. 1 indexed citations
6.
Sannachi, Lakshmanan, Daniel DiCenzo, Frances C. Wright, et al.. (2023). A priori prediction of breast cancer response to neoadjuvant chemotherapy using quantitative ultrasound, texture derivative and molecular subtype. Scientific Reports. 13(1). 22687–22687. 8 indexed citations
7.
8.
Dasgupta, Archya, Daniel DiCenzo, Maureen Trudeau, et al.. (2022). Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer. Cancers. 14(5). 1247–1247. 14 indexed citations
9.
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.
Dasgupta, Archya, Daniel DiCenzo, Irene Karam, et al.. (2021). Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma. Clinical and Translational Radiation Oncology. 28. 62–70. 20 indexed citations
12.
Sannachi, Lakshmanan, Archya Dasgupta, Daniel DiCenzo, et al.. (2021). MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Oncotarget. 12(14). 1354–1365. 12 indexed citations
13.
Dasgupta, Archya, Daniel DiCenzo, Maureen Trudeau, et al.. (2021). Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound. Oncotarget. 12(25). 2437–2448. 15 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.
Sannachi, Lakshmanan, et al.. (2020). Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods. Translational Oncology. 13(10). 100827–100827. 23 indexed citations
16.
Dasgupta, Archya, Lakshmanan Sannachi, Daniel DiCenzo, et al.. (2020). Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer. Oncotarget. 11(42). 3782–3792. 28 indexed citations
17.
Dasgupta, Archya, Daniel DiCenzo, Irene Karam, et al.. (2020). Quantitative ultrasound radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma treated with radical radiotherapy. Cancer Medicine. 10(8). 2579–2589. 15 indexed citations
18.
Prent, Nicole, et al.. (2014). Second harmonic generation polarization properties of myofilaments. Journal of Biomedical Optics. 19(5). 56005–56005. 15 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Sami Tabbarah Breakdown of academic impact, for papers by Xinzhi Teng Breakdown of academic impact, for papers by Aditi Iyer Breakdown of academic impact, for papers by Yushi Chang Breakdown of academic impact, for papers by Kenta Ninomiya Breakdown of academic impact, for papers by Manasa Vulchi Breakdown of academic impact, for papers by Zongrui Ma Breakdown of academic impact, for papers by Aasheesh Kanwar Breakdown of academic impact, for papers by Kui‐Yuan Liu Breakdown of academic impact, for papers by Yunxia Huang
Rankless by CCL
2026