Andrew Lagree

465 total citations
15 papers, 295 citations indexed

About

Andrew Lagree is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Oncology. According to data from OpenAlex, Andrew Lagree has authored 15 papers receiving a total of 295 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Artificial Intelligence and 5 papers in Oncology. Recurrent topics in Andrew Lagree's work include Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (7 papers) and Breast Cancer Treatment Studies (4 papers). Andrew Lagree is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (7 papers) and Breast Cancer Treatment Studies (4 papers). Andrew Lagree collaborates with scholars based in Canada, United Kingdom and United States. Andrew Lagree's co-authors include William T. Tran, Ali Sadeghi‐Naini, Sami Tabbarah, Fang‐I Lu, Katarzyna J. Jerzak, Jonathan Klein, Sonal Gandhi, Tina Wu, Nicholas Meti and Elzbieta Slodkowska and has published in prestigious journals such as Scientific Reports, International Journal of Radiation Oncology*Biology*Physics and The Oncologist.

In The Last Decade

Andrew Lagree

14 papers receiving 292 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrew Lagree Canada 11 192 151 61 52 39 15 295
Mireia Crispin‐Ortuzar United Kingdom 10 337 1.8× 121 0.8× 50 0.8× 67 1.3× 109 2.8× 27 471
Peter Truszkowski United States 2 187 1.0× 211 1.4× 41 0.7× 60 1.2× 50 1.3× 2 341
Luca L. Weishaupt United States 3 225 1.2× 288 1.9× 50 0.8× 61 1.2× 37 0.9× 6 499
Charles Maussion France 4 178 0.9× 190 1.3× 57 0.9× 67 1.3× 74 1.9× 12 338
Sami Tabbarah Canada 6 142 0.7× 100 0.7× 44 0.7× 25 0.5× 18 0.5× 9 190
Mishka Gidwani United States 5 315 1.6× 143 0.9× 27 0.4× 65 1.3× 107 2.7× 7 480
Venkata N. P. Vemuri United States 4 98 0.5× 143 0.9× 41 0.7× 51 1.0× 20 0.5× 6 305
Ran Gu China 12 136 0.7× 122 0.8× 49 0.8× 52 1.0× 47 1.2× 30 328
Paula Toro United States 8 125 0.7× 92 0.6× 37 0.6× 58 1.1× 56 1.4× 27 239
Samantha Bove Italy 11 216 1.1× 191 1.3× 67 1.1× 71 1.4× 65 1.7× 35 343

Countries citing papers authored by Andrew Lagree

Since Specialization
Citations

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

Fields of papers citing papers by Andrew Lagree

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrew Lagree

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

All Works

15 of 15 papers shown
1.
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
2.
Gandhi, Sonal, Andrew Lagree, Robert C. Grant, et al.. (2024). Identifying Oncology Patients at High Risk for Potentially Preventable Emergency Department Visits Using a Novel Definition. JCO Clinical Cancer Informatics. 8(8). e2400147–e2400147.
3.
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
4.
Lagree, Andrew, Robert C. Grant, Alex Kiss, et al.. (2023). Drivers of Emergency Department Use Among Oncology Patients in the Era of Novel Cancer Therapeutics: A Systematic Review. The Oncologist. 28(12). 1020–1033. 11 indexed citations
5.
Ferré, Romuald, Andrew Lagree, Sami Tabbarah, et al.. (2023). Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes. Breast Disease. 42(1). 59–66. 19 indexed citations
6.
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
7.
8.
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
9.
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
10.
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
11.
Meti, Nicholas, Andrew Lagree, Sami Tabbarah, et al.. (2021). Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features. JCO Clinical Cancer Informatics. 5(5). 66–80. 31 indexed citations
12.
Tabbarah, Sami, Andrew Lagree, Tina Wu, et al.. (2020). Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning. International Journal of Radiation Oncology*Biology*Physics. 106(5). 1071–1083. 28 indexed citations
13.
Wu, Tina, Sami Tabbarah, Andrew Lagree, & William T. Tran. (2020). Predictive Models for Neoadjuvant Chemotherapy Response in Breast Cancer Patients Using Quantitative Digital Pathology Imaging Biomarkers. Journal of medical imaging and radiation sciences. 51(3). S12–S12. 2 indexed citations
14.
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
15.
Tran, William T., Irene Karam, Ian Poon, et al.. (2019). Predictive Quantitative Ultrasound Radiomic Markers Associated With Treatment Response in Head and Neck Cancer. Future Science OA. 6(1). FSO433–FSO433. 18 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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2026