David Spak

554 total citations · 1 hit paper
10 papers, 335 citations indexed

About

David Spak is a scholar working on Pathology and Forensic Medicine, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, David Spak has authored 10 papers receiving a total of 335 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Pathology and Forensic Medicine, 4 papers in Artificial Intelligence and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in David Spak's work include AI in cancer detection (4 papers), Breast Lesions and Carcinomas (4 papers) and Global Cancer Incidence and Screening (3 papers). David Spak is often cited by papers focused on AI in cancer detection (4 papers), Breast Lesions and Carcinomas (4 papers) and Global Cancer Incidence and Screening (3 papers). David Spak collaborates with scholars based in United States. David Spak's co-authors include Lumarie Santiago, Başak E. Doğan, Mark J. Dryden, Beatriz E. Adrada, Gary J. Whitman, Mark A. Helvie, Gaiane M. Rauch, Lewis E. Foxhall, Kenneth R. Hess and Mary S. Guirguis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Radiographics and Ultrasound in Medicine & Biology.

In The Last Decade

David Spak

8 papers receiving 327 citations

Hit Papers

BI-RADS ® fifth edition: A summary of changes 2017 2026 2020 2023 2017 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Spak United States 5 193 153 93 90 71 10 335
Vivianne Freitas Canada 10 212 1.1× 151 1.0× 116 1.2× 148 1.6× 82 1.2× 35 385
Lorenza Meneghetti Italy 12 178 0.9× 127 0.8× 48 0.5× 97 1.1× 75 1.1× 26 303
Lonie R. Salkowski United States 13 245 1.3× 155 1.0× 94 1.0× 105 1.2× 93 1.3× 24 468
Carolina Rossi Saccarelli United States 8 244 1.3× 107 0.7× 51 0.5× 46 0.5× 57 0.8× 14 305
Ran Gu China 12 136 0.7× 122 0.8× 52 0.6× 47 0.5× 49 0.7× 30 328
Nicole Kay France 4 116 0.6× 164 1.1× 111 1.2× 83 0.9× 103 1.5× 6 335
Athina Vourtsis United States 4 129 0.7× 213 1.4× 111 1.2× 188 2.1× 64 0.9× 4 339
Mengsu Xiao China 12 203 1.1× 171 1.1× 78 0.8× 56 0.6× 80 1.1× 39 390
Carmen van Dooijeweert Netherlands 11 99 0.5× 116 0.8× 131 1.4× 65 0.7× 105 1.5× 29 304
Lídia Tortajada Spain 9 120 0.6× 131 0.9× 48 0.5× 41 0.5× 61 0.9× 14 271

Countries citing papers authored by David Spak

Since Specialization
Citations

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

Fields of papers citing papers by David Spak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Spak

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

All Works

10 of 10 papers shown
1.
Patel, Tejal, Virginia Kaklamani, Maryam Elmi, et al.. (2023). A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms. Radiology Artificial Intelligence. 5(6). e220259–e220259. 6 indexed citations
2.
Candelaria, Rosalind P., Beatriz E. Adrada, Deanna L. Lane, et al.. (2022). Mid-treatment Ultrasound Descriptors as Qualitative Imaging Biomarkers of Pathologic Complete Response in Patients with Triple-Negative Breast Cancer. Ultrasound in Medicine & Biology. 48(6). 1010–1018. 3 indexed citations
3.
Adrada, Beatriz E., et al.. (2022). MRI-guided Breast Biopsy Case-based Review: Essential Techniques and Approaches to Challenging Cases. Radiographics. 42(2). E46–E47. 8 indexed citations
4.
Candelaria, Rosalind P., David Spak, Gaiane M. Rauch, et al.. (2021). BI-RADS Ultrasound Lexicon Descriptors and Stromal Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer. Academic Radiology. 29. S35–S41. 18 indexed citations
5.
Spak, David, Lewis E. Foxhall, Alyssa G. Rieber, et al.. (2020). Retrospective Review of a Mobile Mammography Screening Program in an Underserved Population within a Large Metropolitan Area. Academic Radiology. 29. S173–S179. 12 indexed citations
6.
Spak, David, Wei Wei, Marion E. Scoggins, et al.. (2019). Association of Retrospective Peer Review and Positive Predictive Value of Magnetic Resonance Imaging-Guided Vacuum-Assisted Needle Biopsies of Breast. SHILAP Revista de lepidopterología. 15(4). 229–234. 1 indexed citations
7.
Spak, David & Huong T. Le‐Petross. (2019). Screening Modalities for Women at Intermediate and High Risk for Breast Cancer. Current Breast Cancer Reports. 11(3). 111–116. 1 indexed citations
8.
Spak, David, et al.. (2017). BI-RADS ® fifth edition: A summary of changes. Diagnostic and Interventional Imaging. 98(3). 179–190. 286 indexed citations breakdown →
9.
Spak, David, et al.. (2013). Security Best Practices Efforts and Documents.
10.
Spak, David, et al.. (2005). 日本語版ダイジェスト USGA Green Section RECORD スープリのための新しい道具. 26(262). 98–101.

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

Rankless by CCL
2026