Craig Barker

1.5k total citations
37 papers, 1.1k citations indexed

About

Craig Barker is a scholar working on Oncology, Pulmonary and Respiratory Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Craig Barker has authored 37 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Oncology, 12 papers in Pulmonary and Respiratory Medicine and 11 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Craig Barker's work include Cancer Immunotherapy and Biomarkers (24 papers), Lung Cancer Treatments and Mutations (9 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Craig Barker is often cited by papers focused on Cancer Immunotherapy and Biomarkers (24 papers), Lung Cancer Treatments and Mutations (9 papers) and Radiomics and Machine Learning in Medical Imaging (8 papers). Craig Barker collaborates with scholars based in United Kingdom, United States and Spain. Craig Barker's co-authors include Marietta Scott, Jill Walker, Paul Scorer, Anita Midha, Marlon C. Rebelatto, M. Ratcliffe, Michel E. Vandenberghe, Alan Sharpe, Hytham Al‐Masri and Marc Ballas and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Cancer Research.

In The Last Decade

Craig Barker

36 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Craig Barker United Kingdom 16 816 445 219 197 177 37 1.1k
Nolwenn Le Stang France 17 352 0.4× 628 1.4× 219 1.0× 144 0.7× 70 0.4× 28 1.1k
Robin Edwards United States 12 492 0.6× 220 0.5× 205 0.9× 219 1.1× 131 0.7× 25 1.1k
Ahrong Kim South Korea 16 369 0.5× 344 0.8× 116 0.5× 154 0.8× 126 0.7× 49 905
Cory Batenchuk United States 10 660 0.8× 356 0.8× 85 0.4× 183 0.9× 186 1.1× 16 881
Philipp Jurmeister Germany 17 291 0.4× 239 0.5× 150 0.7× 204 1.0× 68 0.4× 45 781
Joyce Sanders Netherlands 23 718 0.9× 324 0.7× 230 1.1× 261 1.3× 316 1.8× 73 1.5k
Bailiang Li China 13 403 0.5× 413 0.9× 246 1.1× 312 1.6× 165 0.9× 25 994
R. Storey Wilson United States 10 216 0.3× 370 0.8× 125 0.6× 93 0.5× 104 0.6× 11 848
Balázs Ács Sweden 17 563 0.7× 167 0.4× 461 2.1× 333 1.7× 173 1.0× 50 1.2k
Germán Corredor United States 13 348 0.4× 293 0.7× 524 2.4× 126 0.6× 95 0.5× 57 908

Countries citing papers authored by Craig Barker

Since Specialization
Citations

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

Fields of papers citing papers by Craig Barker

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Craig Barker

This figure shows the co-authorship network connecting the top 25 collaborators of Craig Barker. A scholar is included among the top collaborators of Craig Barker 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 Craig Barker. Craig Barker 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
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Harel, Jonathan, Giuseppe Mallel, Michel E. Vandenberghe, et al.. (2023). Abstract P6-04-05: A fully automatic artificial intelligence system for accurate and reproducible HER2 IHC scoring in breast cancer. Cancer Research. 83(5_Supplement). P6–4. 1 indexed citations
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Vandenberghe, Michel E., et al.. (2019). Fast fluorescence in situ hybridisation for the enhanced detection of MET in non-small cell lung cancer. PLoS ONE. 14(10). e0223926–e0223926. 8 indexed citations
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Brown, Helen, Johan Vansteenkiste, Kazuhiko Nakagawa, et al.. (2019). Programmed Cell Death Ligand 1 Expression in Untreated EGFR Mutated Advanced NSCLC and Response to Osimertinib Versus Comparator in FLAURA. Journal of Thoracic Oncology. 15(1). 138–143. 38 indexed citations
7.
Zając, M, Marietta Scott, M. Ratcliffe, et al.. (2019). Concordance among four commercially available, validated programmed cell death ligand-1 assays in urothelial carcinoma. Diagnostic Pathology. 14(1). 99–99. 38 indexed citations
8.
Scott, Marietta, et al.. (2019). Comparison of patient populations identified by different PD-L1 assays in in triple-negative breast cancer (TNBC). Annals of Oncology. 30. iii4–iii4. 24 indexed citations
9.
Williams, Gareth, Andrew G. Nicholson, David Snead, et al.. (2019). Interobserver Reliability of Programmed Cell Death Ligand-1 Scoring Using the VENTANA PD-L1 (SP263) Assay in NSCLC. Journal of Thoracic Oncology. 15(4). 550–555. 16 indexed citations
10.
Dix, Carly I., Christopher J. Stubbs, Eileen McCall, et al.. (2019). Mapping the binding sites of antibodies utilized in programmed cell death ligand-1 predictive immunohistochemical assays for use with immuno-oncology therapies. Modern Pathology. 33(4). 518–530. 58 indexed citations
11.
Scott, Marietta, Sophie Wildsmith, M. Ratcliffe, et al.. (2018). Comparison of patient populations identified by different PD-L1 assays in head and neck squamous cell carcinoma (HNSCC). Annals of Oncology. 29. viii375–viii375. 9 indexed citations
12.
Brown, Heather A., Johan Vansteenkiste, Kazuhiko Nakagawa, et al.. (2018). MA15.03 PD-L1 Expression in Untreated EGFRm Advanced NSCLC and Response to Osimertinib and SoC EGFR-TKIs in the FLAURA Trial. Journal of Thoracic Oncology. 13(10). S408–S408. 3 indexed citations
13.
Zając, Magdalena, Anne-Marie Boothman, Yong Ben, et al.. (2018). Analytical Validation and Clinical Utility of an Immunohistochemical Programmed Death Ligand-1 Diagnostic Assay and Combined Tumor and Immune Cell Scoring Algorithm for Durvalumab in Urothelial Carcinoma. Archives of Pathology & Laboratory Medicine. 143(6). 722–731. 18 indexed citations
14.
Scorer, Paul, Marietta Scott, M. Ratcliffe, et al.. (2018). Consistency of tumor and immune cell programmed cell death ligand-1 expression within and between tumor blocks using the VENTANA SP263 assay. Diagnostic Pathology. 13(1). 47–47. 22 indexed citations
15.
Ratcliffe, M., Alan Sharpe, Anita Midha, et al.. (2017). Agreement between Programmed Cell Death Ligand-1 Diagnostic Assays across Multiple Protein Expression Cutoffs in Non–Small Cell Lung Cancer. Clinical Cancer Research. 23(14). 3585–3591. 237 indexed citations
16.
Vandenberghe, Michel E., et al.. (2017). Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Scientific Reports. 7(1). 45938–45938. 148 indexed citations
17.
Stokes, Michael E., Sophie Wildsmith, Maria Secrier, et al.. (2017). Relationship between PD-L1 expression and survival in head and neck squamous cell carcinoma (HNSCC) patients (pts). Annals of Oncology. 28. v375–v375. 5 indexed citations
18.
Brody, Robert S., Yiduo Zhang, Marc Ballas, et al.. (2017). PD-L1 expression in advanced NSCLC: Insights into risk stratification and treatment selection from a systematic literature review. Lung Cancer. 112. 200–215. 234 indexed citations
19.
Ratcliffe, M., Alan Sharpe, Craig Barker, et al.. (2017). Correction to: Abstracts. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 472(2). 301–301. 1 indexed citations
20.
Barker, Craig, et al.. (2009). Nondestructive Quality Control of HER2 Control Cell Line Sections. Applied immunohistochemistry & molecular morphology. 17(6). 536–542. 3 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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