Chris McIntosh

2.9k total citations
97 papers, 1.4k citations indexed

About

Chris McIntosh is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Social Psychology. According to data from OpenAlex, Chris McIntosh has authored 97 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Pulmonary and Respiratory Medicine and 13 papers in Social Psychology. Recurrent topics in Chris McIntosh's work include Radiomics and Machine Learning in Medical Imaging (19 papers), Advanced Radiotherapy Techniques (12 papers) and Medical Image Segmentation Techniques (11 papers). Chris McIntosh is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), Advanced Radiotherapy Techniques (12 papers) and Medical Image Segmentation Techniques (11 papers). Chris McIntosh collaborates with scholars based in Canada, United States and Netherlands. Chris McIntosh's co-authors include Thomas G. Purdie, Ghassan Hamarneh, Mattea Welch, John E. Phillips, Neil Audsley, David A. Jaffray, Andrea McNiven, Benjamin Haibe‐Kains, Leonard Wee and Shao Hui Huang and has published in prestigious journals such as Circulation, Nature Medicine and Journal of Clinical Oncology.

In The Last Decade

Chris McIntosh

82 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chris McIntosh Canada 19 675 362 300 255 201 97 1.4k
Matthew McAuliffe United States 20 537 0.8× 86 0.2× 189 0.6× 424 1.7× 134 0.7× 51 2.0k
Pascal O. Zinn United States 29 1.1k 1.6× 20 0.1× 394 1.3× 245 1.0× 116 0.6× 111 2.6k
Andrew D. Trister United States 20 427 0.6× 28 0.1× 118 0.4× 113 0.4× 359 1.8× 32 1.7k
Yimei Li United States 21 489 0.7× 151 0.4× 272 0.9× 74 0.3× 57 0.3× 115 1.6k
Christian Graff United States 17 643 1.0× 74 0.2× 291 1.0× 319 1.3× 166 0.8× 43 1.0k
Maria João Cardoso Portugal 33 754 1.1× 75 0.2× 511 1.7× 142 0.6× 960 4.8× 124 4.2k
Sanjeev Chawla United States 33 1.9k 2.8× 15 0.0× 244 0.8× 193 0.8× 232 1.2× 116 3.7k
Philippe Schucht Switzerland 28 705 1.0× 19 0.1× 291 1.0× 455 1.8× 44 0.2× 109 2.5k
Marc E. Miquel United Kingdom 22 861 1.3× 147 0.4× 336 1.1× 146 0.6× 86 0.4× 69 1.7k
Diego Ardila United States 4 839 1.2× 14 0.0× 481 1.6× 179 0.7× 489 2.4× 6 1.6k

Countries citing papers authored by Chris McIntosh

Since Specialization
Citations

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

Fields of papers citing papers by Chris McIntosh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chris McIntosh

This figure shows the co-authorship network connecting the top 25 collaborators of Chris McIntosh. A scholar is included among the top collaborators of Chris McIntosh 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 Chris McIntosh. Chris McIntosh 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
1.
Patel, Tirth, Mattea Welch, Xiang Y. Ye, et al.. (2025). Association of artificial intelligence-screened interstitial lung disease with radiation pneumonitis in locally advanced non-small cell lung cancer. Radiotherapy and Oncology. 212. 111144–111144. 1 indexed citations
2.
Moayedi, Yasbanoo, Farid Foroutan, Ben Kim, et al.. (2025). Developments in Digital Wearable in Heart Failure and the Rationale for the Design of TRUE-HF (Ted Rogers Understanding of Exacerbations in Heart Failure) Apple CPET Study. Circulation Heart Failure. 18(6). e012204–e012204. 2 indexed citations
3.
4.
Mishra, Jyoti, et al.. (2025). Cancer Genomic Alterations and Microenvironmental Features Encode Synergistic Interactions with Disease Outcomes. Molecular Cancer Research. 23(12). 971–983.
5.
Namdar, Khashayar, S. Carey, Sandra E. Fischer, et al.. (2025). Non-invasive liver fibrosis screening on CT images using radiomics. BMC Medical Imaging. 25(1). 285–285. 1 indexed citations
6.
Woolman, Michael, Taira Kiyota, Vijay Ramaswamy, et al.. (2024). Lipidomic-Based Approach to 10 s Classification of Major Pediatric Brain Cancer Types with Picosecond Infrared Laser Mass Spectrometry. Analytical Chemistry. 96(3). 1019–1028. 7 indexed citations
7.
8.
Chong, Jaron, et al.. (2024). External Validation of an Artificial Intelligence Screening Tool for Interstitial Lung Disease in Patients Receiving Lung Stereotactic Ablative Radiotherapy. International Journal of Radiation Oncology*Biology*Physics. 120(2). e657–e658. 1 indexed citations
9.
Gao, Yuan, et al.. (2024). Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence. Canadian Journal of Cardiology. 40(10). 1934–1945. 16 indexed citations
10.
Winter, Jeff D., et al.. (2024). Machine learning automated treatment planning for online magnetic resonance guided adaptive radiotherapy of prostate cancer. Physics and Imaging in Radiation Oncology. 32. 100649–100649.
11.
Welch, Mattea, Chris McIntosh, Shao Hui Huang, et al.. (2023). Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics. Cancer Research Communications. 3(6). 1140–1151. 18 indexed citations
12.
Winter, Jeff D., et al.. (2022). Domain adaptation of automated treatment planning from computed tomography to magnetic resonance. Physics in Medicine and Biology. 67(12). 125010–125010. 2 indexed citations
13.
Conroy, Leigh, et al.. (2021). Performance stability evaluation of atlas-based machine learning radiation therapy treatment planning in prostate cancer. Physics in Medicine and Biology. 66(13). 134001–134001. 3 indexed citations
14.
Foroutan, Farid, Juan Duero Posada, Michael E. Farkouh, et al.. (2021). Remote Mobile Outpatient Monitoring in Transplant (Reboot) 2.0: Protocol for a Randomized Controlled Trial. JMIR Research Protocols. 10(10). e26816–e26816. 2 indexed citations
15.
Tsang, Derek S., Chris McIntosh, Thomas G. Purdie, et al.. (2020). Automated Machine-Learning Radiation Therapy Treatment Planning for Pediatric and Adult Brain Tumors. International Journal of Radiation Oncology*Biology*Physics. 108(3). e777–e777. 2 indexed citations
16.
McIntosh, Chris, Mattea Welch, Andrea McNiven, David A. Jaffray, & Thomas G. Purdie. (2017). Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method. Physics in Medicine and Biology. 62(15). 5926–5944. 141 indexed citations
17.
Drescher, Jack, Alan Schwartz, Chris McIntosh, et al.. (2016). The Growing Regulation of Conversion Therapy. Journal of Medical Regulation. 102(2). 7–12. 51 indexed citations
18.
Sole, Claudio V., et al.. (2015). Applying a Real Time Pretreatment Review of Radiation Oncology Breast Cancer Rounds: Automated Quality Assurance Results. International Journal of Radiation Oncology*Biology*Physics. 93(3). E586–E586.
19.
McIntosh, Chris & Ghassan Hamarneh. (2013). Medical Image Segmentation: Energy Minimization and Deformable Models (Chapter 23). 1 indexed citations
20.
McIntosh, Chris & Ghassan Hamarneh. (2006). Genetic algorithm driven statistically deformed models for medical image segmentation. Genetic and Evolutionary Computation Conference. 14 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 Matthew McAuliffe Breakdown of academic impact, for papers by Pascal O. Zinn Breakdown of academic impact, for papers by Andrew D. Trister Breakdown of academic impact, for papers by Yimei Li Breakdown of academic impact, for papers by Christian Graff Breakdown of academic impact, for papers by Maria João Cardoso Breakdown of academic impact, for papers by Sanjeev Chawla Breakdown of academic impact, for papers by Philippe Schucht Breakdown of academic impact, for papers by Marc E. Miquel Breakdown of academic impact, for papers by Diego Ardila
Rankless by CCL
2026