Michael D. Chan

9.6k total citations · 2 hit papers
249 papers, 6.8k citations indexed

About

Michael D. Chan is a scholar working on Pulmonary and Respiratory Medicine, Genetics and Epidemiology. According to data from OpenAlex, Michael D. Chan has authored 249 papers receiving a total of 6.8k indexed citations (citations by other indexed papers that have themselves been cited), including 140 papers in Pulmonary and Respiratory Medicine, 111 papers in Genetics and 66 papers in Epidemiology. Recurrent topics in Michael D. Chan's work include Brain Metastases and Treatment (126 papers), Glioma Diagnosis and Treatment (111 papers) and Meningioma and schwannoma management (59 papers). Michael D. Chan is often cited by papers focused on Brain Metastases and Treatment (126 papers), Glioma Diagnosis and Treatment (111 papers) and Meningioma and schwannoma management (59 papers). Michael D. Chan collaborates with scholars based in United States, Canada and China. Michael D. Chan's co-authors include Stephen B. Tatter, Edward G. Shaw, Hui‐Wen Lo, Ann M. Peiffer, Shadi Qasem, Adrian W. Laxton, Mike E. Robbins, Dana Greene-Schloesser, Tadas Rimkus and Thomas L. Ellis and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and The Journal of Experimental Medicine.

In The Last Decade

Michael D. Chan

234 papers receiving 6.7k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Radiation-induced brain injury: A review

2012 511 citations Edward G. Shaw, Michael D. Chan et al. profile →
Targeting the Sonic Hedgehog Signaling Pathway: Review of Smoothened and GLI Inhibitors

2016 459 citations Tadas Rimkus, Shadi Qasem et al. Cancers profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Michael D. Chan	3.1k	2.4k	1.8k	1.6k	1.5k	249	6.8k
Steven N. Kalkanis	2.3k 0.7×	2.6k 1.1×	1.4k 0.8×	1.1k 0.7×	1.3k 0.9×	132	5.5k
Émilie Le Rhun	4.5k 1.5×	3.6k 1.5×	3.4k 1.9×	1.1k 0.7×	1.3k 0.9×	237	7.8k
John L. Villano	1.2k 0.4×	2.6k 1.1×	819 0.5×	1.1k 0.7×	869 0.6×	141	5.0k
Yoshitaka Narita	1.2k 0.4×	2.6k 1.1×	1.3k 0.7×	2.0k 1.3×	594 0.4×	234	5.6k
Gheeyoung Choe	2.1k 0.7×	878 0.4×	1.4k 0.8×	1.3k 0.8×	827 0.6×	210	5.5k
Roland Goldbrunner	1.2k 0.4×	2.6k 1.1×	1.3k 0.7×	1.9k 1.2×	1.8k 1.2×	242	7.4k
Steven Brem	1.3k 0.4×	2.8k 1.2×	1.5k 0.8×	2.9k 1.8×	922 0.6×	193	7.7k
William R. Shapiro	2.8k 0.9×	4.7k 2.0×	1.7k 1.0×	1.8k 1.1×	1.1k 0.8×	137	8.9k
Gelareh Zadeh	1.4k 0.4×	2.8k 1.2×	1.1k 0.6×	2.2k 1.4×	2.2k 1.5×	328	7.9k
Elisabeth J. Rushing	1.1k 0.4×	2.6k 1.1×	910 0.5×	1.9k 1.2×	1.7k 1.2×	235	7.1k

Countries citing papers authored by Michael D. Chan

Since Specialization

Citations

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

Fields of papers citing papers by Michael D. Chan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael D. Chan. 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 Michael D. Chan. The network helps show where Michael D. Chan may publish in the future.

Co-authorship network of co-authors of Michael D. Chan

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

All Works

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20 of 20 papers shown

Xing, Fei, et al.. (2024). Exposed Phosphatidylserine as a Biomarker for Clear Identification of Breast Cancer Brain Metastases in Mouse Models. Cancers. 16(17). 3088–3088. 1 indexed citations

Smith, Eleanor, Carol Kittel, Marc Benayoun, et al.. (2024). Fractionated Radiotherapy With Stereotactic Radiosurgery Boost Controls Gross Disease in Grade 2 Meningioma. World Neurosurgery. 194. 123429–123429.

Cramer, Christina K., Scott Isom, Michael McCormack, et al.. (2024). Brain metastases from renal cell carcinoma: Effects of novel systemic agents on brain metastasis outcomes. Clinical Neurology and Neurosurgery. 238. 108191–108191. 1 indexed citations

Russell, Gregory B., M.C. LeCompte, Weiren Liu, et al.. (2024). Multi-institutional study of reirradiation for recurrent high grade glioma.. Journal of Clinical Oncology. 42(16_suppl). 2063–2063.

Qian, Xiaohua, Hua Tan, Weiling Zhao, et al.. (2024). Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance. Genes. 15(6). 718–718. 3 indexed citations

D’Agostino, Ralph B., Jimmy Ruiz, Lance D. Miller, et al.. (2024). Targeting NTRK1 Enhances Immune Checkpoint Inhibitor Efficacy in NTRK1 Wild-Type Non–Small Cell Lung Cancer. Cancer Research. 84(23). 4002–4016. 4 indexed citations

Hughes, Ryan T., N. Razavian, Anna C. Snavely, et al.. (2024). “If You’re Talking, I Think You’re Muted”: Follow-up Analysis of Weekly Peer Review Discussion and Plan Changes After Transitioning From Virtual to In-Person Format. Clinical Oncology. 36(12). 809–816.

D’Agostino, Ralph B., Steven E. Glynn, M. D. Smith, et al.. (2024). Genomic Signature for Leptomeningeal Progression in Brain Metastasis Patients Using Liquid Biopsy-Acquired Comprehensive Genomic Profiling. International Journal of Radiation Oncology*Biology*Physics. 120(2). S190–S191.

Farris, Michael, N. Razavian, Ryan T. Hughes, et al.. (2023). Bridging the Communication Gaps: A Prospective Single-Arm Pilot Study Testing the Feasibility of Interdisciplinary Radiotherapy Planning in Locally Advanced Lung Cancer. Academic Radiology. 30(11). 2566–2573. 3 indexed citations

10.

Chen, Xuguang, Vishwa S. Parekh, Luke Peng, et al.. (2021). Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery. Neuro-Oncology Advances. 3(1). vdab150–vdab150. 14 indexed citations

11.

Chen, X., Vishwa S. Parekh, Luke Peng, et al.. (2020). External Validation Of A Radiomics-Based Machine Learning Model For Distinguishing Radiation Necrosis From Progression Of Brain Metastases Treated With Stereotactic Radiosurgery. International Journal of Radiation Oncology*Biology*Physics. 108(3). e722–e723. 2 indexed citations

12.

LeCompte, M.C., Ryan T. Hughes, Michael Farris, et al.. (2020). Impact of brain metastasis velocity on neurologic death for brain metastasis patients experiencing distant brain failure after initial stereotactic radiosurgery. Journal of Neuro-Oncology. 146(2). 285–292. 8 indexed citations

13.

Wu, Shih-Ying, Fei Xing, Sambad Sharma, et al.. (2020). Nicotine promotes brain metastasis by polarizing microglia and suppressing innate immune function. The Journal of Experimental Medicine. 217(8). 77 indexed citations

14.

Rimkus, Tadas, Richard L. Carpenter, Sherona Sirkisoon, et al.. (2018). Truncated Glioma-Associated Oncogene Homolog 1 (tGLI1) Mediates Mesenchymal Glioblastoma via Transcriptional Activation of CD44. Cancer Research. 78(10). 2589–2600. 32 indexed citations

15.

Xing, Fei, Yin Liu, Shih-Ying Wu, et al.. (2018). Loss of XIST in Breast Cancer Activates MSN-c-Met and Reprograms Microglia via Exosomal miRNA to Promote Brain Metastasis. Cancer Research. 78(15). 4316–4330. 259 indexed citations

16.

Farris, Michael, E. McTyre, Christina K. Cramer, et al.. (2017). Brain Metastasis Velocity: A Novel Prognostic Metric Predictive of Overall Survival and Freedom From Whole-Brain Radiation Therapy After Distant Brain Failure Following Upfront Radiosurgery Alone. International Journal of Radiation Oncology*Biology*Physics. 98(1). 131–141. 75 indexed citations

17.

Xing, Fei, Yin Liu, Sambad Sharma, et al.. (2016). Activation of the c-Met Pathway Mobilizes an Inflammatory Network in the Brain Microenvironment to Promote Brain Metastasis of Breast Cancer. Cancer Research. 76(17). 4970–4980. 102 indexed citations

18.

McTyre, E., D.N. Ayala-Peacock, Joseph N. Contessa, et al.. (2016). Multi-Institutional Competing Risks Analysis Predicting Distant Brain Failure and Salvage Patterns After Upfront Radiosurgery Without Whole-Brain Radiation Therapy for Brain Metastasis. International Journal of Radiation Oncology*Biology*Physics. 96(2). S180–S181. 1 indexed citations

19.

Lucas, John T., Christopher M. Läck, Benjamin Wagner, et al.. (2015). A Comprehensive Assessment of Radiographic, Clinical, and Treatment Related Risk Factors for Metastasis Related Intracranial Hemorrhage in Metastatic Melanoma Following Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics. 93(3). E111–E111. 2 indexed citations

20.

Okada, Hideho, Lisa H. Butterfield, Ronald L. Hamilton, et al.. (2014). Induction of Robust Type-I CD8+ T-cell Responses in WHO Grade 2 Low-Grade Glioma Patients Receiving Peptide-Based Vaccines in Combination with Poly-ICLC. Clinical Cancer Research. 21(2). 286–294. 83 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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