Pamela Jackson

2.0k total citations · 1 hit paper
26 papers, 1.1k citations indexed

About

Pamela Jackson is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Modeling and Simulation. According to data from OpenAlex, Pamela Jackson has authored 26 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Radiology, Nuclear Medicine and Imaging, 12 papers in Genetics and 6 papers in Modeling and Simulation. Recurrent topics in Pamela Jackson's work include Radiomics and Machine Learning in Medical Imaging (12 papers), Glioma Diagnosis and Treatment (12 papers) and MRI in cancer diagnosis (9 papers). Pamela Jackson is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (12 papers), Glioma Diagnosis and Treatment (12 papers) and MRI in cancer diagnosis (9 papers). Pamela Jackson collaborates with scholars based in United States, New Zealand and South Korea. Pamela Jackson's co-authors include Andrew Smith, Jan O. Cowan, D. Robin Taylor, Kristin R. Swanson, Andrea Hawkins‐Daarud, Leili Shahriyari, Russell C. Rockne, Qi Wang, Jun Deng and Huanmei Wu and has published in prestigious journals such as PLoS ONE, American Journal of Respiratory and Critical Care Medicine and Scientific Reports.

In The Last Decade

Pamela Jackson

26 papers receiving 1.0k citations

Hit Papers

align trajectories

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.

Digital twins for health: a scoping review

2024 220 citations Evangelia Katsoulakis, Qi Wang et al. npj Digital Medicine profile →

Peers

Countries citing papers authored by Pamela Jackson

Since Specialization

Citations

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

Fields of papers citing papers by Pamela Jackson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pamela Jackson

This figure shows the co-authorship network connecting the top 25 collaborators of Pamela Jackson. A scholar is included among the top collaborators of Pamela Jackson 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 Pamela Jackson. Pamela Jackson 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

#	Work	Indexed citations
1	Digital twins for health: a scoping review breakdown → 2024 ·npj Digital Medicine ·Evangelia Katsoulakis, Qi Wang, Huanmei Wu, Leili Shahriyari, R. Fletcher, Jinwei Liu, Luke E. K. Achenie, Hongfang Liu, Pamela Jackson, Ying Xiao, Tanveer Syeda-Mahmood, Richard Tuli, Jun Deng	220
2	Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma 2024 ·npj Digital Medicine ·(unknown), Michael Argenziano, Hyunsoo Yoon, Deborah Boyett, Akshay Save, Petros Petridis, (unknown), Pamela Jackson, Andrea Hawkins‐Daarud, Nhan L. Tran, Leland Hu, Kyle Singleton, (unknown), Osama Al‐Dalahmah, Jeffrey N. Bruce, Jack Grinband, Kristin R. Swanson, Peter Canoll, Jing Li	2
3	A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration 2021 ·Journal of Personalized Medicine ·(unknown), (unknown), (unknown), (unknown), Mohamed H. Abdel‐Rahman, Alireza Asadpoure, Colleen M. Cebulla, Young Hwan Chang, Wenrui Hao, Pamela Jackson, Adrian V. Lee, Daniel G. Stover, Zuzana Tatárová, Ioannis K. Zervantonakis, Leili Shahriyari	23
4	Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma 2020 ·PLoS ONE ·(unknown), (unknown), Paula Whitmire, (unknown), Sandra K. Johnston, Kyle Singleton, Pamela Jackson, Andrea Hawkins‐Daarud, Bernard R. Bendok, Alyx B. Porter, Sujay A. Vora, Jann N. Sarkaria, Leland Hu, Maciej M. Mrugała, Kristin R. Swanson	8
5	Improved model prediction of glioma growth utilizing tissue-specific boundary effects 2019 ·Mathematical Biosciences ·(unknown), Russell C. Rockne, Andrea Hawkins‐Daarud, Pamela Jackson, Sandra K. Johnston, Paul E. Kinahan, Kristin R. Swanson	15
6	NIMG-50. DOES THE PRESENCE OF GADOLINIUM AFFECT T2 –WEIGHTED IMAGES OF GLIOMA?: IMPLICATIONS FOR MRI PROTOCOL STANDARDIZATION 2019 ·Neuro-Oncology ·Pamela Jackson, Yuxiang Zhou, Joseph M. Hoxworth, Kristin R. Swanson, Leland Hu	1
7	A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type 2019 ·Journal of Digital Imaging ·Sara Ranjbar, Kyle Singleton, Pamela Jackson, (unknown), (unknown), (unknown), J. Ross Mitchell, Kristin R. Swanson, Leland Hu	20
8	Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme 2019 ·Tomography ·Olya Stringfield, John A. Arrington, Sandra K. Johnston, N. Rognin, Noah C. Peeri, Yoganand Balagurunathan, Pamela Jackson, (unknown), Kristin R. Swanson, Kathleen M. Egan, Robert A. Gatenby, Natarajan Raghunand	30
9	Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI 2019 ·Scientific Reports ·Nathan Gaw, Andrea Hawkins‐Daarud, Leland Hu, Hyunsoo Yoon, Lujia Wang, Yanzhe Xu, Pamela Jackson, Kyle Singleton, Leslie C. Baxter, Jennifer Eschbacher, (unknown), (unknown), Kris A. Smith, Peter Nakaji, J. Ross Mitchell, Teresa Wu, Kristin R. Swanson, Jing Li	62
10	ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma 2019 ·American Journal of Clinical Oncology ·Sandra K. Johnston, Paula Whitmire, (unknown), Priya Kumthekar, Alyx B. Porter, Natarajan Raghunand, Luis F. Gonzalez‐Cuyar, Maciej M. Mrugała, Andrea Hawkins‐Daarud, Pamela Jackson, Leland Hu, Jann N. Sarkaria, Lei Wang, Robert A. Gatenby, Kathleen M. Egan, Peter Canoll, Kristin R. Swanson	3
11	TMOD-07. LOCALIZATION OF ERLOTONIB RELATIVE TO MRI-BASED TUMOR EXTENT IN PDX GLIOBLASTOMA MODEL: TOWARDS A MATHEMATICAL MODEL FOR THE INTERFACE BETWEEN MRI AND DRUG DISTRIBUTION 2018 ·Neuro-Oncology ·Pamela Jackson, Sara Ranjbar, Elizabeth C. Randall, Michael S. Regan, Walid M. Abdelmoula, Begoña Giménez, (unknown), Leland Hu, Lihong He, Slobodan Macura, Jeffrey N. Agar, Jann N. Sarkaria, Nathalie Y.R. Agar, Kristin R. Swanson	1
12	Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival 2018 ·JCO Clinical Cancer Informatics ·(unknown), Fillan S. Grady, Gustavo De León, Russell C. Rockne, Eduardo Carrasco, Pamela Jackson, (unknown), Sandra K. Johnston, Andrea Hawkins‐Daarud, (unknown), (unknown), Mauricio E. Gamez, Alyx B. Porter, Leland Hu, Luis F. Gonzalez‐Cuyar, Bernard R. Bendok, Sujay A. Vora, Kristin R. Swanson	14
13	NIMG-74. RADIOMICS OF TUMOR INVASION 2.0: COMBINING MECHANISTIC TUMOR INVASION MODELS WITH MACHINE LEARNING MODELS TO ACCURATELY PREDICT TUMOR INVASION IN HUMAN GLIOBLASTOMA PATIENTS 2017 ·Neuro-Oncology ·Kristin R. Swanson, Nathan Gaw, Andrea Hawkins‐Daarud, Pamela Jackson, Kyle Singleton, (unknown), Jennifer Eschbacher, Leslie C. Baxter, Kris A. Smith, Peter Nakaji, Samuel McGee, (unknown), Bernard R. Bendok, Amylou C. Dueck, Teresa Wu, Jing Li, Leland Hu	1
14	Patient-Specific Mathematical Neuro-Oncology: Using a Simple Proliferation and Invasion Tumor Model to Inform Clinical Practice 2015 ·Bulletin of Mathematical Biology ·Pamela Jackson, Joseph Juliano, Andrea Hawkins‐Daarud, Russell C. Rockne, Kristin R. Swanson	74
15	BM-14 * A PARADIGM SHIFT IN DETERMINING BRAIN METASTASIS TREATMENT: DOES NUMBER REALLY MATTER? 2014 ·Neuro-Oncology ·Pamela Jackson, Fillan S. Grady, (unknown), (unknown), (unknown), Russell C. Rockne, Carly A. Bridge, Maryanne H. Marymont, James R. Chandler, Kristin R. Swanson, Priya Kumthekar	1
16	Real-time visualization and characterization of liposomal delivery into the monkey brain by magnetic resonance imaging 2005 ·Brain Research Protocols ·Michal T. Krauze, Tracy R. McKnight, Yoji Yamashita, John R. Bringas, Charles O. Noble, Ryuta Saito, Karsten Geletneky, John Forsayeth, Mitchel S. Berger, Pamela Jackson, John W. Park, Krystof S. Bankiewicz	77
17	Gadolinium-loaded liposomes allow for real-time magnetic resonance imaging of convection-enhanced delivery in the primate brain 2005 ·Experimental Neurology ·Ryuta Saito, Michal T. Krauze, John R. Bringas, Charles O. Noble, Tracy R. McKnight, Pamela Jackson, Michael F. Wendland, Christoph Mamot, Daryl C. Drummond, Dmitri B. Kirpotin, Keelung Hong, Mitchel S. Berger, John W. Park, Krystof S. Bankiewicz	120
18	Diagnosing Asthma 2003 ·American Journal of Respiratory and Critical Care Medicine ·Andrew Smith, Jan O. Cowan, (unknown), (unknown), (unknown), Pamela Jackson, D. Robin Taylor	370
19	Thyroid carcinoma in a cat 1984 ·Veterinary Record ·Philip J. Cowen, Pamela Jackson	1
20	Diabetic Retinopathy in Chronic Pancreatitis 1971 ·Archives of Ophthalmology ·D Sevel, (unknown), S Bank, I. N. Marks, Pamela Jackson	19

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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