Guillermo Lorenzo

1.4k total citations · 1 hit paper
29 papers, 861 citations indexed

About

Guillermo Lorenzo is a scholar working on Modeling and Simulation, Radiology, Nuclear Medicine and Imaging and Cancer Research. According to data from OpenAlex, Guillermo Lorenzo has authored 29 papers receiving a total of 861 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Modeling and Simulation, 9 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Cancer Research. Recurrent topics in Guillermo Lorenzo's work include Mathematical Biology Tumor Growth (14 papers), Prostate Cancer Diagnosis and Treatment (6 papers) and Cancer Genomics and Diagnostics (6 papers). Guillermo Lorenzo is often cited by papers focused on Mathematical Biology Tumor Growth (14 papers), Prostate Cancer Diagnosis and Treatment (6 papers) and Cancer Genomics and Diagnostics (6 papers). Guillermo Lorenzo collaborates with scholars based in United States, Italy and Spain. Guillermo Lorenzo's co-authors include Thomas J.R. Hughes, Thomas E. Yankeelov, Alessandro Reali, Héctor Gómez, David A. Hormuth, Alex Viguerie, Davide Baroli, Ferdinando Auricchio, Ernesto A. B. F. Lima and Alessandro Veneziani and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Cancer Research and Journal of Neurology Neurosurgery & Psychiatry.

In The Last Decade

Guillermo Lorenzo

28 papers receiving 851 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.

Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion.

2021 152 citations Alex Viguerie, Guillermo Lorenzo et al. PubMed profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Guillermo Lorenzo United States	16	355	209	131	109	94	29	861
N. Ayache France	7	139 0.4×	350 1.7×	33 0.3×	13 0.1×	85 0.9×	10	645
Jana Lipková United States	14	75 0.2×	662 3.2×	118 0.9×	38 0.3×	137 1.5×	30	1.4k
Graeme J. Pettet Australia	17	509 1.4×	23 0.1×	16 0.1×	89 0.8×	298 3.2×	47	1.1k
Rafel Bordas United Kingdom	14	99 0.3×	38 0.2×	165 1.3×	12 0.1×	149 1.6×	24	960
Regina C. Almeida Brazil	14	204 0.6×	46 0.2×	11 0.1×	29 0.3×	47 0.5×	42	624
Jianzhong Su China	19	17 0.0×	86 0.4×	257 2.0×	36 0.3×	95 1.0×	73	1.5k
Christopher J. Arthurs United Kingdom	13	65 0.2×	78 0.4×	242 1.8×	8 0.1×	141 1.5×	18	762
Georgios C. Manikis Greece	15	31 0.1×	489 2.3×	73 0.6×	11 0.1×	72 0.8×	65	795
Bindi S. Brook United Kingdom	19	40 0.1×	28 0.1×	303 2.3×	12 0.1×	133 1.4×	55	887
Muhammad Khalid Khan Niazi United States	19	17 0.0×	604 2.9×	130 1.0×	42 0.4×	124 1.3×	86	1.6k

Countries citing papers authored by Guillermo Lorenzo

Since Specialization

Citations

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

Fields of papers citing papers by Guillermo Lorenzo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guillermo Lorenzo

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

Wu, Chengyue, David A. Hormuth, Ernesto A. B. F. Lima, et al.. (2025). A critical assessment of artificial intelligence in magnetic resonance imaging of cancer. PubMed. 3(1). 15–15.

Lorenzo, Guillermo, Michael A. Liss, Michael I. Miga, et al.. (2024). A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. Cancer Research Communications. 4(3). 617–633. 6 indexed citations

Beretta, Elena, et al.. (2024). Mathematical Analysis of a Model-Constrained Inverse Problem For the Reconstruction of Early States of Prostate Cancer Growth. SIAM Journal on Applied Mathematics. 84(5). 2000–2027. 2 indexed citations

Lima, Ernesto A. B. F., et al.. (2024). A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin. Cancer Biology & Therapy. 25(1). 2321769–2321769. 11 indexed citations

Lorenzo, Guillermo, David A. Hormuth, Jayashree Kalpathy–Cramer, et al.. (2024). Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data. Annual Review of Biomedical Engineering. 26(1). 529–560. 25 indexed citations

Lorenzo, Guillermo, Angela M. Jarrett, Christian T. Meyer, et al.. (2023). A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data. Engineering With Computers. 40(3). 1469–1499. 3 indexed citations

Yang, Emily Y., et al.. (2023). Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States. Engineering With Computers. 40(2). 813–837. 1 indexed citations

Chaudhuri, Anirban, David A. Hormuth, Guillermo Lorenzo, et al.. (2023). Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. Frontiers in Artificial Intelligence. 6. 1222612–1222612. 41 indexed citations

Yankeelov, Thomas E., David A. Hormuth, Ernesto A. B. F. Lima, et al.. (2023). Designing clinical trials for patients who are not average. iScience. 27(1). 108589–108589. 18 indexed citations

10.

Wu, Chengyue, Guillermo Lorenzo, David A. Hormuth, et al.. (2022). Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. PubMed. 3(2). 21304–21304. 68 indexed citations

11.

Lorenzo, Guillermo, Michael A. Liss, Michael I. Miga, et al.. (2022). Abstract 5064: Patient-specific forecasting of prostate cancer growth during active surveillance using an imaging-informed mechanistic model. Cancer Research. 82(12_Supplement). 5064–5064. 3 indexed citations

12.

Lorenzo, Guillermo, N. Di Muzio, Chiara Lucrezia Deantoni, et al.. (2022). Patient-specific forecasting of postradiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse. iScience. 25(11). 105430–105430. 6 indexed citations

13.

Hormuth, David A., Chengyue Wu, Ernesto A. B. F. Lima, et al.. (2021). Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers. 13(12). 3008–3008. 37 indexed citations

14.

Hormuth, David A., Angela M. Jarrett, Guillermo Lorenzo, et al.. (2021). Math, magnets, and medicine: enabling personalized oncology. Expert Review of Precision Medicine and Drug Development. 6(2). 79–81. 20 indexed citations

15.

Wu, Chengyue, David A. Hormuth, Todd Oliver, et al.. (2020). Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE Transactions on Medical Imaging. 39(9). 2760–2771. 24 indexed citations

16.

Hormuth, David A., Angela M. Jarrett, Kaitlyn E. Johnson, et al.. (2020). Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology. iScience. 23(12). 101807–101807. 27 indexed citations

17.

Viguerie, Alex, Alessandro Veneziani, Guillermo Lorenzo, et al.. (2020). Diffusion–reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study. Computational Mechanics. 66(5). 1131–1152. 62 indexed citations

18.

Lorenzo, Guillermo, Victor M. Pérez-Garcı́a, Alfonso Mariño, et al.. (2019). Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome. Journal of The Royal Society Interface. 16(157). 20190195–20190195. 18 indexed citations

19.

Lorenzo, Guillermo, Michael A. Scott, Kevin Tew, et al.. (2016). Tissue-scale, personalized modeling and simulation of prostate cancer growth. Proceedings of the National Academy of Sciences. 113(48). E7663–E7671. 64 indexed citations

20.

Starkstein, Sergio, L. Sabe, Silvia Vázquez, et al.. (1997). Neuropsychological, psychiatric, and cerebral perfusion correlates of leukoaraiosis in Alzheimer's disease. Journal of Neurology Neurosurgery & Psychiatry. 63(1). 66–73. 73 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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