Patient-specific, imaging-based forecasting of prostate cancer growth

Prostate cancer is a major health problem among ageing men worldwide. The current clinical management of this pathology enables its detection at early organ-confined stages by combining regular screening and patient classification in risk groups. 

However, the limited individualization of the clinical management beyond risk-group definition has led to significant overtreatment and undertreatment rates, which might adversely impact the patients’ lives and life expectancy, respectively. Thus, prostate cancer is a paradigmatic disease in which an individualized predictive technology could make a crucial difference in clinical practice, thereby separating less aggressive tumors that could be safely monitored from lethal tumors that require immediate treatment. 

To address this critical need, I leverage routine patient-specific clinical and imaging data to construct and parameterize personalized mathematical models of prostate cancer growth, with which I can perform computational forecasts of the patient's tumor prognosis to improve diagnosis and clinical decision-making on a patient-specific basis. 

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 838786.
Figure. Organ-scale, patient-specific prediction of prostate cancer growth. First, we obtain the imaging and clinical data from the patient. Then, a 3D geometric model of the prostate is created from the organ segmentation in anatomic magnetic resonance images. The tumor segmentation is projected over this geometric model. Finally, we run a personalized simulation of prostate cancer growth, estimating the parameters in the model equations from the patient’s longitudinal clinical and imaging data.


A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

Guillermo Lorenzo, Jon S Heiselman, Michael A Liss, Michael I Miga, Hector Gomez, Thomas E Yankeelov, Alessandro Reali, Thomas JR Hughes

Cancer Research Communications, vol. 4, 2024, pp. 617--633

Designing clinical trials for patients who are not average

Thomas E Yankeelov, David A Hormuth, Ernesto ABF Lima, Guillermo Lorenzo, Chengyue Wu, Lois C Okereke, Gaiane M Rauch, Aradhana M Venkatesan, Caroline Chung

Iscience, vol. 27, 2024, p. 108589

Integrating mechanism-based modeling with biomedical imaging to build digital twins for clinical oncology

Chengyue Wu, Guillermo Lorenzo, David A. Hormuth II, Ernesto A.B.F. Lima, Kalina P. Slavkova, Julie C. DiCarlo, John Virotsko, Caleb M. Phillips, Debra Patt, Caroline Chung, Thomas E. Yankeelov

Biophysics Review, vol. 3(2), 2022, p. 021304

Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

Guillermo Lorenzo, David A Hormuth II, Angela M Jarrett, Ernesto ABF Lima, Shashank Subramanian, George Biros, J Tinsley Oden, Thomas JR Hughes, Thomas E Yankeelov

In: Igor Balaz, Andrew Adamatzky, Cancer, Complexity, Computation, Springer, 2022, pp. 55-97

Chapter Six - Oncology and mechanics: Landmark studies and promising clinical applications

Stéphane Urcun, Guillermo Lorenzo, Davide Baroli, Pierre-Yves Rohan, Giuseppe Sciumè, Wafa Skalli, Vincent Lubrano, Stéphane P.A. Bordas

In: S.P.A. Bordas, Advances in Applied Mechanics, vol. 55, Elsevier, 2022, pp. 513-571

Data-Driven Simulation of Fisher-Kolmogorov Tumor Growth Models Using Dynamic Mode Decomposition

Alex Viguerie, Malú Grave, Gabriel F. Barros, Guillermo Lorenzo, Alessandro Reali, Alvaro Coutinho

Journal of Biomechanical Engineering, vol. 144(12), 2022, p. 121001

Math, magnets, and medicine: enabling personalized oncology

David A. Hormuth, Angela M. Jarrett, Guillermo Lorenzo, Ernesto A. B. F. Lima, Chengyue Wu, Caroline Chung, Debra Patt, Thomas E. Yankeelov

Expert Review of Precision Medicine and Drug Development, vol. 6(2), 2021, pp. 79-81

A numerical simulation study of the dual role of 5α-reductase inhibitors on tumor growth in prostates enlarged by benign prostatic hyperplasia via stress relaxation and apoptosis upregulation

Guillermo Lorenzo, Thomas J.R. Hughes, Alessandro Reali, Hector Gomez

Computer Methods in Applied Mechanics and Engineering, vol. 362, 2020, p. 112843

Computer simulations suggest that prostate enlargement due to benign prostatic hyperplasia mechanically impedes prostate cancer growth

Guillermo Lorenzo, Thomas J. R. Hughes, Pablo Dominguez-Frojan, Alessandro Reali, Hector Gomez

Proceedings of the National Academy of Sciences of the United States of America, vol. 116(4), 2019, pp. 1152-1161

Hierarchically refined and coarsened splines for moving interface problems, with particular application to phase-field models of prostate tumor growth

Guillermo Lorenzo, Michael A. Scott, Kevin Tew, Thomas J.R. Hughes, Hector Gomez

Computer Methods in Applied Mechanics and Engineering, vol. 319, 2017, pp. 515-548

Tissue-scale, personalized modeling and simulation of prostate cancer growth

Guillermo Lorenzo, Michael A. Scott, Kevin Tew, Thomas J. R. Hughes, Yongjie Jessica Zhang, Lei Liu, Guillermo Vilanova, Hector Gomez

Proceedings of the National Academy of Sciences of the United States of America, vol. 113(48), 2016, pp. E7663-E7671


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