Guillermo Lorenzo GómezUniversity of Texas at Austin | UT · Oden Institute for Computational Engineering and Sciences
Guillermo Lorenzo Gómez
Doctor of Engineering
About
36
Publications
5,846
Reads
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597
Citations
Citations since 2017
Introduction
I am a Marie Skłodowska-Curie Fellow at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin (US) and the University of Pavia (Italy). My investigation focuses on the development of mathematical models and computational methods to predict cancer growth. I am working on the personalized modelling of prostate and breast cancer growth based on currently available clinical and imaging data.
Additional affiliations
September 2019 - August 2020
Position
- PostDoc Position
Description
- Development of image-based, personalized computational technologies to predict the growth of breast cancer during neoadjuvant chemotherapy and untreated prostate cancer.
October 2017 - August 2019
Position
- Researcher
Description
- Development of image-based, isogeometric computational methods to predict the growth and tumor-induced deformation of prostate cancer on a tissue-scale, patient-specific basis.
September 2013 - September 2017
Position
- Researcher
Description
- Study and development of new computational methods and techniques based on isogeometric analysis in order to model and simulate cancer growth, in particular, in the human prostate.
Education
November 2014 - June 2018
October 2013 - July 2014
September 2007 - July 2013
Publications
Publications (36)
Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally advanced triple negative breast cancer (TNBC) before surgery. The early assessment of TNBC response to NAC would enable an oncologist to adapt the therapeutic plan of a non-responding patient, thereby improving treatment outcomes while preventing unnecessary toxicities. To t...
The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision-making of public health authorities. Here, we propose a computational pipeline that dynamically...
Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanica...
The detection of prostate cancer recurrence after external beam radiotherapy relies on the measurement of a sustained rise of serum prostate-specific antigen (PSA). However, this biochemical relapse may take years to occur, thereby delaying the delivery of a secondary treatment to patients with recurring tumors. To address this issue, we propose to...
As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the...
The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the cytotoxic effects of the standard chemotherapeutic drug doxorub...
Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumour status and treatment outcomes. However, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate...
The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions of tumor growth and treatment response over the anatomy of the patient's affected organ.
However, the simulation of the underlying spatiotemporal models can entail a pro...
Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular, and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanic...
Active surveillance (AS) is a suitable management option for many newly-diagnosed prostate cancer (PCa) cases, which usually exhibit low to intermediate clinical risk. In AS, patients are closely monitored via multiparametric magnetic resonance imaging (mpMRI), prostate-specific antigen tests, and biopsies until these reveal an increase in PCa risk...
Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are on...
Objective:
This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers.
Methods:
Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic...
The detection of prostate cancer recurrence after external beam radiotherapy relies on the measurement of a sustained rise of serum prostate-specific antigen (PSA). However, this biochemical relapse may take years to occur, thereby delaying the delivery of a secondary treatment to patients with recurring tumors. To address this issue, here we propo...
The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions of tumor growth and treatment response over the anatomy of the patient's affected organ. These patient-specific computational forecasts have been regarded as a promising...
The early assessment of neoadjuvant therapy (NAT) response in triple-negative breast cancer (TNBC) would enable a treating oncologist to adjust a therapeutic plan of a non-responding patient, and thereby enhance outcomes while preventing unnecessary toxicities. To address this challenge, we propose leveraging personalized, in silico forecasts of tu...
The development of chemoresistance remains a significant cause of treatment failure in breast cancer. We posit that a mathematical understanding of chemoresistance could assist in developing successful treatment strategies. Towards that end, we have developed a model that describes the effects of the standard chemotherapeutic drug doxorubicin on th...
Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2–3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary...
Prostate cancer can be lethal in advanced stages, for which chemotherapy may become the only viable therapeutic option. While there is no clear clinical management strategy fitting all patients, cytotoxic chemotherapy with docetaxel is currently regarded as the gold standard. However, tumors may regain activity after treatment conclusion and become...
Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor status and treatment outcomes. However, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate...
The early determination of response to neoadjuvant therapy (NAT) in triple-negative breast cancer would enable the treating oncologist to adapt the therapeutic regimen of a non-responding patient (e.g., by changing dosage, dose schedule, prescribed drugs), and thereby improve treatment outcomes while avoiding unnecessary toxicities. To address this...
We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing inter...
The outbreak of COVID-19 in 2020 has led to a surge in interest in the research of the mathematical modeling of epidemics. Many of the introduced models are so-called compartmental models, in which the total quantities characterizing a certain system may be decomposed into two (or more) species that are distributed into two (or more) homogeneous un...
According to the American Cancer Society, prostate cancer (PCa) is the most common newly-diagnosed cancer and the second leading cancer-related cause of death among men in the US in 2019. The current clinical protocols of PCa are based on two key strategies: regular screening of men over fifty and patient triaging in risk groups. Prostatic tumors a...
Prostate cancer can be lethal in advanced stages, for which chemotherapy may become the only viable therapeutic option. While there is no clear clinical management strategy fitting all patients, cytotoxic chemotherapy with docetaxel is currently regarded as the gold standard. However, tumors may regain activity after treatment conclusion and become...
We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features....
Chemotherapy is a common treatment for advanced prostate cancer. The standard approach relies on cytotoxic drugs, which aim at inhibiting proliferation and promoting cell death. Advanced prostatic tumors are known to rely on angiogenesis, i.e. the growth of local microvasculature via chemical signaling produced by the tumor. Thus, several clinical...
We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features....
5α-reductase inhibitors are regarded as a promising chemoprevention strategy to reduce the incidence and delay the progression of prostate cancer. Landmark clinical trials have shown the chemopreventive potential of these drugs, but they appear to be mostly effective in mild tumors and have also been correlated with a higher prevalence of advanced...
The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculatureinterstitium geometry and realistic materia...
External beam radiation therapy is a widespread treatment for prostate cancer. The ensuing patient follow-up is based on the evolution of the prostate-specific antigen (PSA). Serum levels of PSA decay due to the radiation-induced death of tumour cells and cancer recurrence usually manifest as a rising PSA. The current definition of biochemical rela...
Cytotoxic chemotherapy is a common treatment for advanced prostate cancer. These tumors are also known to rely on angiogenesis, i.e., the growth of local microvasculature via chemical signaling produced by the tumor. Thus, several clinical studies have been investigating antiangiogenic therapy for advanced prostate cancer, either as monotherapy or...
Significance
Benign prostatic hyperplasia (BPH) is a common disease in aging men that causes the prostate to enlarge progressively. Men with larger prostates tend to harbor prostatic tumors with more favorable features. The underlying mechanisms that explain this interaction between BPH and prostate cancer (PCa) are largely unknown. Here, we propos...
Moving interface problems are ubiquitous in science and engineering. To develop an accurate and efficient methodology for this class of problems, we present algorithms for local h-adaptivity of hierarchical B-splines to be utilized in isogeometric analysis. We extend Bézier projection, an efficient quadrature-free local projection technique, to the...
Significance
We perform a tissue-scale, personalized computer simulation of prostate cancer (PCa) growth in a patient, based on prostatic anatomy extracted from medical images. To do so, we propose a mathematical model for the growth of PCa. The model includes an equation for the reference biomarker of PCa: the prostate-specific antigen (PSA). Henc...
Projects
Project (1)
This project aims at constructing a personalized predictive mathematical model of prostate cancer based on quantitative magnetic resonance imaging data to run organ-scale simulations that improve diagnosis and forecast the patient’s tumor evolution. The model will rely on robust biological and mechanical phenomena described via differential equations whose parameters are identified from the patient’s clinical and imaging data at two dates. The model will then be validated by comparing simulation and actual data at a posterior date.
