Speaker
Description
The Gleason score (GS) is a key predictor of prostate cancer (PCa) aggressiveness and survival, yet treatment decisions rely on biopsies that incompletely sample highly heterogeneous tumors. As a result, clinically relevant spatial variations in tumor aggressiveness may remain undetected. To address this clinically unresolved issue, I present a personalized modelling framework for pointwise prediction of PCa aggressiveness across the 3D tumor domain that is informed by routine clinical and imaging data. The approach combines physics-based and data-driven components in a three-step pipeline. First, a biomechanistic model of PCa growth is personalized using longitudinal MRI and serum PSA data. Second, the model generates spatial maps of mechanistic biomarkers (e.g., reflecting tumor proliferation activity, cell density, and growth dynamics). Third, machine-learning classifiers use these spatial features to infer local tumor aggressiveness across the 3D tumor geometry. Preliminary results using a reaction-diffusion biomechanistic model in a cohort of n=16 PCa cases in active surveillance show that a logistic classifier based on tumor proliferation activity and density achieved an AUC under the ROC curve of 0.96, with sensitivity of 86.4% and specificity of 90.7% for prediction of GS. Of note, model-based predictions anticipated the emergence of higher-risk disease more than one year earlier than standard monitoring, thereby showing promise for guiding clinical decision-making.
Bibliography
@article{lorenzo2024pilot,
title={A pilot study on patient-specific computational forecasting of prostate cancer growth during active surveillance using an imaging-informed biomechanistic model},
author={Lorenzo, Guillermo and Heiselman, Jon S and Liss, Michael A and Miga, Michael I and Gomez, Hector and Yankeelov, Thomas E and Reali, Alessandro and Hughes, Thomas JR},
journal={Cancer research communications},
volume={4},
number={3},
pages={617--633},
year={2024},
publisher={American Association for Cancer Research}
}
@article{lorenzo2024patient,
title={Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data},
author={Lorenzo, Guillermo and Ahmed, Syed Rakin and Hormuth, David A and Vaughn, Brenna and Kalpathy-Cramer, Jayashree and Solorio, Luis and Yankeelov, Thomas E and Gomez, Hector},
journal={Annual Review of Biomedical Engineering},
volume={26},
number={1},
pages={529--560},
year={2024},
publisher={Annual Reviews}
}
@article{yankeelov2024designing,
title={Designing clinical trials for patients who are not average},
author={Yankeelov, Thomas E and Hormuth, David A and Lima, Ernesto ABF and Lorenzo, Guillermo and Wu, Chengyue and Okereke, Lois C and Rauch, Gaiane M and Venkatesan, Aradhana M and Chung, Caroline},
journal={Iscience},
volume={27},
number={1},
year={2024},
publisher={Elsevier}
}
