American Association for Cancer Research
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FIGURE 2 from A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

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posted on 2024-03-01, 14:20 authored by Guillermo Lorenzo, Jon S. Heiselman, Michael A. Liss, Michael I. Miga, Hector Gomez, Thomas E. Yankeelov, Alessandro Reali, Thomas J.R. Hughes

Mapping ADC to N and GS. A, Nonlinear least-squares fit of a hyperbolic tangent model (blue solid line) of the mean values of ADC in the tumor across GS groups in previous studies (red dots), which were normalized with respect to the mean ADC in healthy tissue (ADCh) reported for each of them (41–45). We leverage a continuous, extended GS scale in which 0 < GS < 2 indicates healthy and pretumoral tissue and GS > 2 corresponds to prostate cancer (i.e., overlapping the standard discrete values of the GS used to assess histopathologic samples of prostate cancer; see refs. 2, 4). Further details on the hyperbolic tangent model and the fitting method are reported in Supplementary Data S1. B, Mapping of the hyperbolic tangent model obtained for the normalized ADC values (ADC/ADCh, A) to normalized tumor cell density values (i.e., , B). We use the linear mapping in Eq. (1), which introduces a negative proportionality between ADC and tumor cell density, as in other mpMRI-informed biomechanistic models of solid tumor growth (18, 21, 28, 29).


European Commission (EC)

HHS | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)

NSF | ENG | Division of Civil, Mechanical and Manufacturing Innovation (CMMI)

Cancer Prevention and Research Institute of Texas (CPRIT)

Ministero dell'Università e della Ricerca (MUR)



Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.