American Association for Cancer Research
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FIGURE 8 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

Potential model-based biomarkers of higher-risk prostate cancer. AF, Distribution of six potential model-based biomarkers in lower-risk (n = 8) and higher-risk (n = 8) prostate cancer, which were defined as tumors with GS = 3 + 3 and GS ≥ 3 + 4, respectively. These model-based biomarkers were calculated at the times were both a histopathologic assessment and imaging measurement are available for each patient in the cohort (n = 7). In particular, the model-based markers in A–F are: prostate volume (VP), tumor volume, total tumor cell volume (VN), mean normalized tumor cell density (), total tumor index (NT), and mean proliferation activity of the tumor (Ap). Outliers are represented as hollow circles and an asterisk indicates significance under a one-sided Wilcoxon rank-sum test (P < 0.05). G, ROC curves for (i) the univariate logistic regression model constructed using the mean proliferation activity of the tumor (i.e., the only model-based marker that was significantly different between lower-risk prostate cancer and higher-risk prostate cancer), and (ii) the bivariate logistic regression model constructed using the mean proliferation activity of the tumor and the total tumor index (which is the combination of model-based markers that rendered the highest performance). The AUC of each ROC curve is reported within the plot, and the optimal performance point for both the univariate and bivariate logistic regression models operates at 75% sensitivity and specificity (bullet points on the ROC curves).

Funding

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)

History

ARTICLE ABSTRACT

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.