Abstract 4135968: Radiomic features of skeletal muscle on prostate T1-Weighted MRI associated with MACE outcomes in prostate cancer patients undergoing hormonal therapy

Background: Patients with high-risk prostate cancer (PC) who undergo hormonal therapy(HT) frequently die of major adverse cardiovascular events (MACE). Skeletal muscle on abdominal T1-weighted MRI (T1W) has been shown to be associated with cardiovascular fitness. Aims: To quantify heterogeneity associated with skeletal muscle tissue composition on prostate MRI patients undergoing HT using computationally derived radiomic features and identify their association with MACE. Methods: Cohort of N=58 men diagnosed with PC who underwent a 3 Tesla pre-treatment multi-parametric MRI followed by HT and followed up for MACE events were identified. MACE is defined as a composite of cardiovascular death, atrial fibrillation, myocardial infarction, ischemic stroke, heart failure, coronary artery disease, and peripheral vascular disease. Regions of interest (ROI) of skeletal muscle, including the obturator externus, pectineus, and the adductor group of muscles, were delineated on axial T1W under the guidance of an experienced radiologist. A set of 120 radiomic features, including gray level statistics, co-occurrence, wavelet, and texture energy-based features, were extracted within the ROI which quantifies underlying subvisual texture-based heterogeneity. Radiomics associated with MACE events were identified using the Wilcoxon rank-sum test and univariate Cox-regression (p<0.05). Results: Of the N=58 patients included in this study, 18 experienced MACE. The most common MACE event was AF (8.6%). Median-time-to-MACE event was 7.3 years (6.2-8.2). 3 radiomic features (Table 1) quantifying the heterogeneity of skeletal muscle on prostate T1W were observed to be associated with MACE. Specifically, we observe that first order features have a lower response and wavelet features have a lower response in MACE patients which suggests a higher degree of local deformations and heterogeneity in skeletal tissue compared to non-MACE patients. Conclusions: Results from our study suggest that radiomics quantified texture characteristics of skeletal muscle tissue may indicate cardiovascular fitness, reflecting the likelihood of experiencing MACE following initiation of HT in PC men. Pre-treatment identification of PC patients at high risk of MACE may allow for alternative treatment options improving cardiovascular outcomes. Future studies will involve exploring larger, multi-site cohorts and machine learning modeling to determine cardiovascular risk.

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Journal
Circulation
Published
2024-11-12
DOI
https://doi.org/10.1161/circ.150.suppl_1.4135968
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
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Abstract 4135968: Radiomic features of skeletal muscle on prostate T1-Weighted MRI associated with MACE outcomes in prostate cancer patients undergoing hormonal therapy

Omar Mohamed Makram, Stanley Joseph, Pardeep Mittal, Neal L. Weintraub et al.
Circulation
Radiomics and Machine Learning in Medical Imaging
article

Abstract 4135968: Radiomic features of skeletal muscle on prostate T1-Weighted MRI associated with MACE outcomes in prostate cancer patients undergoing hormonal therapy

Omar Mohamed Makram, Stanley Joseph, Pardeep Mittal, Neal L. Weintraub, Gourav Modanwal, Rakesh Shiradkar, Avirup Guha, Stephanie Jiang, Sandeep Yerraguntla, Harikrishnan Hyma Kunhiraman, Liping Li, Sagar Patel, Gaurav Gopu, Natasha Setiadi
article en

Abstract

Background: Patients with high-risk prostate cancer (PC) who undergo hormonal therapy(HT) frequently die of major adverse cardiovascular events (MACE). Skeletal muscle on abdominal T1-weighted MRI (T1W) has been shown to be associated with cardiovascular fitness. Aims: To quantify heterogeneity associated with skeletal muscle tissue composition on prostate MRI patients undergoing HT using computationally derived radiomic features and identify their association with MACE. Methods: Cohort of N=58 men diagnosed with PC who underwent a 3 Tesla pre-treatment multi-parametric MRI followed by HT and followed up for MACE events were identified. MACE is defined as a composite of cardiovascular death, atrial fibrillation, myocardial infarction, ischemic stroke, heart failure, coronary artery disease, and peripheral vascular disease. Regions of interest (ROI) of skeletal muscle, including the obturator externus, pectineus, and the adductor group of muscles, were delineated on axial T1W under the guidance of an experienced radiologist. A set of 120 radiomic features, including gray level statistics, co-occurrence, wavelet, and texture energy-based features, were extracted within the ROI which quantifies underlying subvisual texture-based heterogeneity. Radiomics associated with MACE events were identified using the Wilcoxon rank-sum test and univariate Cox-regression (p<0.05). Results: Of the N=58 patients included in this study, 18 experienced MACE. The most common MACE event was AF (8.6%). Median-time-to-MACE event was 7.3 years (6.2-8.2). 3 radiomic features (Table 1) quantifying the heterogeneity of skeletal muscle on prostate T1W were observed to be associated with MACE. Specifically, we observe that first order features have a lower response and wavelet features have a lower response in MACE patients which suggests a higher degree of local deformations and heterogeneity in skeletal tissue compared to non-MACE patients. Conclusions: Results from our study suggest that radiomics quantified texture characteristics of skeletal muscle tissue may indicate cardiovascular fitness, reflecting the likelihood of experiencing MACE following initiation of HT in PC men. Pre-treatment identification of PC patients at high risk of MACE may allow for alternative treatment options improving cardiovascular outcomes. Future studies will involve exploring larger, multi-site cohorts and machine learning modeling to determine cardiovascular risk.

CirculationVol. 150(Suppl_1)
Georgia Institute of Technology (US), Emory University (US), Atlanta Technical College (US), Augusta University Health (US)
Good health and well-being
Openalex Percentile: Top 66%
Radiomics and Machine Learning in Medical Imaging
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