Temporal assessment of lesion morphology on radiological images beyond lesion volumes-a proof-of-principle study

Abstract

Objectives: To develop a general framework to assess temporal changes in lesion morphology on radiological images beyond volumetric changes and to test whether cocaine abstinence changes coronary plaque structure on serial coronary CT angiography (CTA).

Methods: Chronic cocaine users with human immunodeficiency virus (HIV) infection were prospectively enrolled to undergo cash-based contingency management to achieve cocaine abstinence. Participants underwent coronary CTA at baseline and 6 and 12 months following recruitment. We segmented all coronary plaques and extracted 1103 radiomic features. We implemented weighted correlation network analysis to derive consensus eigen radiomic features (named as different colors) and used linear mixed models and mediation analysis to assess whether cocaine abstinence affects plaque morphology correcting for clinical variables and plaque volumes and whether serum biomarkers causally mediate these changes. Furthermore, we used Bayesian hidden Markov network changepoint analysis to assess the potential rewiring of the radiomic network.

Results: Sixty-nine PLWH (median age 55 IQR: 52-59 years, 19% female) completed the study, of whom 26 achieved total abstinence. Twenty consensus eigen radiomic features were derived. Cocaine abstinence significantly affected the pink and cyan eigen features (-0.04 CI: [-0.06; -0.02], p = 0.0009; 0.03 CI: [0.001; 0.04], p = 0.0017, respectively). These effects were mediated through changes in endothelin-1 levels. In abstinent individuals, we observed significant rewiring of the latent radiomic signature network.

Conclusions: Using our proposed framework, we found 1 year of cocaine abstinence to significantly change specific latent coronary plaque morphological features and rewire the latent morphologic network above and beyond changes in plaque volumes and clinical characteristics.

Key points: • We propose a general methodology to decompose the latent morphology of lesions on radiological images using a radiomics-based systems biology approach. • As a proof-of-principle, we show that 1 year of cocaine abstinence results in significant changes in specific latent coronary plaque morphologic features and rewiring of the latent morphologic network above and beyond changes in plaque volumes and clinical characteristics. • We found endothelin-1 levels to mediate these structural changes providing potential pathological pathways warranting further investigation.

Keywords: Artificial intelligence; Cocaine addiction; Coronary artery disease; Longitudinal studies; Precision medicine.

Figure 1.

Hierarchical clustering of patients based on their radiomic profiles and the corresponding clinical values at baseline, 6 months, and 12 months follow-up. Patient hierarchical clustering dendrograms were created based on the radiomic profiles of the patients at a) baseline, b) 6 months and c) 12 months. Red indicates the value of the given clinical parameter, with red corresponding to the highest value and white corresponding to the lowest value among the individuals. Low cophenetic correlation values between the dendrograms at baseline and 6 months (cophenetic correlation=0.42) and 6 months and 12 months (cophenetic correlation=0.67) indicate substantial changes in radiomic features which result in reorganization of patient similarities. Abbreviations: ASCVD: atherosclerotic cardiovascular risk, CP: calcified plaque, LANCP: low-attenuation noncalcified plaque, NCP: noncalcified plaque

Figure 2.

Visual representation of consensus radiomic feature modules. Using weighted gene co-expression network analysis, we created a consensus topological overlap matrix which provides a representation of the radiomic profile co-expression considering all three timepoints. a) shows the topological overlap matrix with the corresponding hierarchical clustering dendrogram on the left. Using the dynamic tree-cut algorithm, we identified 20 consensus radiomic feature modules (grey is used to mark features not allocated to any of the modules). b) Plotting the radiomic features in multidimensional latent space we can appreciate that the features corresponding to the modules are grouped close to each other, while separation between the consensus modules can be observed indicating that the modules represent different latent structural components. c) Network quality statistics show that there is high inter-correlation between the elements of each network module. Furthermore, the derived eigen radiomic feature shows high module membership (correlation with the elements of the module) and explains a substantial proportion of the variance of the corresponding features in a given module. These results show that the proposed methodology provides a framework to construct a mathematical representation of the latent structural characteristics of lesions using radiological imaging. Furthermore, this reduces the feature space from 3 timepoints x 1,081 radiomic features to 3 timepoints x 20 consensus eigen radiomic features.

Figure 3.

Stability of radiomic feature modules and consensus eigen radiomic features. Percolation analysis of the radiomic feature network was done by randomly removing data of patients (patient-based elimination) or any of their corresponding visits (visit-based elimination) to analyze the stability of the derived feature modules and consensus eigen features. For each percentage of removed cases, 1,000 random draws were done. The median value of the resulting statistic is presented in the figures. a, b) Median and interquartile range of the cophenetic correlation value between the percolated networks and original reference network after excluding patients or visits respectively. c, d) Median percentage of overlap in the features assigned to modules derived from the percolated networks and the original 20 feature modules. e, f) Median Pearson correlation between the 20 percolated consensus eigen radiomic features and the original consensus eigen radiomic features from the reference network. All preservation statistics highlight the robustness of the derived radiomic feature modules and consensus eigen radiomic features. Values for pink and cyan modules are shown with continuous lines as cocaine abstinence showed a significant association with these two modules.

Figure 4.

Mediation analysis on the effects of cocaine abstinence on coronary latent structural features. Causal mediation analysis was used to estimate the average causal mediation effect (ACME) and the average direct effect (ADE). Even though cocaine abstinence only showed a significant association with endothelin-1 and noncalcified plaque volumes, for completeness the results of all other analyses are presented even though a lack of association with the mediator precludes mediation. a) Results for mediation analysis regarding serum biomarkers and the pink consensus eigen radiomic feature. We found a significant ACME for endothelin-1 implying a potential biologically plausible pathway through which cocaine abstinence affects plaque structure. b) Results of mediation analysis with regards to plaque volumes. We found no significant ACME for noncalcified plaque volumes further strengthening our findings that cocaine abstinence modifies plaque structure above and beyond its effect on plaque volumes. c) Results for mediation analysis with regards to serum biomarkers and the blue consensus eigen radiomic feature. Similar to the pink feature, endothelin-1 was found to have a significant ACME providing a biological correlate to our findings. d) Results for mediation analysis regarding plaque volumes and the blue consensus eigen feature. We found no significant ACME for noncalcified plaque volume showing that the effects of cocaine abstinence on plaque structure are above and beyond the effects on plaque volumes. Abbreviation: ACME: average causal mediation effect, ADE: average direct effect, CP: calcified plaque volume, hsCRP: high sensitivity C-reactive protein, LANCP: low-attenuation noncalcified plaque, NCP: noncalcified plaque

Figure 5.

Network changepoint analysis in abstinent and non-abstinent individuals. Using Bayesian hidden Markov network changepoint models, we analyzed whether cocaine abstinence resulted in temporal changes in the consensus radiomic feature network topology. a, f) Model fit statistics of considering different number of changepoints in the data. In case of abstinent individuals, we found a statistical confirmation of 1 break point, while in non-abstinent individuals, a model without a changepoint had the best fit. b, g) Position of consensus radiomic features of abstinent and non-abstinent individuals in the latent space at the identified first (baseline) time regime. The position of the consensus radiomic features is similar between the two patient groups. c, h) Position of consensus radiomic features in latent space in the second time regime (6- and 12-months follow-up). In case of abstinent individuals, significant reorganization of the node positions can be observed, while in non-abstinent participants, the relative node positions are similar to the first time regime, with only a rotation in the latent space. d, i) Strength of the network generation rule (V) in each dimension at the different timepoints. A considerable change in the value of V can be observed in abstinent individuals, where the values of V diverge for dimension-1 and -2 following the intervention. However, in case of non-abstinent individuals, V changes synchronously for both dimensions, indicating minimal change in network topology. e, j) Posterior probability of being in given regime as a function of time. In case of abstinent individuals there is a clear change in regimes over time, while in case of non-abstinent individual, timepoints correspond to the same regime, indicating there is no changepoint in network topology. Abbreviation: LL: log-likelihood, Pr(S_t=k|Y_t): posterior probability of given regime state, V: network generation rule, WAIC: Watanabe-Akaike information criterion