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000289023 1001_ $$0P:(DE-HGF)0$$aAlmeida, Silvia D$$b0$$eFirst author
000289023 245__ $$aCapturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography.
000289023 260__ $$aLausanne$$bFrontiers Media$$c2024
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000289023 520__ $$aChronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD.Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated.Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements.Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.
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000289023 650_7 $$2Other$$aGOLD
000289023 650_7 $$2Other$$aairway disease
000289023 650_7 $$2Other$$aanomaly detection
000289023 650_7 $$2Other$$aartificial intelligence
000289023 650_7 $$2Other$$achronic obstructive pulmonary disease
000289023 650_7 $$2Other$$acomputed tomography
000289023 650_7 $$2Other$$aemphysema
000289023 7001_ $$0P:(DE-He78)a70f21a2bf78bbc1306c3d432ae08dc7$$aNorajitra, Tobias$$b1$$udkfz
000289023 7001_ $$0P:(DE-He78)6a78e3a44a8038881d941fb467eb4e19$$aLüth, Carsten T$$b2$$udkfz
000289023 7001_ $$0P:(DE-He78)4412d586f86ca57943732a2b9318c44f$$aWald, Tassilo$$b3$$udkfz
000289023 7001_ $$0P:(DE-He78)7dc85735e114a4ace658ba1450a2cca6$$aWeru, Vivienn$$b4$$udkfz
000289023 7001_ $$0P:(DE-He78)a657bf15b4cbdf70baed30e14c19d9d3$$aNolden, Marco$$b5$$udkfz
000289023 7001_ $$0P:(DE-He78)04a0b5a49db132d8f00cee326cb743ca$$aJäger, Paul F$$b6$$udkfz
000289023 7001_ $$avon Stackelberg, Oyunbileg$$b7
000289023 7001_ $$aHeußel, Claus Peter$$b8
000289023 7001_ $$aWeinheimer, Oliver$$b9
000289023 7001_ $$aBiederer, Jürgen$$b10
000289023 7001_ $$aKauczor, Hans-Ulrich$$b11
000289023 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b12$$eLast author$$udkfz
000289023 773__ $$0PERI:(DE-600)2775999-4$$a10.3389/fmed.2024.1360706$$gVol. 11, p. 1360706$$p1360706$$tFrontiers in medicine$$v11$$x2296-858X$$y2024
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