Márton Kolossváry1, Natasa Jávorszky2, Júlia Karády3, Milán Vecsey-Nagy2, Tamás Zoltán Dávid2, Judit Simon2, Bálint Szilveszter2, Béla Merkely2, Pál Maurovich-Horvat4. 1. MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary. Electronic address: kolossvary.marton@med.semmelweis-univ.hu. 2. MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary. 3. MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary; Cardiovascular Imaging Research Center, Massachusetts General Hospital - Harvard Medical School, Boston, MA, USA. 4. MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary; Department of Radiology, Medical Imaging Centre, Semmelweis University, 2 Korányi Sándor Street, Budapest, Hungary.
Abstract
BACKGROUND: Quantitative coronary plaque parameters are increasingly being utilized as surrogate endpoints of pharmaceutical trials. However, little is known whether differences in segmentation significantly alter parameter values. METHODS: Overall, 100 coronary plaques with adverse imaging characteristics were segmented automatically, by two experts (R1-R2) and three nonexperts (R3-R5). Low attenuation noncalcified (LANCP), noncalcified and calcified plaque volume were calculated and 4310 radiomic features were extracted. Intraclass correlation coefficient (ICC) values were calculated between the segmentations. RESULTS: ICC values between expert readers were 0.84 [CI: 0.77-0.89] for total; 0.83 [CI: 0.76-0.88] for noncalcified; 0.96 [CI: 0.94-0.98] for calcified and 0.65 [CI: 0.51-0.75] for LANCP volumes. Comparing nonexperts' and experts' results, ICC ranged between 0.64 and 0.90 for total; 0.63-0.91 for noncalcified; 0.86-0.96 for calcified and 0.34-0.84 for LANCP volume. All readers (R1-R5) showed poor agreement with automatic segmentation (range: 0.00-0.27), except for calcified plaque volumes (range: 0.73-0.88). Regarding radiomic features, expert readers (R1-R2) achieved good reproducibility (ICC>0.80) in 88.6% (39/44) of first-order, 62.0% (424/684) of gray level co-occurrence matrix (GLCM), 75.8% (50/66) of gray level run length matrix (GLRLM) and 19.8% (696/3516) of geometrical parameters. Between experts and nonexperts, ICC ranged between: 70.5%-86.4% for first-order, 31.0%-58.3% for GLCM, 24.2%-78.8% for GLRLM and 6.2%-21.1% for geometrical features, while between all readers and automatic segmentation ICC ranged between: 25.0%-38.6%; 0.0%-0.0%; 0.0%-3.0% and 1.1%-1.4%, respectively. CONCLUSIONS: Even among experts there is a considerable amount of disagreement in LANCP volumes. Nevertheless, expert readers have the best agreement which currently cannot be replaced with nonexperts' or automatic segmentation.
BACKGROUND: Quantitative coronary plaque parameters are increasingly being utilized as surrogate endpoints of pharmaceutical trials. However, little is known whether differences in segmentation significantly alter parameter values. METHODS: Overall, 100 coronary plaques with adverse imaging characteristics were segmented automatically, by two experts (R1-R2) and three nonexperts (R3-R5). Low attenuation noncalcified (LANCP), noncalcified and calcified plaque volume were calculated and 4310 radiomic features were extracted. Intraclass correlation coefficient (ICC) values were calculated between the segmentations. RESULTS: ICC values between expert readers were 0.84 [CI: 0.77-0.89] for total; 0.83 [CI: 0.76-0.88] for noncalcified; 0.96 [CI: 0.94-0.98] for calcified and 0.65 [CI: 0.51-0.75] for LANCP volumes. Comparing nonexperts' and experts' results, ICC ranged between 0.64 and 0.90 for total; 0.63-0.91 for noncalcified; 0.86-0.96 for calcified and 0.34-0.84 for LANCP volume. All readers (R1-R5) showed poor agreement with automatic segmentation (range: 0.00-0.27), except for calcified plaque volumes (range: 0.73-0.88). Regarding radiomic features, expert readers (R1-R2) achieved good reproducibility (ICC>0.80) in 88.6% (39/44) of first-order, 62.0% (424/684) of gray level co-occurrence matrix (GLCM), 75.8% (50/66) of gray level run length matrix (GLRLM) and 19.8% (696/3516) of geometrical parameters. Between experts and nonexperts, ICC ranged between: 70.5%-86.4% for first-order, 31.0%-58.3% for GLCM, 24.2%-78.8% for GLRLM and 6.2%-21.1% for geometrical features, while between all readers and automatic segmentation ICC ranged between: 25.0%-38.6%; 0.0%-0.0%; 0.0%-3.0% and 1.1%-1.4%, respectively. CONCLUSIONS: Even among experts there is a considerable amount of disagreement in LANCP volumes. Nevertheless, expert readers have the best agreement which currently cannot be replaced with nonexperts' or automatic segmentation.
Authors: Mardhiyati Mohd Yunus; Akmal Sabarudin; Muhammad Khalis Abdul Karim; Puteri N E Nohuddin; Isa Azzaki Zainal; Mohd Shahril Mohd Shamsul; Ahmad Khairuddin Mohamed Yusof Journal: Diagnostics (Basel) Date: 2022-08-19