Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [18F]NaF PET/CT images.

Background: [¹⁸F]NaF is a potential biomarker for assessing cardiac risk. Automated analysis of [¹⁸F]NaF positron emission tomography (PET) images, specifically through quantitative image analysis ("radiomics"), can potentially enhance diagnostic accuracy and personalised patient management. However, it is essential to evaluate the reproducibility and reliability of radiomic features to ensure their clinical applicability. This study aimed to (i) develop and evaluate an automated model for coronary artery segmentation using [¹⁸F]NaF PET and calcium scoring computed tomography (CSCT) images, (ii) assess inter- and intra-observer radiomic reproducibility from manual segmentations, and (iii) evaluate the radiomics reliability from AI-derived segmentations by comparison with manual segmentations.

Results: 141 patients from the "effects of Vitamin K and Colchicine on vascular calcification activity" (VikCoVac, ACTRN12616000024448) trial were included. 113 were used to train an auto-segmentation model using nnUNet on [¹⁸F]NaF PET and CSCT images. Reproducibility of inter- and intra-observer radiomics and reliability of radiomics from AI-derived segmentations was assessed using lower bound of intraclass correlation coefficient (ICC). The auto-segmentation model achieved an average Dice Similarity Coefficient of 0.61 ± 0.05, having no statistically significant difference compared to the intra-observer variability (p = 0.922). For the unfiltered images, 47(12.6%) CT and 25(7.5%) PET radiomics were inter-observer reproducible, while 133(35.8%) CT and 57(15.3%) PET radiomics were intra-observer reproducible. 7(9.7%) CT and 18(25.0%) PET first-order features, as well as 17(17.7%) CT GLCM features, were reproducible for both inter- and intra-observer analyses. 9.8% and 16.8% of radiomics from AI-derived segmentations showed excellent and good reliability. First-order features were most reliable (ICC > 0.75; 78/144[54.2%]) and shape features least (2/112[1.8%]). CT features demonstrated greater reliability (147/428[34.3%]) than PET (81/428 [18.9%]). Features from the left anterior descending (76/214[35.5%]) and right coronary artery (75/214[35.0%]) were more reliability than the circumflex (49/214[22.9%]) and left main (28/214[13.1%]) arteries.

Conclusions: An effective segmentation model for coronary arteries was developed and reproducible [¹⁸F]NaF PET/CSCT radiomics were identified through inter- and intra-observer assessments, supporting their clinical applicability. The reliability of radiomics from AI-derived segmentations compared to manual segmentations was highlighted. The novelty of [¹⁸F]NaF as a biomarker underscores its potential in providing unique insights into vascular calcification activity and cardiac risk assessment.

Clinical trial registration: VIKCOVAC trial ("effects of Vitamin K and Colchicine on vascular calcification activity"). Unique identifier: ACTRN12616000024448. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368825 .