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Background: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease. Methods: MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted. Results: Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning. Conclusion: Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A—RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C—Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.

Museum specimens provide a record of past species distribution and are an increasingly important resource for conservation genetic research. The scientific value of these specimens depends upon the veracity of their associated data and can be compromised by inaccurate details; including taxonomic identity, collection locality, and collector. New Zealand contains many endemic species that have been driven to extinction or reduced to relict distributions following the arrival of humans and mammalian predators, including the Acanthisittid wrens (of which only two of the eight described species presently persist). One of these is the New Zealand rock wren (Xenicus gilviventris), currently classified as an endangered species and experiencing ongoing population declines. Here we analyze ancient DNA retrieved from New Zealand rock wren museum skins to establish the veracity of their recorded collection localities—New Zealand rock wrens exhibit strong north-south genetic structuring along the Southern Alps of New Zealand's South Island. We include the only specimen reportedly collected from New Zealand's North Island, outside the known range of New Zealand rock wrens, specimens collected by Henry Hamersley Travers, a collector known for poor record keeping and potentially fraudulent specimen data, and type specimens of proposed Xenicus taxa. Multiple instances of inaccurate collection locality were detected, including that of the New Zealand rock wren reportedly collected from the North Island, which matches individuals from the southern South Island. Syntypes of X. haasti, and a syntype of X. gilviventris clustered with individuals belonging to the northern New Zealand rock wren lineage. Our results suggest that New Zealand rock wrens have not been historically extirpated from New Zealand's North Island, and that caution must be taken when utilizing museum specimens to inform conservation management decisions. Additionally, we describe the type locality of both X. gilviventris and X. haasti, with genetic and historical evidence suggesting that the specimens used to describe these taxa were collected from the headwaters of the Rakaia River. This study demonstrates that ancient DNA analysis can add value to museum specimens by revealing incorrect specimen data and inform the conservation management and taxonomy of endangered species.