Assessment of Age-Related Differences in Lower Leg Muscles Quality Using Radiomic Features of Magnetic Resonance Images DOI
Takuro Shiiba,

Suzumi Mori,

Takuya Shimozono

и другие.

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Сен. 16, 2024

Sarcopenia, characterised by a decline in muscle mass and strength, affects the health of elderly, leading to increased falls, hospitalisation, mortality rates. Muscle quality, reflecting microscopic macroscopic changes, is critical determinant physical function. To utilise radiomic features extracted from magnetic resonance (MR) images assess age-related changes dataset 24 adults, divided into older (male/female: 6/6, 66-79 years) younger 21-31 groups, was used investigate radiomics dorsiflexor plantar flexor muscles lower leg that are for mobility. MR were processed using MaZda software feature extraction. Dimensionality reduction performed principal component analysis recursive elimination, followed classification machine learning models, such as support vector machine, extreme gradient boosting, naïve Bayes. A leave-one-out validation test train classifiers, area under receiver operating characteristic curve (AUC) evaluate performance. The revealed significant differences distributions found between age with adults showing higher complexity variability texture. flexors showed similar or AUC than dorsiflexors all models. When combined muscles, they tended have when alone. Radiomic lower-leg reflect ageing, especially muscles. can offer deeper understanding quality traditional assessments.

Язык: Английский

Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors DOI Creative Commons
Huan Zhang, Tingwei Lu, Lingyun Wang

и другие.

European Radiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 31, 2025

To assess the impact of acquisition and reconstruction factors on robustness radiomics within photon-counting detector CT (PCD-CT). A phantom with twenty-eight texture materials was scanned different including reposition, scan mode (standard vs high-pitch), tube voltage (120 kVp 140 kVp), slice thickness (1.0 mm 0.4 mm), radiation dose level (0.5 mGy, 1.0 3.0 5.0 10.0 mGy), quantum iterative (0/4, 2/4, 4/4), kernel (Qr40, Qr44, Qr48). Thirteen sets virtual monochromatic images at 70-keV were reconstructed. The regions interest drawn rigid registrations. Ninety-three features extracted from each material. reproducibility evaluated using intraclass correlation coefficient (ICC) concordance (CCC). variability assessed by variation (CV) quartile dispersion (QCD). percentage ICC > 0.90 CCC high when repositioned (88.2% 88.2%) changed (87.1% 87.1%), but none high-pitch used. CV < 10% QCD (47.3% 68.8%) (64.2% 71.0%), that low between standard scans (16.1% 26.9%) (19.4% 29.0%). PCD-CT robust to voltage, dose, strength level, kernel, brittle thickness. Question stability against should be fully determined before academic research clinical application. Findings are Clinical relevance influence voxel size set careful attention PCD-CT, allow a higher implementation analysis in routine.

Язык: Английский

Процитировано

1
Assessment of Age-Related Differences in Lower Leg Muscles Quality Using Radiomic Features of Magnetic Resonance Images DOI
Takuro Shiiba,

Suzumi Mori,

Takuya Shimozono

и другие.

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Сен. 16, 2024

Sarcopenia, characterised by a decline in muscle mass and strength, affects the health of elderly, leading to increased falls, hospitalisation, mortality rates. Muscle quality, reflecting microscopic macroscopic changes, is critical determinant physical function. To utilise radiomic features extracted from magnetic resonance (MR) images assess age-related changes dataset 24 adults, divided into older (male/female: 6/6, 66-79 years) younger 21-31 groups, was used investigate radiomics dorsiflexor plantar flexor muscles lower leg that are for mobility. MR were processed using MaZda software feature extraction. Dimensionality reduction performed principal component analysis recursive elimination, followed classification machine learning models, such as support vector machine, extreme gradient boosting, naïve Bayes. A leave-one-out validation test train classifiers, area under receiver operating characteristic curve (AUC) evaluate performance. The revealed significant differences distributions found between age with adults showing higher complexity variability texture. flexors showed similar or AUC than dorsiflexors all models. When combined muscles, they tended have when alone. Radiomic lower-leg reflect ageing, especially muscles. can offer deeper understanding quality traditional assessments.

Язык: Английский

Процитировано

0