RADIAL ARTERY PHENOTYPING IN SYSTEMIC SCLEROSIS THROUGH ULTRA-HIGH FREQUENCY ULTRASOUND: A RADIOMIC APPROACH
Bibliographic record
Abstract
Objective: Systemic sclerosis (SSc) is a disorder characterized by a massive vascular involvement. Imaging biomarkers of vascular involvement in SSc may have potential clinical implications for prediction of the pathogenesis of vascular complications. This study is aimed at identifying possible patterns of vascular wall disarray and remodeling in radial arteries of SSc patients, by means of ultrahigh frequency ultrasound (UHFUS). Design and method: 5 end-diastolic frames of the right radial arteries of 41 patients with SSc and 41 healthy controls were obtained by VevoMD (70 MHz probe, FUJIFILM, VisualSonics, Toronto, Canada). 74 radiomic features and 4 engineered parameters were extracted: inner and outer layer thickness, and presence of adjunctive acoustic interfaces (triple signal). A feature selection algorithm was applied to reduce the number of features. The selected features were used to train classification model, using Linear Support Vector Machine (SVM). Results: The SVM classification model showed good performance (sensitivity = 0.63, specificity = 0.88, accuracy = 0.75, AUC = 0.75) to discriminate SSc patients from controls using fifteen selected features. Inner layer (208±61 vs 179±47 μm, p = 0.04) and outer layer thickness (104±22 vs 120±36 μm, p = 0.03) were significantly higher in SSc than in controls, triple signal pattern more frequent in patients (p = 0.002). Conclusions: Wall ultrastructure of radial arteries of SSc patients is altered: inner and outer layer thickened, showing frequently a triple signal pattern. Radiomic approach allow to distinguish between radial images from SSc patients and controls with a 75% accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".