Radiomics-Based Diagnosis in Cardiology: Advances and Prospects
Bibliographic record
Abstract
With the increasing need for faster and more accurate diagnosis in cardiology, radiomics presents an innovative approach for assessing medical images and diagnosing clinical conditions. This review aims to highlight the applications of radiomics in the diagnosis of cardiovascular conditions. The development of a radiomic model typically progresses as follows: image acquisition and preprocessing, image segmentation, image processing, feature extraction, feature selection, and machine learning modeling and validation. Image data is commonly obtained from cardiac computed tomography angiography, cardiac magnetic resonance imaging, echocardiography, and nuclear imaging. Using machine learning frameworks such as decision trees, random forests, support vector machines, XGBoost, and deep learning, radiomics-based models demonstrated better performance for diagnosis and prediction of cardiovascular events than models designed using conventional clinical risk factors. Radiomics is applied in plaque and adipose tissue characterization to determine the degree of stenosis or predict plaque rupture. In cardiomyopathies, radiomics is employed to distinguish between healthy and diseased tissues. A notable challenge hindering the integration of radiomics in clinical practice is the lack of standardization of study protocols, including image acquisition and processing. Multiple studies also highlighted the need for high-quality images as well as validation of the radiomics model using data from multiple data collection centers. Findings from this study revealed that, while notable advancements have been recorded in radiology-based diagnosis in cardiology, there is a need for further research effort to harmonize evidence and enable the real-world clinical application of radiomics.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".