Assessment of Cardiac Masses by Cardiac Magnetic Resonance Imaging: Histological Correlation and Clinical Outcomes
Bibliographic record
Abstract
Background Cardiac magnetic resonance imaging ( CMR ) provides useful information for characterizing cardiac masses, but there are limited data on whether CMR can accurately distinguish benign from malignant lesions. We aimed to describe the distribution and imaging characteristics of cardiac masses identified by CMR and to determine the diagnostic accuracy of CMR for distinguishing benign from malignant tumors. Methods and Results We examined consecutive patients referred for CMR between May 2008 and August 2013 to identify those with a cardiac mass. In patients for whom there was histological correlation, 2 investigators blinded to all data analyzed the CMR images to categorize the mass as benign or malignant. For benign masses, readers were also asked to specify the most likely diagnosis. Benign masses were defined as benign neoplastic or non-neoplastic. Malignant masses were defined as primary cardiac or metastatic. Of 8069 patients (mean age: 58±16 years; 55% female) undergoing CMR , 145 (1.8%) had a cardiac mass. In most cases (142, 98%), there was a known cardiac mass before the CMR study. Among 145 patients with a cardiac mass, 93 (64%) had a known history of malignancy. Among 53 cases that had histological correlation, 25 (47%) were benign, 26 (49%) were metastatic, and 2 (4%) were malignant primary cardiac masses. Blinded readers correctly diagnosed 89% to 94% of the cases as benign versus malignant, with a 95% agreement rate (κ=0.83). Conclusions Although C MR can be highly effective in distinguishing benign from malignant lesions, pathology remains the gold standard in accurately determining the type of mass.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".