Detection and size of pulmonary lesions: how accurate is MRI? A prospective comparison of CT and MRI
Bibliographic record
Abstract
BACKGROUND: Although CT is the modality of choice for morphological lung imaging, an increasing proportion of chest imaging is performed by MRI due to the utilization of whole-body MRI. Therefore, the diagnostic performance of MRI in reliably detecting pulmonary lesions should be established. PURPOSE: To investigate the detection rate of pulmonary lesions by MRI that can be expected in a clinical setting and to assess the accuracy of lesion measurement by MRI compared to CT. MATERIAL AND METHODS: Twenty-eight patients (median age 66 years) with indication for CT imaging due to suspected thoracic malignancy were prospectively included. Chest MRI performed on the same day as CT, comprised unenhanced TrueFisp, ecg-gated T2-weighted HASTE, T1-weighted VIBE, and contrast-enhanced T1-weighted, fat-saturated VIBE sequences. MR sequences were evaluated for lesion detection by two readers independently and measurement of lesion size was performed. MR findings were correlated with CT. RESULTS: One hundred and eight pulmonary lesions (20 thoracic malignancies, 88 lung nodules) were detected by CT in 26 patients. Lesions were ruled out in two patients. All thoracic malignancies were identified by MRI with strong correlation (r = 0.97-0.99; P < 0.01) in lesion size measurement compared to CT. Unenhanced, T1-weighted VIBE correctly classified 94% of thoracic malignancies into T-stages. Contrast-enhanced, T1-weighted VIBE performed best in identifying 36% of lung nodules, 40% were detected combining unenhanced and contrast-enhanced T1-weighted VIBE. Detection rate increased to 65% for the combined sequences regarding lesions ≥5 mm. Lesion size measurement by all MR sequences strongly correlated with CT (r = 0.96-0.97; P = 0.01). CONCLUSION: MRI is as accurate as CT in detection and size measurement of primary thoracic malignancies >1 cm in diameter. If a lung lesion is detected by MRI, it is a reliable finding and its measurement is accurate. CT remains superior in detecting small lung nodules (<6 mm). Detection rate of MRI for small lesions is improved using a multi-sequence protocol including contrast administration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".