Magnetic Resonance of Pulmonary Nodules in Oncological Patients: Are We Ready to Replace Chest CT in Detecting Extrathoracic Cancer?
Bibliographic record
Abstract
Objective: This study aims to assess the accuracy of pulmonary nodule detection via MRI compared to the gold standard, CT, in patients with extrathoracic cancer. Materials and Methods: MRI of the chest was performed on oncological patients for staging extrathoracic cancer and subsequently compared to their CT. Only the largest nodule was considered in patients with multiple nodules. Nodule size and malignancy were recorded for each modality, as well as the presence of interstitial lung disease (ILD), adenopathy, cardiomegaly, pleural effusion, pericardial effusion, and vertebral fracture. All cases were read by two thoracic radiologists and any discrepancies were resolved by discussion. Results: A total of 154 patients with nodules were identified from 241 participants (mean age: 59 years [18–95]). Average nodule diameter was 11.5 mm (range: 3.9–29.1 mm; SD: 8.1 mm). MRI detected all nodules greater than 5 mm. Malignancy was detected in 37 nodules. The sensitivity, specificity, and accuracy values of MRI for all nodules were 93.51%, 100%, and 95.85%, respectively. For ground-glass nodules (n = 11), the values were 43.6%, 100%, and 65.0%, respectively. When compared to CT, long-axis diameter measured by MRI was underestimated by 9 ± 2.3% (p < 0.001). There was a strong correlation between measurements of CT and MRI (κ = 0.70–1.00). Furthermore, MRI accurately detected the presence of adenopathy (97.1%), cardiomegaly (99.17%), pleural effusion (98.34%), pericardial effusion (100%), and vertebral fracture (99.6%). Conclusions: These findings suggest that lung MRI accurately detects pulmonary nodules and other thoracic pathologies commonly observed in oncological patients. Lung MRI may serve as a substitute to CT for oncological patients undergoing routine extrathoracic surveillance, thereby decreasing radiation exposure.
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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 | 0.004 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".