Value of PET/MRI for assessing tumor resectability in NSCLC—intra-individual comparison with PET/CT
Bibliographic record
Abstract
Objective: The purpose of this study was to compare the diagnostic accuracy of positron emission tomography (PET)/MRI with PET/CT for determining tumor resectability of non-small cell lung cancer (NSCLC). Methods: Sequential trimodality PET/CT/MRI was performed in 36 patients referred with the clinical question of resectability assessment in NSCLC. PET/CT and PET/MR images including T 1 weighted sequence (T 1-Dixon) and respiration gated T 2 weighted sequence (T 2-Propeller) were evaluated for resectability-defining factors; i.e. longest diameter of the tumor, minimal tumor distance to the carina, mediastinal invasion, invasion of the carina, pleural infiltration, pericardial infiltration, diaphragm infiltration, presence of additional nodules. Results: There was no significant difference of maximal axial diameter measurements of the primary lung tumors and narrow limits of agreement in Bland–Altman analysis ranging from −11.1 mm to + 11.8 mm for T 2-Propeller and from −14.3 mm to + 13.8 mm for T 1-Dixon sequence. A high agreement of PET/MR with PET/CT for the different resectability-defining factors was observed (k from 0.769 to 1.000). There was an excellent agreement of T 2-Propeller sequence and CT for additional pulmonary nodule detection (k of 0.829 and 0.833), but only a moderate and good agreement using T 1-Dixon sequence (k of 0.484 and 0.722). Conclusion: In NSCLC the use of PET/MRI, including a dedicated pulmonary MR imaging protocol, provides a comparable diagnostic value for determination of tumor resectability compared to PET/CT. Advances in knowledge: Our findings suggest that whole body PET/MRI can safely be used for the local staging of NSCLC patients. Further studies are warranted to determine whether it is feasible to integrate an imaging sequence in a whole body PET/MRI setting with the potential advantage of detection of liver or brain metastases.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".