Improved Radiologic Staging of Lung Cancer with 2-[18F]-Fluoro-2-Deoxy-d-Glucose–Positron Emission Tomography and Computed Tomography Registration
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Bibliographic record
Abstract
PURPOSE: To determine if volumetric nonlinear registration or registration of thoracic computed tomography (CT) and 2-[18F]-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET) datasets changes the detection of mediastinal and hilar nodal disease in patients undergoing staging for lung cancer and if it has any impact on radiologic lung cancer staging. METHOD: Computer-based image registration was performed on 45 clinical thoracic helical CT and FDG-PET scans of patients with lung cancer who were staged by mediastinoscopy and/or thoracotomy. Thoracic CT, FDG-PET, and registration datasets were each interpreted by 2 readers for the presence of metastatic nodal disease and were staged independently of each other. Results were compared with surgical pathologic findings. RESULTS: One hundred and thirty lymph node stations in the mediastinum and hila were evaluated each on CT, PET, and registration datasets. Sensitivity, specificity, positive predictive value, and negative predictive value, respectively, for detecting metastatic nodal disease for CT were 74%, 78%, 55%, 88%; for PET with CT side by side, 59% to 76%, 77% to 89%, 48% to 68%, and 84% to 91%; and for CT-PET registration, 71% to 76%, 89% to 96%, 70% to 86%, and 90% to 91%. Registration images were significantly more sensitive in detecting nodal disease over PET for 1 reader (P = 0.0156) and were more specific than PET (P = 0.0107 and 0.0017) in identifying the absence of mediastinal disease for both readers. Registration was significantly more accurate for staging when compared with PET for both readers (P = 0.002 and 0.035). CONCLUSION: Registration of CT and FDG-PET datasets significantly improved the specificity of detecting metastatic disease. In addition, registration improved the radiologic staging of lung cancer patients when compared with CT or FDG-PET alone.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it