18Fluorodeoxyglucose Positron Emission Tomography in the Diagnosis and Staging of Lung Cancer: A Systematic Review
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related death in industrialized countries. The overall mortality rate for lung cancer is high, and early diagnosis provides the best chance for survival. Diagnostic tests guide lung cancer management decisions, and clinicians increasingly use diagnostic imaging in an effort to improve the management of patients with lung cancer. This systematic review, an expansion of a health technology assessment conducted in 2001 by the Institute for Clinical and Evaluative Sciences, evaluates the accuracy and utility of 18fluorodeoxyglucose positron emission tomography (PET) in the diagnosis and staging of lung cancer. Through a systematic search of the literature, we identified relevant health technology assessments, randomized trials, and meta-analyses published since the earlier review, including 12 evidence summary reports and 15 prospective studies of the diagnostic accuracy of PET. PET appears to have high sensitivity and reasonable specificity for differentiating benign from malignant lesions as small as 1 cm. PET appears superior to computed tomography imaging for mediastinal staging in non-small cell lung cancer (NSCLC). Randomized trials evaluating the utility of PET in potentially resectable NSCLC report conflicting results in terms of the relative reduction in the number of noncurative thoracotomies. PET has not been studied as extensively in patients with small-cell lung cancer, but the available data show that it has good accuracy in staging extensive- versus limited-stage disease. Although the current evidence is conflicting, PET may improve results of early-stage lung cancer by identifying patients who have evidence of metastatic disease that is beyond the scope of surgical resection and that is not evident by standard preoperative staging procedures. Further trials are necessary to establish the clinical utility of PET as part of the standard preoperative assessment of early-stage lung cancer.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".