Is routine baseline brain imaging needed for all newly diagnosed non-small-cell lung cancer patients?
Bibliographic record
Abstract
Aim:Dedicated brain imaging is advocated by the National Comprehensive Cancer Network guidelines for newly diagnosed non-small-cell cancer (NSCLC) patients beyond stage I. The current study assessed the performance characteristics of this recommendation.Methods:Through accessing the Surveillance, Epidemiology and End Points (SEER) registry (2010–2015), all patients (regardless of stage) with newly diagnosed NSCLC and complete information about TN stages and presence or absence of brain metastases were extracted. In the current study, the following performance characteristics of the above recommendation were assessed: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), number needed to investigate (NNI) and accuracy.Results:A total of 182,977 NSCLC patients were included. For the overall cohort, PPV (for the recognition of brain metastases) was 13.8% and NNI to detect one case of brain metastasis was 7.2. Likewise, NPV (for the exclusion of brain metastases) was 97%, sensitivity was 92.1%, specificity was 31.1% and overall accuracy was 37.6%. When stratified by histology, patients with adenocarcinoma have PPV of 17.2% and NNI to detect one case with brain metastasis of 5.8. NPV (for the exclusion of brain metastases) was 97%, sensitivity of 91.4%, specificity of 35.4% and overall accuracy of 32.6%. On the other hand, patients with squamous cell carcinoma have PPV of 6.3% and NNI to detect one case with brain metastasis of 15.8. NPV (for the exclusion of brain metastases) was 98.9%, sensitivity of 94.6%, specificity of 26.3% and overall accuracy of 29.7%.Conclusion:In view of the poor specificity, the current study calls for reconsideration of the universal recommendation of dedicated brain imaging (in addition to PET/CT scan) among NSCLC patients beyond stage I.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".