Population-based assessment of the National Comprehensive Cancer Network recommendations for baseline imaging of rectal cancer
Bibliographic record
Abstract
Aim: To examine the performance characteristics of alternative criteria for baseline staging, in a cohort of contemporary rectal cancer patients from the Surveillance, Epidemiology and End Results (SEER) database. Methods: The SEER database (2010–2015) was accessed and patients with rectal cancer plus complete information on clinical T and N stages as well as metastatic sites were evaluated. We examined various performance characteristics of baseline imaging, including specificity, sensitivity, number needed to investigate (NNI), positive predictive value (PPV), negative predictive value and accuracy. Results: A total of 15,836 rectal cancer patients were included. Based on current guidelines that suggest cross-sectional chest and abdominal imaging for all cases of invasive rectal cancer, these recommendations would yield a PPV of 11.9% for the detection of liver metastases and 6.2% for the detection of lung metastases. This would translate to an NNI of 8.4 for liver metastases and an NNI of 16.1 for lung metastases. When patients with T1N0 were excluded from routine imaging, this resulted in a PPV of 6.4% and an NNI of 15.6 to identify one case of lung metastasis. Likewise, this resulted in a PPV of 12.3% and an NNI of 8.0 to detect one case of liver metastasis. Similarly, when patients with either T1N0 or T2N0 were excluded from routine imaging, the PPV and NNI for lung metastases improved to 6.6% and 15.1, respectively, and the PPV and NNI for liver metastases improved to 12.6 and 7.9%, respectively. Conclusion: Our study suggests that the specificity of the current imaging approach for rectal cancer staging is limited and that the omission of chest and abdominal imaging among selected early stage asymptomatic cases may be reasonable to consider.
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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) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 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".