Is There a Role of Double Reporting and CT Pelvis for Lung Cancer Staging?
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the most common cancer in the world. Staging of lung cancer involves CT of chest and abdomen. Subsequently these are discussed in MDT and if required PET imaging is arranged. We have performed a study to assess double reporting of the initial staging CT would identify in field metastasis and hence decrease the use of PET. METHODS: A refined search from the lung cancer database over 2 years of 980 patients was performed. Metastasis identified on PET (SUV > 2.5) was nominated as the gold standard, 219 patients had both PET and staging CT (chest and abdomen) with 38 patients having metastasis on both PET and CT. CT images were reviewed by two independent radiologist who were blinded to the report. Identified metastases were graded if identified. These were grade as 1- definite, 2- equivocal, 3- normal. Subsequently through a process of arbitration a combined decision about the in field metastasis was achieved. RESULTS: There were 21 metastasis which were within the field of chest and abdomen (in field metastasis). Only a half of these were identified by blinded observers. Following an arbitration there was no significant improvement in the pick up rate. There were 19 out of field metastasis in 15 patients out of this cohort. Majority of these (72%) were in the bony pelvis which would have been reported if a CT pelvis was performed as a part of staging. We estimate that one would have to perform 10 CT pelvises to save one PET-CT. CONCLUSION: Double reading of staging scan would not identify all infield metastasis. The increased contrast in PET images makes it easy to spot metastases. Hence there is no role for double reporting of staging CT in lung cancer management. Inclusion of pelvis in staging of lung cancer may be effective and would improve the detection of out of field metastases hence decreasing the use of PET.
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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 | 0.077 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".