Reducing futile thoracotomy rates in PET-CT staged non small cell lung cancer: Clinical risk factors from a population-based review.
Bibliographic record
Abstract
7551 Background: The use of PET-CT in staging NSCLC reduces futile thoracotomy (FT) rates to approximately 30%. We aimed to identify pre-operative clinical risk factors for FT in patients (pts) staged with PET-CT. Methods: The British Columbia Cancer Agency (BCCA) provides care to 4.5 million people. A retrospective chart review was conducted on all pts referred to the BCCA in 2009-2010 who had staging PET-CT and thoracotomy for NSCLC. Exclusion criteria: tri-modality therapy, clinical N2 disease, or cancer within 5 years. FT was defined as benign lung lesion, exploratory thoracotomy, pathologic N2 disease, stage IIIB/IV, or recurrence or death < 1 year of surgery (sx). The FT and non-FT groups were compared with the Fisher test in univariate analysis and logistic regression model multivariate analysis. Results: 108 pts met inclusion criteria. Baseline characteristics: male 42%, median age 67 (45-82), ECOG 0-1/+2: 85%/15%, never/former/current smoker 18.5/42.5/39%, weight loss >10% 9%. Disease characteristics: nonsquamous/ squamous histology 72/28%, median primary tumor size 3.2 cm, median SUVmax 10.1, PET + N1 24%. Median time from PET to sx 29 days. 29% pts received adjuvant chemotherapy. Thoracotomy was futile in 27 pts (25%); 14 recurred < 1 yr of sx, 10 pathologic N2 and 1 each incomplete resection, pleural disease at sx, death within 1 yr. On univariate analysis, PET + N1 (odds ratio [OR] 3.77, p 0.008) and primary tumor size > 3.2cm (OR 2.93, p 0.026) were associated with FT. On multivariate analysis, ECOG >1 (OR 4.57, p 0.017), PET + N1 status (OR 4.24, p 0.006) and primary tumor size > 3.2cm (OR 2.87, p 0.039) were associated with FT. Among the 26 pts wth PET + N1, 44% underwent FT; 23% due to N2 disease, 19% relapsed within 1 yr, 4% incomplete sx. 27% had mediastinoscopy or EBUS staging. Among the 82 pts with PET – N1,18% underwent FT; 5% due to N2 disease, 11% relapsed < 1 yr, 2% pleural dx or death < 1 yr. Conclusions: Pre-operative ECOG >1, primary tumor size > 3.2 cm and PET + N1 are associated with higher rates of FT in NSCLC. PET + N1 disease corresponds to higher rates of N2 disease and surgical staging may reduce FT in this population. These factors should be taken into consideration to reduce FT rates in NSCLC.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".