Clinical outcomes of pancoast tumors treated with trimodality therapy
Bibliographic record
Abstract
BACKGROUND: Superior sulcus tumors, or Pancoast tumors, are challenging thoracic malignancies to treat due to their anatomical location posing difficult surgical access and potential involvement of adjacent vital structures. The current standard of care is trimodality treatment, which consists of induction chemoradiotherapy followed by radical surgical resection. This study aims to report the clinical outcomes of trimodality approach in British Columbia, Canada. METHODS: Patients with Pancoast tumors who underwent trimodality treatment between 2000-2015 were included in this provincial multi-center retrospective study. Patient-, disease-, and treatment-related data were collected, and treatment outcomes were recorded. RESULTS: We identified 32 patients who underwent induction chemoradiotherapy and subsequent surgical resection. Mean age was 59 (43-75 years) with median follow-up of 43 months (5-216 months). Complete resection was achieved in 31 patients (97%). Fourteen patients (44%) had pathological complete response after induction chemoradiotherapy. Thirteen (41%) showed minimal microscopic (>90% tumor necrosis) and 5 (16%) macroscopic residual disease (<90% tumor necrosis). Fourteen patients (44%) developed recurrence, which was distant in 9 cases. The 2-, 5-, and 10-year overall survival rates were 67.9%, 50.1%, 31.8% and the 2-, 5-, and 10-year disease-free survival rates were 65.1%, 47.1% and 28.2% respectively. There were no statistically significant differences in overall survival or disease-free survival rates with or without pathological complete response. CONCLUSIONS: Complete surgical resection with negative margins can be achieved after induction chemoradiotherapy, and curative-intent trimodality treatment can lead to long-term survival in some patients. This study did not demonstrate any prognostic value of pathological complete response, likely due to small sample size.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".