Stereotactic ablative radiotherapy before resection to avoid delay for early‐stage lung cancer or oligometastases during the COVID‐19 pandemic: Pathologic outcomes from the SABR‐BRIDGE protocol
Bibliographic record
Abstract
BACKGROUND: During coronavirus disease 2019 (COVID-19)-related operating room closures, some multidisciplinary thoracic oncology teams adopted a paradigm of stereotactic ablative radiotherapy (SABR) as a bridge to surgery, an approach called SABR-BRIDGE. This study presents the preliminary surgical and pathological results. METHODS: Eligible participants from four institutions (three in Canada and one in the United States) had early-stage presumed or biopsy-proven lung malignancy that would normally be surgically resected. SABR was delivered using standard institutional guidelines, with surgery >3 months following SABR with standardized pathologic assessment. Pathological complete response (pCR) was defined as absence of viable cancer. Major pathologic response (MPR) was defined as ≤10% viable tissue. RESULTS: Seventy-two patients underwent SABR. Most common SABR regimens were 34 Gy/1 (29%, n = 21), 48 Gy/3-4 (26%, n = 19), and 50/55 Gy/5 (22%, n = 16). SABR was well-tolerated, with one grade 5 toxicity (death 10 days after SABR with COVID-19) and five grade 2-3 toxicities. Following SABR, 26 patients underwent resection thus far (13 pending surgery). Median time-to-surgery was 4.5 months post-SABR (range, 2-17.5 months). Surgery was reported as being more difficult because of SABR in 38% (n = 10) of cases. Thirteen patients (50%) had pCR and 19 (73%) had MPR. Rates of pCR trended higher in patients operated on at earlier time points (75% if within 3 months, 50% if 3-6 months, and 33% if ≥6 months; p = .069). In the exploratory best-case scenario analysis, pCR rate does not exceed 82%. CONCLUSIONS: The SABR-BRIDGE approach allowed for delivery of treatment during a period of operating room closure and was well-tolerated. Even in the best-case scenario, pCR rate does not exceed 82%.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".