Blinded assessment of radiological changes after stereotactic ablative radiotherapy (SABR) for early-stage lung cancer: Local recurrences versus fibrosis.
Bibliographic record
Abstract
7520 Background: Stereotactic ablative radiotherapy (SABR) is a guideline-recommended treatment for unfit patients with early-stage lung cancer. The 5-year local recurrence rates are approximately 10% but fibrotic changes are common during follow-up, leading to difficulty with timely detection and salvage therapies. Previously reported high-risk features (HRFs) on computed tomography (CT) are 1) enlarging opacity at the primary site; 2) sequential enlarging opacity; 3) enlarging opacity after 12 months; 4) bulging margin; 5) loss of linear margin and 6) loss of air bronchograms. We performed a blinded assessment of CT imaging of patients with and without local recurrences. Methods: Patients treated with SABR for early stage lung cancer between 2003 and 2012, who developed pathology-proven local recurrence (n=12), were matched 1:2 to patients without recurrences (n=24), based on baseline factors. The median age at diagnosis was 68 years and median post-SABR imaging follow-up was 24 months (range 6 to 67 months). Patients were well-matched in the recurrence and non-recurrence groups. A total of 153 CT scans were available. Serial CT images were assessed by 3 radiation oncologists blinded to outcomes, viewing anonymized images projected onto a large screen. Results: All established HRFs were significantly associated with local recurrence (p<0.01), and one additional HRF was identified: cranio-caudal growth (p<0.001). The best individual predictor of local recurrence was opacity enlargement after 12 months(100% sensitivity, 83% specificity, p<0.001). The odds of recurrence increased 4-fold for each additional HRF detected in an individual patient. The presence of ≥3 HRFs in an individual patient was highly sensitive and specific for recurrence (both >90%). The HRFs enlarging opacity and cranio-caudal growth were each detected ≥3 months prior to the actual diagnosis of local recurrence in 42% of patients. Conclusions: Local recurrences following SABR can be accurately predicted by the presence of HRF’s on post-treatment CT scans. This approach may reduce unnecessary diagnostic procedures, and ensure earlier use of salvage therapies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".