Uncovering the armpit of SBRT: An institutional experience with stereotactic radiation of axillary metastases
Bibliographic record
Abstract
Purpose /Objective(s): The growing use of stereotactic body radiotherapy (SBRT) in metastatic cancer has led to its use in varying anatomic locations. The objective of this study was to review our institutional SBRT experience for axillary metastases (AM), focusing on outcomes and process. Materials /Methods: Patients treated with SBRT to AM from 2014-2022 were reviewed. Cumulative incidence functions were used to estimate the incidence of local failure (LF), with death as competing risk. Kaplan-Meier method was used to estimate progression-free (PFS) and overall survival (OS). Univariate regression analysis examined predictors of LF. Results We analyzed 37 patients with 39 AM who received SBRT. Patients were predominantly female (60%) and elderly (median age: 72). Median follow-up was 14.6 months. Common primary cancers included breast (43%), skin (19%), and lung (14%). Treatment indication included oligoprogression (46%), oligometastases (35%) and symptomatic progression (19%). A minority had prior overlapping radiation (18%) or surgery (11%). Most had prior systemic therapy (70%). Significant heterogeneity in planning technique was identified; a minority of patient received 4-D CT scans (46%), MR-simulation (21%), or contrast (10%). Median dose was 40Gy (interquartile range (IQR): 35-40) in 5 fractions, (BED 10 =72Gy). Seventeen cases (44%) utilized a low-dose elective volume to cover remaining axilla. At first assessment, 87% had partial or complete response, with a single progression. Of symptomatic patients (n=14), 57% had complete resolution and 21% had improvement. One and 2-year LF rate were 16% and 20%, respectively. Univariable analysis showed increasing BED reduced risk of LF. Median OS was 21.0 months (95% [Confidence Interval (CI)] 17.3-not reached) and median PFS was 7.0 months (95% [CI] 4.3-11.3). Two grade 3 events were identified, and no grade 4/5. Conclusion Using SBRT for AM demonstrated low rates of toxicity and LF, and respectable symptom improvement. Variation in treatment delivery has prompted development of an institutional protocol to standardize technique and increase efficiency. Limited followup may limit detection of local failure and late toxicity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".