Clinicogenomic Radiotherapy Classifier Predicting the Need for Intensified Locoregional Treatment After Breast-Conserving Surgery for Early-Stage Breast Cancer
Bibliographic record
Abstract
PURPOSE Most patients with early-stage breast cancer are treated with adjuvant radiotherapy (RT) after breast-conserving surgery (BCS) to prevent locoregional recurrence (LRR). However, no genomic tools are used currently to select the optimal RT strategy. METHODS We profiled the transcriptome of primary tumors on a clinical grade assay from the SweBCG91-RT trial, in which patients with node-negative breast cancer were randomly assigned to either whole-breast RT after BCS or no RT. We derived a new classifier, Adjuvant Radiotherapy Intensification Classifier (ARTIC), comprising 27 genes and patient age, in three publicly available cohorts, then independently validated ARTIC for LRR in 748 patients in SweBCG91-RT. We also compared previously published genomic signatures for ability to predict benefit from RT in SweBCG91-RT. RESULTS ARTIC was highly prognostic for LRR in patients treated with RT (hazard ratio [HR], 3.4; 95% CI, 2.0 to 5.9; P < .001) and predictive of RT benefit ( P interaction = .005). Patients with low ARTIC scores had a large benefit from RT (HR, 0.33 [95% CI, 0.21 to 0.52], P < .001; 10-year cumulative incidence of LRR, 6% v 21%), whereas those with high ARTIC scores benefited less from RT (HR, 0.73 [95% CI, 0.44 to 1.2], P = .23; 10-year cumulative incidence of LRR, 25% v 32%). In contrast, none of the eight previously published signatures were predictive of benefit from RT in SweBCG91-RT. CONCLUSION ARTIC identified women with a substantial benefit from RT as well as women with a particularly elevated LRR risk in whom whole-breast RT was not sufficiently effective and, thus, in whom intensified treatment strategies such as tumor-bed boost, and possibly regional nodal RT, should be considered. To our knowledge, ARTIC is the first classifier validated as predictive of benefit from RT in a phase III clinical trial with patients randomly assigned to receive or not receive RT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".