Axillary Management in Breast Cancer Patients Undergoing Upfront Surgery: Results from a Nationwide Survey on Behalf of the Clinical Oncology Breast Cancer Group (COBCG) and the Breast Cancer Study Group of the Italian Association of Radiotherapy and Clinical Oncology (AIRO)
Bibliographic record
Abstract
Background: We assessed the current practice concerning the axillary management of breast cancer (BC) patients undergoing upfront surgery among radiation oncologists (ROs) practising in Italy. Methods: An online survey via SurveyMonkey (including 21 questions) was distributed amongst ROs in Italy through personal contacts and the Italian Association for Radiotherapy and Clinical Oncology (AIRO) network from August to September 2022. We particularly focused on the emerging omission of axillary lymph node dissection (ALND) in the presence of 1–2 sentinel node-positive patients and the consequent change in the role of regional nodal irradiation (RNI). Results: A total of 101/195 (51% response rate) Italian Radiotherapy Cancer Care Centres answered the survey. With respect to patients with 1–2 sentinel node-positive, the relative proportion of respondents that offer patients ALND a) always, b) only in selected cases, and c) never was 37.6%, 60.4%, and 2.0%, respectively, with no significant geographical (North vs. Centre–South Italy; p = 0.92) or institutional (Academic vs. non-Academic; p = 0.49) differences. Radiation therapy indications varied widely in patients who did not undergo ALND. Among these, about a third of the respondents (17/56, 30.4%) stated that RNI was constantly performed. On the other hand, half of the respondents offered RNI in selected cases, stating that an unfavourable biologic tumour profile and extracapsular nodal extension were considered drivers of their decision. Conclusions: Results of the present survey show the variability of axillary management offered in clinical practice for BC patients undergoing conserving surgery upfront in Italy. Analysis of these attitudes may trigger the modification of some clinical approaches through multidisciplinary collaboration and create the background for future clinical investigations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".