Axillary Pathological Complete Response After Neoadjuvant Therapy in cN1–2 Breast Cancer: An Internally Validated PET/CT-Integrated Nomogram
Bibliographic record
Abstract
We aimed to develop and internally validate a nomogram to estimate axillary pathological complete response (pCR, ypN0) after neoadjuvant systemic therapy (NAST) in clinically node-positive (cN1–2) breast cancer. In a single-center retrospective cohort of 144 consecutive patients treated with NAST (anti-HER2 as indicated), all underwent standardized pre- and post-NAST 18F-FDG PET/CT and axillary staging (sentinel lymph node biopsy [SLNB], targeted axillary dissection [TAD], or axillary lymph node dissection [ALND]). Axillary pCR occurred in 51.4% (74/144). In a multivariable analysis, independent positive determinants of ypN0 included the triple-negative subtype, Modified PERCIST (SUVmax-based) reduction ≥ 80.70%, pre-NAST tumor-to-axilla SUVmax ratio ≥ 1.21, and residual breast tumor size < 0.5 mm; conversely, conglomerate/matted nodal morphology at diagnosis was inversely associated. The model showed good internal discrimination (AUC 0.857, 95% CI 0.797–0.917) and acceptable calibration (Hosmer–Lemeshow p = 0.425). Exploratory, subtype-restricted signals were observed for inflammatory indices within Luminal B (HER2+) but were not retained in the final model. The resulting nomogram—combining tumor biology, PET/CT response, and pre-NAST nodal features—may support risk stratification for axillary de-escalation after NAST; however, prospective external validation—ideally embedded in ongoing de-escalation frameworks—remains essential before clinical implementation, and the tool should currently be regarded as hypothesis-generating rather than a stand-alone decision aid for routine practice.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".