Enrique Díaz-Cantón*

Instituto de Oncología, Instituto Universitario CEMIC; Postgraduate Program in Artificial Intelligence and Medicine, Academia Nacional de Medicina, Buenos Aires, Argentina

*Corresponding Author: Enrique Díaz-Cantón, Instituto de Oncología, Instituto Universitario CEMIC; Postgraduate Program in Artificial Intelligence and Medicine, Academia Nacional de Medicina, Buenos Aires, Argentina.

Received: April 20, 2026 Published: May 06, 2026

Abstract

Background: Three landmark phase III randomized trials—SOUND, INSEMA, and BOOG 2013-08—have demonstrated the oncological safety of omitting sentinel lymph node biopsy (SLNB) in select patients with clinically node-negative (cN0), hormone receptor-positive (HR+), HER2-negative early breast cancer. The 2025 American Society of Clinical Oncology (ASCO) guideline formally endorses SLNB omission in postmenopausal women aged ≥50 years with low-grade, small tumors undergoing breast-conserving therapy. This paradigm shift creates an unresolved clinical problem: the medical oncologist inherits a patient with unknown nodal status (Nx) precisely when adjuvant therapy options increasingly depend on pathological nodal information.

Key Issues: Nodal status remains central to eligibility for adjuvant abemaciclib (monarchE), ribociclib (NATALEE), and olaparib (OlympiA); to chemotherapy decisions in premenopausal women with node-positive disease (RxPONDER); and to extended endocrine therapy duration. The "blind axilla" paradox describes a scenario in which surgical de-escalation is evidence-based but may inadvertently limit access to survival-improving systemic therapies.

Genomic Platforms and Artificial Intelligence: Genomic assays (Oncotype DX, MammaPrint, Prosigna, EndoPredict, Breast Cancer Index) provide critical biological information but were not designed to replace nodal staging, and some explicitly require nodal input for their validated algorithms. Emerging artificial intelligence (AI) models—including deep learning applied to mammography, ultrasound, and MRI—show promise for non-invasive axillary risk estimation (AUCs 0.69–0.91) but lack prospective clinical validation.

Therapeutic Implications: A decision framework for Nx patients must integrate tumor biology, genomic risk, imaging-based AI risk scores, and shared decision-making within multidisciplinary teams. The OFSET trial (NRG-BR009) may resolve whether ovarian function suppression substitutes for chemotherapy in premenopausal N1 patients, but results are not expected before 2034.

Conclusion: The blind axilla represents a genuine clinical paradox demanding coordinated solutions spanning surgery, medical oncology, genomics, and artificial intelligence. Until non-invasive tools achieve sufficient accuracy for clinical implementation, a thoughtful individualized approach guided by multidisciplinary expertise remains the standard of care.

Keywords: Blind Axilla; Sentinel Lymph Node Biopsy Omission; Surgical De-Escalation; Breast Cancer; Adjuvant Therapy; Genomic Assays; Artificial Intelligence; Axillary Staging; CDK4/6 Inhibitors; Nodal Status

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Citation

Citation: Enrique Díaz-Cantón. “Blind by Design: How Axillary Surgical De-escalation Left the Medical Oncologist Without a Map". Acta Scientific Cancer Biology 10.3 (2026): 15-25.

Copyright

Copyright: © 2026 Enrique Díaz-Cantón. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.