New Prediction Tool Could Spare Breast Cancer Patients Unnecessary Lymph Node Surgery

One of the most consequential decisions in early breast cancer surgery is whether a patient’s armpit lymph nodes need to be examined at all, and how extensively. A new study from Fundación Jiménez Díaz University Hospital in Madrid offers clinicians a data-driven answer for one of the most common tumor subtypes: hormone receptor-positive, HER2-negative breast cancer that appears node-negative on clinical examination. A team led by Jeison Carrillo-Peña and Pedro Villarejo-Campos has built and internally validated a preoperative nomogram, a points-based statistical tool that estimates the probability that cancer has silently spread to the axillary lymph nodes before a single incision is made. The work, published open access in BMC Cancer, arrives at a moment when breast surgeons worldwide are actively trying to reduce the scope of axillary operations without compromising oncological safety.

The clinical problem the researchers tackled is deceptively simple to state but stubbornly difficult to solve. When a woman is diagnosed with invasive breast cancer, determining whether the disease has reached the regional lymph nodes is central to staging, adjuvant therapy planning, and surgical strategy. The current standard for patients whose nodes feel normal and look normal on ultrasound is the sentinel lymph node biopsy, a technique that identifies and removes the first draining node or nodes so they can be examined under the microscope. Yet even this minimally invasive procedure carries real costs: lymphedema, seroma formation, numbness, restricted shoulder mobility, and the anxiety of a second operation when metastases are found. For a substantial fraction of patients, that biopsy ultimately confirms no nodal involvement at all, meaning the procedure was, from a purely diagnostic standpoint, avoidable.

The Madrid cohort comprised 540 consecutive patients with invasive HR+/HER2- breast cancer treated between January 2019 and June 2024, all of whom underwent breast surgery with formal axillary staging. This tumor subtype is the single largest biological category of breast cancer, driven by estrogen or progesterone signaling rather than HER2 amplification, and typically managed with endocrine therapy, chemotherapy in selected cases, and surgery. Because these tumors often behave in a more indolent fashion than triple-negative or HER2-positive disease, the question of which patients can safely forgo parts of the axillary workup is particularly pressing. In this cohort, 173 patients, or 32.0 percent, turned out to have nodal disease despite being classified as clinically node-negative before surgery, a figure that underscores how much microscopic spread evades conventional preoperative assessment.

To build the prediction model, the team assembled a rich set of preoperative variables spanning clinical examination, imaging, and core needle biopsy pathology: tumor size, multifocality or multicentricity, histological type and grade, lymphovascular invasion, perineural invasion, hormone receptor and Ki-67 values, and the findings of axillary ultrasound. Rather than relying on univariate associations, which can be confounded by correlations among variables, the investigators used multivariable logistic regression with LASSO regularization. LASSO, short for least absolute shrinkage and selection operator, is a statistical technique that penalizes model complexity, shrinking weak coefficients toward zero and effectively eliminating predictors that add little independent information. This approach guards against overfitting, the failure mode in which a model memorizes the idiosyncrasies of its development dataset and then performs poorly on new patients.

The variables that survived LASSO selection tell a coherent biological story. By far the strongest predictor was a suspicious axillary ultrasound, which carried an odds ratio of 16.42, meaning patients with abnormal-appearing nodes on imaging had more than a sixteenfold increase in the odds of harboring metastases compared with those whose ultrasound was unremarkable. Lymphovascular invasion on the preoperative biopsy, an indication that tumor cells have already entered lymphatic or blood vessels within the primary tumor, roughly doubled the odds at an odds ratio of 2.61. Each additional millimeter of tumor size increased the odds by about 4 percent, and multifocal or multicentric disease, where multiple tumor foci are present in the breast, raised the odds by 88 percent. Histological grade was retained in the final model for reasons of clinical relevance even though its statistical contribution was more modest.

Performance metrics matter as much as the predictors themselves, and here the nomogram delivered solid numbers. Discrimination, the model’s ability to separate patients who have nodal disease from those who do not, was quantified by the area under the receiver operating characteristic curve, which reached 0.83 with a 95 percent confidence interval of 0.79 to 0.87. In practical terms, an AUC of 0.83 means that if the tool were given one patient with nodal involvement and one without, it would assign the higher risk score to the correct patient roughly 83 percent of the time, a level generally regarded as good for a clinical prediction model built on routine preoperative data. Calibration, which asks whether predicted probabilities match observed frequencies across the risk spectrum, was equally reassuring: the expected-to-observed ratio was 1.002, essentially perfect, and the Brier score, a composite measure combining discrimination and calibration, was 0.135, comfortably below the 0.25 that would correspond to uninformative guessing.

A nomogram translates these regression coefficients into a visual scoring system that clinicians can use at the bedside. Each predictor is assigned a point value on a scale, the points for all variables are summed, and the total maps onto a predicted probability of nodal involvement. The Madrid team went further by calculating diagnostic performance across multiple probability thresholds, reporting positive and negative predictive values at each cut-off. This threshold analysis is what makes the tool actionable rather than merely descriptive: a hospital could, for example, define a low-risk band below which sentinel biopsy might be omitted or deferred, or a high-risk band above which more intensive axillary evaluation or direct discussion of nodal therapy could be considered. The authors explicitly frame the nomogram as a support for individualized risk stratification within the broader movement toward axillary surgical de-escalation.

That de-escalation movement has been reshaping breast surgery for two decades. Following landmark trials such as ACOSOG Z0011, which showed that many women with limited sentinel node metastases who undergo breast-conserving therapy do not benefit from completion axillary lymph node dissection, guidelines have progressively relaxed the requirement for radical nodal surgery in selected patients. More recently, trials of omitting sentinel biopsy altogether in older, low-risk patients have gained traction. The HR+/HER2- subtype is a natural focus for these efforts because modern systemic therapy, including endocrine treatment and CDK4/6 inhibitors for higher-risk disease, exerts systemic control regardless of nodal status, while the prognostic value of detecting a single micrometastasis has become increasingly debatable. A reliable preoperative risk estimate feeds directly into these shared decision-making conversations between surgeon and patient.

The study’s limitations are the ones that typically attend retrospective, single-institution models, and the authors are candid about them. All 540 patients came from one Madrid hospital, raising the possibility that referral patterns, imaging protocols, or pathology practices specific to that center could limit generalizability. The retrospective design means some variables, such as tumor-infiltrating lymphocytes or molecular assays not routinely ordered preoperatively, could not be incorporated. Most importantly, the model has not yet been tested on patients outside its development cohort. External validation, ideally in prospective multicenter cohorts drawn from different health systems, is required before any clinical implementation, and the authors state this requirement explicitly in their conclusions. History is littered with prediction models that performed beautifully in derivation and faded on external testing, which is why the field increasingly demands validation before publication of clinical tools is celebrated.

Even with those caveats, the contribution is timely and practical. Roughly one in three clinically node-negative HR+/HER2- patients in this cohort had occult nodal disease, and the strongest signal, the axillary ultrasound, is already obtained in most modern breast units, meaning the nomogram requires no new tests, no additional cost, and no delay to surgery. If external validation confirms the Madrid results, the tool could help identify the substantial minority of patients whose probability of nodal involvement is so low that even a sentinel procedure might reasonably be omitted, sparing them the attendant risks of lymphedema and nerve injury. Conversely, it could flag patients whose risk justifies a more thorough preoperative discussion about axillary management. In an era when precision oncology is often equated with genomic sequencing, this study is a reminder that careful statistical modeling of everyday clinical variables can still move the needle on surgical care, one calculated probability at a time.

Subject of Research: Development of a preoperative nomogram predicting axillary lymph node involvement in clinically node-negative HR+/HER2- breast cancer

Article Title: A preoperative nomogram to predict axillary lymph node involvement in clinically node-negative HR+/HER2- breast cancer

Article References: Carrillo-Peña, J., Osorio-Silla, I., Mahillo-Fernández, I., Sánchez De Molina-Rampérez, M. L., Escanciano-Escanciano, M., Pastor-Peinado, P., Guadalajara, H., García-Olmo, D., & Villarejo-Campos, P. (2026). A preoperative nomogram to predict axillary lymph node involvement in clinically node-negative HR+/HER2- breast cancer. BMC Cancer. https://doi.org/10.1186/s12885-026-17128-2

Image Credits: AI Generated

DOI: 10.1186/s12885-026-17128-2

Keywords: breast cancer, axillary lymph nodes, nomogram, sentinel lymph node biopsy, HR+/HER2-, surgical de-escalation, lymphovascular invasion, axillary ultrasound, prediction model, LASSO regression, risk stratification, BMC Cancer