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Prediction of high nodal burden in patients with sentinel node-positive luminal ERBB2-negative breast cancer
Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden; Department of Clinical Physiology and Nuclear Medicine, Skane University Hospital, Lund, Sweden.
Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden.
Cytel Inc, Sweden; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.
Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden; Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund, Sweden.
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2024 (English)In: JAMA Surgery, ISSN 2168-6254, E-ISSN 2168-6262, Vol. 159, no 12, p. E1-E10Article in journal (Refereed) Published
Abstract [en]

Importance: In patients with clinically node-negative (cN0) breast cancer and 1 or 2 sentinel lymph node (SLN) macrometastases, omitting completion axillary lymph node dissection (CALND) is standard. High nodal burden (≥4 axillary nodal metastases) is an indication for intensified treatment in luminal breast cancer; hence, abstaining from CALND may result in undertreatment.

Objective: To develop a prediction model for high nodal burden in luminal ERBB2-negative breast cancer (all histologic types and lobular breast cancer separately) without CALND.

Design, Setting, and Participants: The prospective Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial randomized patients 1:1 to CALND or its omission from January 2015 to December 2021 among adult patients with cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases across 5 European countries. The cohort was randomly split into training (80%) and test (20%) sets, with equal proportions of high nodal burden. Prediction models were developed by multivariable logistic regression in the complete luminal ERBB2-negative cohort and a lobular breast cancer subgroup. Nomograms were constructed. The present diagnostic/prognostic study presents the results of a prespecified secondary analysis of the SENOMAC trial. Herein, only patients with luminal ERBB2-negative tumors assigned to CALND were selected. Data analysis for this article took place from June 2023 to April 2024.

Exposure: Predictors of high nodal burden.

Main Outcomes and Measures: High nodal burden was defined as ≥4 axillary nodal metastases. The luminal prediction model was evaluated regarding discrimination and calibration.

Results: Of 1010 patients (median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden and 212 (21.0%) had lobular breast cancer. The model in the training set (n = 804) included number of SLN macrometastases, presence of SLN micrometastases, SLN ratio, presence of SLN extracapsular extension, and tumor size (not included in lobular subgroup). Upon validation in the test set (n = 201), the area under the receiver operating characteristic curve (AUC) was 0.74 (95% CI, 0.62-0.85) and the calibration was satisfactory. At a sensitivity threshold of ≥80%, all but 5 low-risk patients were correctly classified corresponding to a negative predictive value of 94%. The prediction model for the lobular subgroup reached an AUC of 0.74 (95% CI, 0.66-0.83).

Conclusions and Relevance: The predictive models and nomograms may facilitate systemic treatment decisions without exposing patients to the risk of arm morbidity due to CALND. External validation is needed.

Trial Registration: ClinicalTrials.gov Identifier: NCT02240472.

Place, publisher, year, edition, pages
American Medical Association (AMA), 2024. Vol. 159, no 12, p. E1-E10
National Category
Surgery Cancer and Oncology
Identifiers
URN: urn:nbn:se:umu:diva-232281DOI: 10.1001/jamasurg.2024.3944ISI: 001328636700009PubMedID: 39320882Scopus ID: 2-s2.0-85209767652OAI: oai:DiVA.org:umu-232281DiVA, id: diva2:1916719
Funder
Swedish Research CouncilSwedish Cancer SocietySwedish Society of MedicineThe Breast Cancer FoundationFamiljen Erling-Perssons StiftelseAvailable from: 2024-11-28 Created: 2024-11-28 Last updated: 2024-11-28Bibliographically approved

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