Accepted for/Published in: JMIR Research Protocols
Date Submitted: Aug 16, 2025
Date Accepted: Dec 24, 2025
Mathematical Prediction Models (Excluding Machine Learning) for Sentinel Node Status in Early Breast Cancer: A Systematic Review Protocol
ABSTRACT
Background:
The status of the axilla remains a significant prognostic factor and influences adjuvant systemic and locoregional treatment choices in early-stage breast cancer (EBC). Sentinel node biopsy (SNB) continues to be the preferred technique for establishing axillary nodal status in clinically node-negative EBC. A non-invasive alternative to SNB is sought but has yet to be identified. A multivariable prediction model with adequate accuracy and generalisability could replace SNB, with numerous such models proposed. Interpreting the results of a multivariable prediction model is challenging yet crucial for assessing its usefulness in the relevant population. This systematic review aims to evaluate the quality and risk of bias associated with the mathematical models (MM) for predicting sentinel node status in EBC patients.
Objective:
1. List the available MMs. 2. Determine the key predictive factors influencing sentinel node status prediction in MMs. 3. Evaluate the methodological adequacy and suitability of individual MMs. 4. Compare the reported predictive performances of methodologically robust MMs.
Methods:
A search will be conducted in PubMed, Cochrane Central, and Embase to identify studies that report the development of a sentinel node status prediction model. Only studies that report the sentinel node status using mathematical modelling techniques will be included. Two independent reviewers will screen the search results and extract data from the articles that are included.
Results:
Protocol only
Conclusions:
Nomogram-based prediction models have the potential to guide selective omission of sentinel node biopsy, offering a safe and evidence-based alternative in low-risk patients. This systematic review, therefore, provides timely and clinically meaningful insight into an area of growing importance. Clinical Trial: International Prospective Register of Systematic Reviews (PROSPERO) on 23/01/2025
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.