A balanced view of imaging and molecular markers for predicting axillary lymph node metastasis in T1 breast cancer

With great interest, we have read the study conducted by Shang et al. (1), published in Quantitative Imaging in Medicine and Surgery. The authors present a novel approach to preoperatively predict axillary lymph node metastasis (ALNM) in T1 breast cancer, which holds promise for avoiding unnecessary axillary surgery and enhancing individualized treatment. However, we would like to highlight several key aspects that could further strengthen the study’s findings and their clinical implications.

The axillary lymph node (ALN) status is a critical prognostic factor for breast cancer patients. The Z0011 Trial has yielded promising results (2), significantly altering the initial clinical management of clinically node-negative breast cancer patients. Furthermore, the latest American Society of Clinical Oncology (ASCO) Guideline has updated the axilla management for patients with clinical T ≤2 cm breast cancer (3). Three major imaging modalities for breast cancer detection are mammography, ultrasound, and magnetic resonance imaging (MRI). However, these methods have certain limitations: mammography offers limited information mainly about the anterior axilla, and ultrasound is highly operator-dependent resulting in false-negative rates in axillary ultrasound. As for MRI, despite its superior diagnostic capabilities, it may not be feasible for patients with implantable devices. Integrating multi-modal imaging is expected to provide more robust approaches to predicting ALN status. The study by Shang et al. did not compare the new techniques with traditional imaging methods such as ultrasound, mammography, and MRI. Therefore, it is challenging to highlight the advantages of the new techniques in diagnosing lymph node metastasis and their potential for clinical application.

We have also reviewed relevant data on ALNM in T1 breast cancer. Zhao et al. conducted a study based on the Surveillance, Epidemiology, and End Results Program, finding that among 91,364 eligible patients, only 3,819 (4.18%) were ALN positive (4). Li et al. developed and validated a simple nomogram to predict lymph node metastasis in early-stage breast cancer patients, reporting ALNM rates in T1 breast cancer patients of 17.0% and 25.6% in the primary cohort and the validation cohort, respectively (5). However, in Shang’s study, despite the patients being in the T1 stage, the ALNM rate was as high as 45.9%. The authors should clarify whether the included patients had indications of lymph node enlargement or suspicious lymph nodes on preoperative evaluation.

The diffuse optical tomography technique has limitations, such as the inability to measure lesions at certain depths due to the limited penetration depth of near-infrared light. Additionally, as a single-center study with a relatively small sample size, the results may not be generalizable and require validation in multicenter studies with larger samples. The study also did not consider other potential factors that may influence ALNM, such as lymphovascular invasion. Although the combined model showed good predictive performance, further investigation into its underlying mechanisms may be needed.

Lastly, the study was conducted in 2012, and over a decade has passed since then. With such a long time span, it would have been valuable to follow up with the cohort to assess outcomes like axillary recurrence-free survival, regional recurrence-free survival, and overall survival. If the new techniques could improve the survival of T1 breast cancer patients, it would enhance the applicability of the research results in clinical practice for breast surgeons.

In conclusion, the study by Shang et al. provides valuable insights into predicting ALNM in T1 breast cancer. Combining imaging and molecular markers offers a promising approach for preoperative ALNM assessment. However, further research is needed to address the study’s limitations and refine the predictive model.

Appendix 1: Response to “A balanced view of imaging and molecular markers for predicting axillary lymph node metastasis in T1 breast cancer”.

Acknowledgments

The authors thank Editage for English language editing.

Footnote

Funding: This study was supported by the National High Level Hospital Clinical Research Funding (grant number 2022-PUMCH-B-064).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1293/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Shang C, Zhang J, Huang Y. Prediction of axillary lymph node metastasis in T1 breast cancer using diffuse optical tomography, strain elastography and molecular markers. Quant Imaging Med Surg 2025;15:2162-74. [Crossref] [PubMed]
2. Giuliano AE, Ballman KV, McCall L, Beitsch PD, Brennan MB, Kelemen PR, Ollila DW, Hansen NM, Whitworth PW, Blumencranz PW, Leitch AM, Saha S, Hunt KK, Morrow M. Effect of Axillary Dissection vs No Axillary Dissection on 10-Year Overall Survival Among Women With Invasive Breast Cancer and Sentinel Node Metastasis: The ACOSOG Z0011 (Alliance) Randomized Clinical Trial. JAMA 2017;318:918-26. [Crossref] [PubMed]
3. Park KU, Somerfield MR, Anne N, Brackstone M, Conlin AK, Couto HL, Dengel LT, Eisen A, Harvey BE, Hawley J, Kim JN, Lasebikan N, McDonald ES, Pradhan D, Shams S, Vega RM, Thompson AM, Torres MA. Sentinel Lymph Node Biopsy in Early-Stage Breast Cancer: ASCO Guideline Update. J Clin Oncol 2025;43:1720-41. [Crossref] [PubMed]
4. Zhao YX, Liu YR, Xie S, Jiang YZ, Shao ZM. A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program. J Cancer 2019;10:2443-9. [Crossref] [PubMed]
5. Li H, Tang L, Chen Y, Mao L, Xie H, Wang S, Guan X. Development and validation of a nomogram for prediction of lymph node metastasis in early-stage breast cancer. Gland Surg 2021;10:901-13. [Crossref] [PubMed]