J Korean Med Sci.  2023 Aug;38(34):e251. 10.3346/jkms.2023.38.e251.

Value of Breast MRI and Nomogram After Negative Axillary Ultrasound for Predicting Axillary Lymph Node Metastasis in Patients With Clinically T1-2 N0 Breast Cancer

• Song SE
• Cho KR
• Cho Y
• Jung SP
• Park KH
• Woo OH
• Seo BK

• KMID: 2545540
• DOI: http://doi.org/10.3346/jkms.2023.38.e251

Abstract

Background
There are increasing concerns about that sentinel lymph node biopsy (SLNB) could be omitted in patients with clinically T1-2 N0 breast cancers who has negative axillary ultrasound (AUS). This study aims to assess the false negative result (FNR) of AUS, the rate of high nodal burden (HNB) in clinically T1-2 N0 breast cancer patients, and the diagnostic performance of breast magnetic resonance imaging (MRI) and nomogram.
Methods
We identified 948 consecutive patients with clinically T1-2 N0 cancers who had negative AUS, subsequent MRI, and breast conserving therapy between 2013 and 2020 from two tertiary medical centers. Patients from two centers were assigned to development and validation sets, respectively. Among 948 patients, 402 (mean age ± standard deviation, 57.61 ± 11.58) were within development cohort and 546 (54.43 ± 10.02) within validation cohort. Using logistic regression analyses, clinical-imaging factors associated with lymph node (LN) metastasis were analyzed in the development set from which nomogram was created. The performance of MRI and nomogram was assessed. HNB was defined as ≥ 3 positive LNs.
Results
The FNR of AUS was 20.1% (81 of 402) and 19.2% (105 of 546) and the rates of HNB were 1.2% (5/402) and 2.2% (12/546), respectively. Clinical and imaging features associated with LN metastasis were progesterone receptor positivity, outer tumor location on mammography, breast imaging reporting and data system category 5 assessment of cancer on ultrasound, and positive axilla on MRI. In validation cohorts, the positive predictive value (PPV) and negative predictive value (NPV) of MRI and clinical-imaging nomogram was 58.5% and 86.5%, and 56.0% and 82.0%, respectively.
Conclusion
The FNR of AUS was approximately 20% but the rate of HNB was low. The diagnostic performance of MRI was not satisfactory with low PPV but MRI had merit in reaffirming negative AUS with high NPV. Patients who had low probability scores from our clinical-imaging nomogram might be possible candidates for the omission of SLNB.

Keyword

Breast Neoplasms; Axilla; Ultrasonography; Lymphatic Metastasis; Nomogram

Figure

• 

  Fig. 1 Flow diagram of the study population.US = ultrasound, MRI = magnetic resonance imaging.

• 

  Fig. 2 Findings in a 53-year-old woman with clinically T1N0 breast cancer in her left breast. (A) Breast US shows a 1.3-cm mass with breast imaging reporting and data system category 5 assessment (arrows). (B) Axillary US shows a benign looking LN with an assement of negative axilla (arrows). (C) Axial T1-weighted postcontrast MRI covering entire axilla shows a suspicious metastatic LN (arrow) which was enlarged with cortical thickening, round shape, and a long axis to short axis ratio of less than two at level I of the left axilla. (D) Right craniocaudal view of mammography shows a mass without calcifications (arrow) in the outer breast. (E) Axial T1-weighted postcontrast MRI shows an irregular shaped and marginated, heterogeneously enhancing mass in her left breast.US = ultrasound, LN = lymph node, MRI = magnetic resonance imaging.

• 

  Fig. 3 Clinical-imaging nomogram. Each point that corresponds to each feature is on the uppermost point scale, and the sum of all points is the total points. The total points projected at the bottom scale indicate the probability of LN metastasis.MG = mammography, BI-RADS = breast imaging reporting and data system, US = ultrasound, MRI = magnetic resonance imaging, LN = lymph node.

• 

  Fig. 4 Receiver operating characteristic curves (AUCs) of the clinical-imaging nomogram to predict axillary lymph node metastasis in development and validation sets. Numbers in parentheses are AUCs.AUC = area under the curve, CI = confidence interval.

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