Objective: To develop a nomogram and validate its use for the intraoperative evaluation of nodal metastasis using shear-wave elastography (SWE) elasticity values and nodal size Methods: We constructed a nomogram to predict metastasis using ex vivo SWE values and ultrasound features of 228 axillary LNs from fifty-five patients. We validated its use in an independent cohort comprising 80 patients. In the validation cohort, a total of 217 sentinel LNs were included. Results: We developed the nomogram using the nodal size and elasticity values of the development cohort to predict LN metastasis; the area under the curve (AUC) was 0.856 (95% confidence interval (CI), 0.783–0.929). In the validation cohort, 15 (7%) LNs were metastatic, and 202 (93%) were non-metastatic. The mean stiffness (23.54 and 10.41 kPa, p = 0.005) and elasticity ratio (3.24 and 1.49, p = 0.028) were significantly higher in the metastatic LNs than those in the non-metastatic LNs. However, the mean size of the metastatic LNs was not significantly larger than that of the non-metastatic LNs (8.70 mm vs 7.20 mm, respectively; p = 0.123). The AUC was 0.791 (95% CI, 0.668–0.915) in the validation cohort, and the calibration plots of the nomogram showed good agreement. Conclusions: We developed a well-validated nomogram to predict LN metastasis. This nomogram, mainly based on ex vivo SWE values, can help evaluate nodal metastasis during surgery. Key Points: • A nomogram was developed based on axillary LN size and ex vivo SWE values such as mean stiffness and elasticity ratio to easily predict axillary LN metastasis during breast cancer surgery. • The constructed nomogram presented high predictive performance of sentinel LN metastasis with an independent cohort. • This nomogram can reduce unnecessary intraoperative frozen section which increases the surgical time and costs in breast cancer patients.
Bibliographical noteFunding Information:
This work was supported by a grant from the research fund of Yonsei University Medical College, Seoul, Republic of Korea (6-2014-0199).
© 2019, The Author(s).
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging