Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

Simple Summary

Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients.

Abstract

In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.

Prognostic Effect of Neck Dissection and Risk Factors for Occult Lymph Node Metastasis in cN0 Parotid Carcinoma

Objective

This study aimed to explore the prognostic effect of neck dissection and to identify risk factors associated with occult lymph node metastasis (OLNM) in clinically node-negative (cN0) parotid carcinoma (PC).

Methods

A retrospective analysis was conducted on cN0 PC patients who underwent primary surgery at the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, between 2012 and 2022. Kaplan-Meier (KM) survival analyses were carried out to evaluate differences in progression-free survival (PFS) and overall survival (OS) between patients undergoing neck dissection and those who did not. Clinical variables associated with OLNM in the neck dissection group were assessed using univariate and multivariate logistic regression analyses.

Results

Among 472 PC patients, 133 were classified as cN0 following initial surgery, of whom 75 (56.4%) underwent neck dissection. Pathological lymph node metastases were confirmed in 20 (26.7%) patients in the neck dissection cohort. Poor tumor differentiation was identified as an independent risk factor for OLNM (p = 0.017). No significant differences in PFS or OS were observed between the no-neck dissection and neck dissection groups for patients with low-grade or well-differentiated tumors (p > 0.05). However, neck dissection was associated with significantly prolonged PFS in patients with tumors of higher grade or low to moderate differentiation (p < 0.05). Notably, OS did not improve with neck dissection across all subgroups (p > 0.05).

Conclusion

Poorly differentiated tumors in cN0 PC are independently associated with a higher risk of OLNM. While prophylactic neck dissection may enhance PFS in patients with higher grade or poorly differentiated tumors, it does not confer a survival benefit in terms of OS. These findings support the selective use of neck dissection in patients with higher risk tumor profiles.