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

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.

The Guiding Significance of the Number of Positive Sentinel Lymph Nodes in Frozen Section for Intraoperative Axillary Dissection in Early Breast Cancer

“…We further analyzed the clinicopathological characteristics of these 31 patients and found that HER2 status (p<0.03) and Ki67 (p<0.02) were significant factors affecting the accuracy rate of the number of positive SLNs ( Table 5 ). Fanizzi et al 23 found that the prognostic factors HER2 and Ki67 can help to improve the predictive performance of the CancerMath (CM) model for SLN positive. These results suggest that the HER2 positive and the high expression of Ki67 may be associated with SLN metastasis.…”

Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs

“…In the state-of-the-art, there are a number of works that make use of predictive models or radiomic analysis to identify and characterize breast neoplasms and the state of lymph node involvement [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ].…”

Ultrasound guided needle biopsy of axilla to evaluate nodal metastasis after preoperative systemic therapy in cohort of 106 breast cancers enriched with BRCA1/2 pathogenic variant carriers

“…However, as showed by experimental results by Fanizzi et al, CancerMath is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients. And again, it should be emphasized that those tools are not validated in patients after PST, which is the target group of our study [ 30 ].…”