Abstract
The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.
Highlights
The prediction of lymph node involvement in breast cancer represents an important task which could reduce unnecessary surgery and improve the definition of oncological therapies [1,2,3,4,5,6]
We report the results of a multivariate analysis aimed at developing a sentinel lymph nodes status predictive model for patients with and characterized by a clinically negative lymph node status
We considered the patients with clinically negative lymph nodes who did not have suspicious signs in axillary ultrasound, which is a routine examination during the presurgical staging phase of the armpit, or patients that resulted negative after a fine needle aspiration biopsy following the identification of axillary changes on instrumental examination
Summary
The prediction of lymph node involvement in breast cancer represents an important task which could reduce unnecessary surgery and improve the definition of oncological therapies [1,2,3,4,5,6]. The research of a trade-off strategy among time-consuming, expensive and invasive methodologies is the scientific goal of several research studies [7,8], especially with reference to breast cancer patients with clinically negative sentinel lymph nodes. For patients with clinically negative lymph nodes at the clinical or radiological exam [9], the current guidelines provide the removal of sentinel lymph node biopsy (SLNB). These are the first axillary draining lymph nodes, known as “sentinel” lymph nodes [10,11,12]. The SLNB is time-consuming and expensive, and may lead to complications such as wound infection, seroma, paraesthesia, lymphedema and hematoma [7,13,14,15,16,17].
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