## 4 Discussion  
In patients with clinically node-negative breast cancer, current international guidelines require sentinel lymph node biopsy (SLNB) [11; 12; 13; 14]. Although it is currently the most-performed exam, it is a time consuming and expensive procedure and is also extremely invasive, and could result in a number of side effects [7; 18; 32]. However, in patients with early stage breast cancer the incidence of axillary metastases is low, therefore that procedure may be an unnecessary invasive procedure. The aim of our work was to develop a sentinel lymph nodes status predictive model for clinically negative patients that could replace the SLNB. Starting from a set of 907 patients, for each of them we collected several prognostic features, such as age, tumor size, histological subtype, estrogen receptor expression, progesterone receptor expression, histological grade, cellular marker for proliferation, human epidermal growth factor receptor-2, multiplicity, in situ component and also the sentinel lymph nodes status. Subsequently, we trained three different classifiers combined with a sequential forward feature selection algorithm on the hold-out training set to select the feature subset which reached the highest \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Features**} & _Cluster_ & \(1\) & 2 & \(3\) \\ \cline{2-5}  & **n** & **164** & **207** & **264** \\  & **(Pos/Neg)** & **(66/98)** & **(70/137)** & **(96/168)** \\ \hline \multirow{2}{*}{Histologic Type} & Ductal & 152 (92.7\%) & 146 (70.5\%) & 214 (81.1\%) \\  & Lobular & 5 (3.1\%) & 33 (15.9\%) & 29 (11.0\%) \\  & Special & 7 (4.3\%) & 28 (13.5\%) & 21 (8.0\%) \\  & T1a & 5 (3.1\%) & 12 (5.8\%) & 14 (5.3\%) \\  & T1b & 21 (12.8\%) & 62 (30.0\%) & 42 (15.9\%) \\ Diameter & T1c & 40 (24.4\%) & 94 (45.4\%) & 147 (55.7\%) \\  & T2 & 98 (59.8\%) & 39 (18.8\%) & 61 (23.1\%) \\ Age & Median & 57 & 61 & 61 \\  & Neg & 61 (37.2\%) & 0.0 & 3 (1.1\%) \\ ER & Pos & 103 (62.9\%) & 207 (100\%) & 261 (98.8\%) \\  & Neg & 112 (68.3\%) & 20 (9.7\%) & 15 (5.7\%) \\ PgR & Pos & 52 (31.7\%) & 187 (90.3\%) & 249 (94.3\%) \\  & Neg & 13 (7.9\%) & 154 (74.4\%) & 65 (24.6\%) \\ Kio7 & Pos & 151 (92.1\%) & 53 (25.6\%) & 199 (75.4\%) \\  & G1 & 10 (6.1\%) & 130 (62.8\%) & 35 (13.3\%) \\ Grading & G2 & 15 (9.1\%) & 71 (34.3\%) & 201 (76.1\%) \\  & G3 & 139 (84.8\%) & 6 (2.9\%) & 28 (10.6\%) \\  & 0 & 93 (56.7\%) & 177 (85.5\%) & 202 (76.5\%) \\ Her2/Neu & 1+ & 19 (11.6\%) & 26 (12.6\%) & 33 (12.5\%) \\  & 2+ & 20 (12.2\%) & 3 (1.4\%) & 23 (8.7\%) \\  & 3+ & 32 (19.5\%) & 1 (0.5\%) & 6 (2.3\%) \\ Multiplicity & Neg & 123 (75.0\%) & 177 (85.5\%) & 192 (72.7\%) \\  & Pos & 41 (25.0\%) & 30 (14.5\%) & 72 (27.3\%) \\ In Situ & Neg & 54 (32.9\%) & 153 (73.9\%) & 59 (22.3\%) \\  & Pos & 110 (67.1\%) & 54 (26.1\%) & 205 (77.7\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Frequency distribution (percentage) of each group identified by PAM algorithm on the overall hold-out training set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **\# Features** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline RF & 8 & 63.6 & 52.4 & 68.4 \\ Logistic Regression & 7 & 62.1 & 68.3 & 59.7 \\ Naïve Bayesian & 7 & 61.8 & 52.4 & 65.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance calculated on the hold-out test set by using the subset of features that maximized performance for each classifier. value of AUC. Then, we trained the three previously selected models on the hold-out test set and we compared their performances in terms of accuracy, sensitivity and specificity. On the hold-out training set, the best classification model was the Logistic Regression algorithm on a subset of seven features, i.e., tumor size, age, histologic type, grading, multiplicity, in situ component and Her2-neu status. This model reached a median AUC equals to 71.5%, a median accuracy of 67.9%, sensitivity equal to 69.4% and specificity of 66.9%.