More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow‐up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco‐regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%‐96.86%), sensitivity (=0.35‐0.64), specificity (=0.97‐0.99), and AUC (=0.804‐0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow‐up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging.

Background: Systemic inflammatory markers draw great interest as potential blood-based prognostic factors in several oncological settings. Objectives: The aim of this study is to evaluate whether neutrophil-to-lymphocyte ratio (NLR) and pan-immune-inflammation value (PIV) predict nodal pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in node-positive (cN+) breast cancer (BC) patients. Design: Clinically, cN+ BC patients undergoing NAC followed by breast and axillary surgery were enrolled in a multicentric study from 11 Breast Units. Methods: Pretreatment blood counts were collected for the analysis and used to calculate NLR and PIV. Logistic regression analyses were performed to evaluate independent predictors of nodal pCR. Results: A total of 1274 cN+ BC patients were included. Nodal pCR was achieved in 586 (46%) patients. At multivariate analysis, low NLR [odds ratio (OR) = 0.71; 95% CI, 0.51–0.98; p = 0.04] and low PIV (OR = 0.63; 95% CI, 0.44–0.90; p = 0.01) were independently predictive of increased likelihood of nodal pCR. A sub-analysis on cN1 patients ( n = 1075) confirmed the statistical significance of these variables. PIV was significantly associated with axillary pCR in estrogen receptor (ER)−/human epidermal growth factor receptor 2 (HER2)+ (OR = 0.31; 95% CI, 0.12–0.83; p = 0.02) and ER−/HER2− (OR = 0.41; 95% CI, 0.17–0.97; p = 0.04) BC patients. Conclusion: This study found that low NLR and PIV levels predict axillary pCR in patients with BC undergoing NAC. Registration: Eudract number NCT05798806.