Elaboration of a nomogram to predict nonsentinel node status in breast cancer patients with positive sentinel node, intraoperatively assessed with one step nucleic amplification: Retrospective and validation phase

Background: Tumor-positive sentinel lymph node (SLN) biopsy results in a risk of non sentinel node metastases in micro-and macro-metastases ranging from 20 to 50%, respectively. Therefore, most patients underwent unnecessary axillary lymph node dissections. We have previously developed a mathematical model for predicting patient-specific risk of non sentinel node (NSN) metastases based on 2460 patients. The study reports the results of the validation phase where a total of 1945 patients were enrolled, aimed at identifying a tool that gives the possibility to the surgeon to choose intraoperatively whether to perform or not axillary lymph node dissection (ALND).Methods: The following parameters were recorded: Clinical: hospital, age, medical record number; Bio pathological: Tumor (T) size stratified in quartiles, grading (G), histologic type, lymphatic/vascular invasion (LVI), ER-PR status, Ki 67, molecular classification (Luminal A, Luminal B, HER-2 Like, Triple negative); Sentinel and non-sentinel node related: Number of NSNs removed, number of positive NSNs, cytokeratin 19 (CK19) mRNA copy number of positive sentinel nodes stratified in quartiles. A total of 1945 patients were included in the database. All patient data were provided by the authors of this paper.Results: The discrimination of the model quantified with the area under the receiver operating characteristics (ROC) curve (AUC), was 0.65 and 0.71 in the validation and retrospective phase, respectively. The calibration determines the distance between predicted outcome and actual outcome. The mean difference between predicted/observed was 2.3 and 6.3% in the retrospective and in the validation phase, respectively. The two values are quite similar and as a result we can conclude that the nomogram effectiveness was validated. Moreover, the ROC curve identified in the risk category of 31% of positive NSNs, the best compromise between false negative and positive rates i.e. when ALND is unnecessary ( 31%).Conclusions: The results of the study confirm that OSNA nomogram may help surgeons make an intraoperative decision on whether to perform ALND or not in case of positive sentinel nodes, and the patient to accept this decision based on a reliable estimation on the true percentage of NSN involvement. The use of this nomogram achieves two main gools: 1) the choice of the right treatment during the operation, 2) to avoid for the patient a second surgery procedure

International expert consensus on primary systemic therapy in the management of early breast cancer: highlights of the Fourth Symposium on Primary Systemic Therapy in the Management of Operable Breast Cancer, Cremona, Italy (2010).

A panel of international breast cancer experts formulated a declaration of consensus regarding many key issues in the use of primary systemic therapy (PST) either in clinical routine or research practice. The attainment of pathological complete response (pCR), defined as no residual invasive tumor in the surgical specimens both in breast and in axillary nodes, is one of the main goals of PST, and pCR can be used as the primary objective in prospective clinical trials. However, pCR is not a reliable endpoint with all treatment approaches, and alternatives such as Ki67 index of the residual invasive disease or after 2 weeks of PST are also potential endpoints. PST has several advantages: breast conservation and the unique opportunity to obtain information on the interaction between treatment and tumor biology. Changes in tumor biology after PST are an early phenomenon; so, an additional core biopsy performed after 14 days from treatment start should be considered in clinical trials.Consensus Development ConferenceJournal ArticleSCOPUS: ar.kinfo:eu-repo/semantics/publishe

A machine learning approach to predict healthcare-associated infections at intensive care unit admission: findings from the SPIN-UTI project

BACKGROUND: Identifying patients at higher risk of healthcare-associated infections (HAIs) in intensive care unit (ICU) represents a major challenge for public health. Machine learning could improve patient risk stratification and lead to targeted infection prevention and control interventions.AIM: To evaluate the performance of the Simplified Acute Physiology Score (SAPS) II for HAIs risk prediction in ICUs, using both traditional statistical and machine learning approaches.METHODS: We used data of 7827 patients from the "Italian Nosocomial Infections Surveillance in Intensive Care Units" project. The Support Vector Machines (SVM) algorithm was applied to classify patients according to sex, patient origin, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II at admission, presence of invasive devices, trauma, impaired immunity, antibiotic therapy in 48 hours before ICU admission.FINDINGS: The performance of SAPS II for predicting the risk of HAIs provides a ROC (Receiver Operating Characteristics) curve with an AUC (Area Under the Curve) of 0.612 (p<0.001) and an accuracy of 56%. Considering SAPS II along with other characteristics at ICU admission, we found an accuracy of the SVM classifier of 88% and an AUC of 0.90 (p<0.001) for the test set. In line, the predictive ability was lower when considering the same SVM model but removing the SAPS II variable (accuracy= 78% and AUC= 0.66).CONCLUSIONS: Our study suggested the SVM model as a tool to early predict patients at higher risk of HAI at ICU admission