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

Franco Di Filippo, Simona Di Filippo, Anna Maria Ferrari, Raffaele Antonetti, Alessandro Battaglia, Francesca Becherini, Laia Bernet, Renzo Boldorini, Catherine Bouteille, Simonetta Buglioni, Paolo Burelli, Rafael Cano, Vincenzo Canzonieri, Pierluigi Chiodera, Alfredo Cirilli, Luigi Coppola, Stefano Drago, Luca Di Tommaso, Privato Fenaroli, Roberto FranchiniAndrea Gianatti, Diana Giannarelli, Carmela Giardina, Florence Godey, Massimo M. Grassi, Giuseppe B. Grassi, Siobhan Laws, Samuele Massarut, Antonio Giuseppe Naccarato, Maria Iole Natalicchio, Sergio Orefice, Fabrizio Palmieri, Tiziana Perin, Manuela Roncella, Massimo G. Roncalli, Antonio Rulli, Angelo Sidoni, Corrado Tinterri, Maria Caterina Truglia, Isabella Sperduti

Research output: Contribution to journalArticlepeer-review

Abstract

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%) or recommended (>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.

Original languageEnglish
Article number193
JournalJournal of Experimental and Clinical Cancer Research
Volume35
Issue number1
DOIs
Publication statusPublished - Dec 8 2016

Keywords

  • CK19 mRNA number copies
  • Nomogram
  • Non Sentinel Node status
  • OSNA method

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Cite this