Comparison of the efficiency of nomograms used to predict preoperative sentinel lymph node positivity in breast cancer with clinical findings, PET/CT images and laboratory parameters
DOI:
https://doi.org/10.53545/jbm.2024.38Keywords:
Breast cancer, laboratory parameters, nomogram, PET/CT, sentinel lymph node, biopsyAbstract
Aim: To compare the efficacy of nomograms used to predict preoperative sentinel lymph node involvement in patients diagnosed with breast cancer and clinical findings, PET/CT imaging and laboratory parameters.
Methods: In this retrospective study, patients who were operated for invasive breast carcinoma with sentinel lymph node biopsy in our Generel Surgery Department between 2015 and 2020, were identified from our database. Laboratory parameters (PLR, NLR, LMR and MPR) were calculated from the complete blood count taken within 24 hours before surgery. Memorial Sloan Kettering Cancer Center (MSKCC) method was used from nomograms. Patients were compared according to sentinel lymph node positivity. All obtained data were compared with statistical tests.
Results: A total of 48 patients could be included in the study. A statistically significant correlation was found between physical examination, USG and PET/CT findings in terms of axilla positivity and pathology results (p<0.001, p=0.005 and p=0.002). The SUVmax value of the axilla was found to be statistically significantly higher in patients with positive SLNB group than in SLNB negative group (2.90±3.46 vs. 0.66±1.86, p=0.004). Although the rates of PLR, NLR, LMR and MPR among the laboratory parameters were higher in the SLNB positive group, they were not statistically significant (p=0.683, p=0.6, p=0.948 and p=0.354).
MSKCC nomogram values were higher in SNLB positive group, however it was not statistically significant (p=0.243).
Conclusion: In our study, clinical examination, laboratory testings, PET/CT imaging results and nomograms; on their own, have limited prediction about sentinel lymph node involvement. Therefore, we think it is necessary to design new algorithms that are more effective to predict axillary involvement and this will give better results in this regard.
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Copyright (c) 2024 Ferdi Bolat, Songul Peltek Ozer, Fatih Keyif, Bahri Ozer, Oguz Catal, Mustafa Sit, Hayri Erkol
This work is licensed under a Creative Commons Attribution 4.0 International License.