Ines Gruber1, Maja Henzel2, Birgitt Schönfisch3, Annette Stäbler4, Florin-Andrei Taran3, Markus Hahn3, Carmen Röhm3, Gisela Helms3, Ernst Oberlechner3, Benjamin Wiesinger5, Konstantin Nikolaou5, Christian LA Fougère6, Diethelm Wallwiener3, Andreas Hartkopf3, Natalia Krawczyk7, Tanja Fehm3,7, Sara Brucker3,8. 1. Department of Obstetrics and Gynecology, University of Tübingen, Tübingen, Germany ines.gruber@med.uni-tuebingen.de. 2. Department of Obstetrics and Gynecology, Filder Clinic, Filderstadt-Bonlanden, Germany. 3. Department of Obstetrics and Gynecology, University of Tübingen, Tübingen, Germany. 4. Department of Anatomic Pathology, Institute of Pathology and Neuropathology, University of Tübingen, Tübingen, Germany. 5. Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany. 6. Department of Nuclear Medicine, University of Tübingen, Tübingen, Germany. 7. Department of Obstetrics and Gynecology, University of Düsseldorf, Düsseldorf, Germany. 8. Research Institute for Women's Health, University of Tübingen, Tübingen, Germany.
Abstract
BACKGROUND/AIM: Only 30-50% of patients with sentinel lymph node (SLN) metastases present with further axillary lymph node metastases. Therefore, up to 70% of patients with positive SLN are overtreated by axillary dissection (AD) and may suffer from complications such as sensory disturbances or lymphedema. According to the current S3 guidelines, AD can be avoided in patients with a T1/T2 tumor if breast-conserving surgery with subsequent tangential irradiation is performed and no more than two SLNs are affected. Additionally, use of nomograms, that predict the probability of non-sentinel lymph node (NSLN) metastases, is recommended. Therefore, models for the prediction of NSLN metastases in our defined population were constructed and compared with the published nomograms. PATIENTS AND METHODS: In a retrospective study, 2,146 primary breast cancer patients, who underwent SLN biopsy at the University Women's Hospital in Tuebingen, were evaluated by dividing the patient group in a training and validation collective (TC or VC). Using the SLN-positive TC patients, three models for the prediction of the likelihood of NSLN metastases were adapted and were then validated using the SLN-positive VC patients. In addition, the predictive power of nomograms from Memorial Sloan Kettering Cancer Center (MSKCC), Stanford, and the Cambridge model were compared with regard to our patient collective. RESULTS: A total of 2,146 patients were included in the study. Of these, 470 patients had positive SLN, 295 consisted the training collective and 175 consisted the validation collective. In a regression model, three variants - with 11, 6 and 2 variables - were developed for the prediction of NSLN metastases in our defined population and compared to the most frequently used nomograms. Our variants with 11 and with 6 variables were proven to be a particularly suitable model and showed similarly good results as the published MSKCC nomogram. CONCLUSION: Our developed nomograms may be used as a prediction tool for NSLN metastases after positive SLN. Copyright
BACKGROUND/AIM: Only 30-50% of patients with sentinel lymph node (SLN) metastases present with further axillary lymph node metastases. Therefore, up to 70% of patients with positive SLN are overtreated by axillary dissection (AD) and may suffer from complications such as sensory disturbances or lymphedema. According to the current S3 guidelines, AD can be avoided in patients with a T1/T2 tumor if breast-conserving surgery with subsequent tangential irradiation is performed and no more than two SLNs are affected. Additionally, use of nomograms, that predict the probability of non-sentinel lymph node (NSLN) metastases, is recommended. Therefore, models for the prediction of NSLN metastases in our defined population were constructed and compared with the published nomograms. PATIENTS AND METHODS: In a retrospective study, 2,146 primary breast cancerpatients, who underwent SLN biopsy at the University Women's Hospital in Tuebingen, were evaluated by dividing the patient group in a training and validation collective (TC or VC). Using the SLN-positive TCpatients, three models for the prediction of the likelihood of NSLN metastases were adapted and were then validated using the SLN-positive VC patients. In addition, the predictive power of nomograms from Memorial Sloan Kettering Cancer Center (MSKCC), Stanford, and the Cambridge model were compared with regard to our patient collective. RESULTS: A total of 2,146 patients were included in the study. Of these, 470 patients had positive SLN, 295 consisted the training collective and 175 consisted the validation collective. In a regression model, three variants - with 11, 6 and 2 variables - were developed for the prediction of NSLN metastases in our defined population and compared to the most frequently used nomograms. Our variants with 11 and with 6 variables were proven to be a particularly suitable model and showed similarly good results as the published MSKCC nomogram. CONCLUSION: Our developed nomograms may be used as a prediction tool for NSLN metastases after positive SLN. Copyright
Authors: Fabio Corsi; Luca Sorrentino; Sara Albasini; Daniela Bossi; Carlo Morasso; Laura Villani; Marta Truffi Journal: Medicine (Baltimore) Date: 2020-08-28 Impact factor: 1.817