BACKGROUND: : It remains unclear how many sentinel lymph nodes (SLNs) must be removed to accurately predict lymph node status during SLN dissection in breast cancer. The objective of this study was to determine how many SLNs need to be removed for accurate lymph node staging and which patient and tumor characteristics influence this number. METHODS: : The authors reviewed data for all patients in their prospective database with clinical tumor, lymph node, metastasis (TNM) T1 through T3, N0, M0 breast cancer who underwent lymphatic mapping at their institution during the years 1994 through 2006. There were 777 patients who had at least 1 SLN that was positive for cancer. Simple and multiple quantile regression analyses were used to determine which patient and tumor characteristics were associated with the number of positive SLNs. The baseline number of SLNs that needed to be dissected for detection of 99% of positive SLNs in the total group of patients also was determined. RESULTS: : The mean number of SLNs removed in the 777 lymph node-positive patients was 2.9 (range, 1-13 SLNs). Greater than 99% of positive SLNs were identified in the first 5 lymph nodes removed. On univariate analysis, tumor histology, patient race, tumor location, and tumor size significantly affected the number of SLNs that needed to be removed to identify 99% of all positive SLNs. On multivariate analysis, mixed ductal and lobular histology, Caucasian race, inner quadrant tumor location, and T1 tumor classification significantly increased the number of SLNs that needed to be removed to achieve 99% recovery of all positive SLNs. CONCLUSIONS: : In general, the removal of a maximum of 5 SLNs at surgery allowed for the recovery of >99% of positive SLNs in patients with breast cancer. The current findings indicated that tumor histology, patient race, and tumor size and location may influence this number. (Copyright) 2008 American Cancer Society.
BACKGROUND: : It remains unclear how many sentinel lymph nodes (SLNs) must be removed to accurately predict lymph node status during SLN dissection in breast cancer. The objective of this study was to determine how many SLNs need to be removed for accurate lymph node staging and which patient and tumor characteristics influence this number. METHODS: : The authors reviewed data for all patients in their prospective database with clinical tumor, lymph node, metastasis (TNM) T1 through T3, N0, M0 breast cancer who underwent lymphatic mapping at their institution during the years 1994 through 2006. There were 777 patients who had at least 1 SLN that was positive for cancer. Simple and multiple quantile regression analyses were used to determine which patient and tumor characteristics were associated with the number of positive SLNs. The baseline number of SLNs that needed to be dissected for detection of 99% of positive SLNs in the total group of patients also was determined. RESULTS: : The mean number of SLNs removed in the 777 lymph node-positive patients was 2.9 (range, 1-13 SLNs). Greater than 99% of positive SLNs were identified in the first 5 lymph nodes removed. On univariate analysis, tumor histology, patient race, tumor location, and tumor size significantly affected the number of SLNs that needed to be removed to identify 99% of all positive SLNs. On multivariate analysis, mixed ductal and lobular histology, Caucasian race, inner quadrant tumor location, and T1 tumor classification significantly increased the number of SLNs that needed to be removed to achieve 99% recovery of all positive SLNs. CONCLUSIONS: : In general, the removal of a maximum of 5 SLNs at surgery allowed for the recovery of >99% of positive SLNs in patients with breast cancer. The current findings indicated that tumor histology, patient race, and tumor size and location may influence this number. (Copyright) 2008 American Cancer Society.
Authors: D J Winchester; S F Sener; D P Winchester; R M Perlman; R A Goldschmidt; G Motykie; C H Martz; S L Rabbitt; D Brenin; M A Stull; J M Moulthrop Journal: J Am Coll Surg Date: 1999-06 Impact factor: 6.113
Authors: D R Byrd; L K Dunnwald; D A Mankoff; B O Anderson; R E Moe; R S Yeung; E K Schubert; J F Eary Journal: Ann Surg Oncol Date: 2001-04 Impact factor: 5.344
Authors: Matthew T Hueman; Bradford J Scanlan; Paul W White; Scott R Golarz; George E Peoples; Craig D Shriver; Mary E Maniscalco-Theberge Journal: Curr Surg Date: 2002 May-Jun
Authors: Susanne H Estourgie; Omgo E Nieweg; Renato A Valdés Olmos; Emiel J Th Rutgers; Bin B R Kroon Journal: Ann Surg Date: 2004-02 Impact factor: 12.969
Authors: Nathan Andrew Rohner; Jacob McClain; Sara Lydia Tuell; Alex Warner; Blair Smith; Youngho Yun; Abhinav Mohan; Manuela Sushnitha; Susan Napier Thomas Journal: FASEB J Date: 2015-07-15 Impact factor: 5.191
Authors: Sarah B Bateni; Anders J Davidson; Mili Arora; Megan E Daly; Susan L Stewart; Richard J Bold; Robert J Canter; Candice A M Sauder Journal: Ann Surg Oncol Date: 2019-02-13 Impact factor: 5.344
Authors: Alexandra S Brown; Kelly K Hunt; Jeannie Shen; Lei Huo; Gildy V Babiera; Merrick I Ross; Funda Meric-Bernstam; Barry W Feig; Henry M Kuerer; Judy C Boughey; Christine D Ching; Michael Z Gilcrease Journal: Cancer Date: 2010-06-15 Impact factor: 6.860
Authors: J Serrano-Vicente; J I Rayo-Madrid; M L Domínguez-Grande; J R Infante-Torre; L García-Bernardo; M Moreno-Caballero; F Medina-Romero; C Durán-Barquero Journal: Clin Transl Oncol Date: 2015-08-18 Impact factor: 3.405
Authors: Ted A James; Alex R Coffman; Anees B Chagpar; Judy C Boughey; V Suzanne Klimberg; Monica Morrow; Armando E Giuliano; Seth P Harlow Journal: Ann Surg Oncol Date: 2016-07-21 Impact factor: 5.344
Authors: Danielle Riley; Elizabeth A Chrischilles; Ingrid M Lizarraga; Mary Charlton; Brian J Smith; Charles F Lynch Journal: Breast Cancer Res Treat Date: 2022-01-24 Impact factor: 4.872