AIMS: A meta-analysis was performed to identify the clinicopathological variables most predictive of non-sentinel node (NSN) metastases when the sentinel node is positive. METHODS: A Medline search was conducted that ultimately identified 56 candidate studies. Original data were abstracted from each study and used to calculate odds ratios. The random-effects model was used to combine odds ratios to determine the strength of the associations. FINDINGS: The 8 individual characteristics found to be significantly associated with the highest likelihood (odds ratio >2) of NSN metastases are SLN metastases >2mm in size, extracapsular extension in the SLN, >1 positive SLN, ≤1 negative SLN, tumour size >2cm, ratio of positive sentinel nodes >50% and lymphovascular invasion in the primary tumour. The histological method of detection, which is associated with the size of metastases, had a correspondingly high odds ratio. CONCLUSIONS: We identified 8 factors predictive of NSN metastases that should be recorded and evaluated routinely in SLN databases. These factors should be included in a predictive model that is generally applicable among different populations.
AIMS: A meta-analysis was performed to identify the clinicopathological variables most predictive of non-sentinel node (NSN) metastases when the sentinel node is positive. METHODS: A Medline search was conducted that ultimately identified 56 candidate studies. Original data were abstracted from each study and used to calculate odds ratios. The random-effects model was used to combine odds ratios to determine the strength of the associations. FINDINGS: The 8 individual characteristics found to be significantly associated with the highest likelihood (odds ratio >2) of NSN metastases are SLN metastases >2mm in size, extracapsular extension in the SLN, >1 positive SLN, ≤1 negative SLN, tumour size >2cm, ratio of positive sentinel nodes >50% and lymphovascular invasion in the primary tumour. The histological method of detection, which is associated with the size of metastases, had a correspondingly high odds ratio. CONCLUSIONS: We identified 8 factors predictive of NSN metastases that should be recorded and evaluated routinely in SLN databases. These factors should be included in a predictive model that is generally applicable among different populations.
Authors: Francesco Giammarile; Naomi Alazraki; John N Aarsvold; Riccardo A Audisio; Edwin Glass; Sandra F Grant; Jolanta Kunikowska; Marjut Leidenius; Valeria M Moncayo; Roger F Uren; Wim J G Oyen; Renato A Valdés Olmos; Sergi Vidal Sicart Journal: Eur J Nucl Med Mol Imaging Date: 2013-10-02 Impact factor: 9.236
Authors: Raquel F D van la Parra; Petronella G M Peer; Wilfred K de Roos; Miranda F Ernst; Johannes H W de Wilt; Koop Bosscha Journal: World J Surg Date: 2014-05 Impact factor: 3.352
Authors: Andrea V Barrio; Stephanie Downs-Canner; Marcia Edelweiss; Kimberly J Van Zee; Hiram S Cody; Mary L Gemignani; Melissa L Pilewskie; George Plitas; Mahmoud El-Tamer; Laurie Kirstein; Deborah Capko; Sujata Patil; Monica Morrow Journal: Ann Surg Oncol Date: 2019-12-09 Impact factor: 5.344