F Noushi1, A J Spillane, R F Uren, V Gebski. 1. Department of Surgical Oncology, Mater and Royal North Shore Hospital, University of Sydney, Sydney, Australia. fnoushi@med.usyd.edu.au
Abstract
AIM: Internal mammary node (IMN) metastases are an important prognostic factor in breast cancer. However due to difficulty of access, most surgeons ignore these nodes, hence adjuvant treatment decisions may be compromised. Through mathematical modeling based on large datasets this study aims to estimate the current rate of IMN and sentinel node metastasis. METHODS: Models were created to estimate the current rate of axillary and IM sentinel node metastasis. Data from historical extended radical mastectomy series were analyzed to project contemporary rates of IMN metastasis. This information was coupled with derived models and contemporary datasets: a single-institution breast lymphoscintigraphy database (1992-2007) to establish lymphatic anatomy; and the Surveillance, Epidemiology and End-Results (SEER) registries in the US (2000-2003). RESULTS: Rates of IMN metastasis and positive sentinel nodes were estimated and models derived to assist with predicting IMN status in patients. If high definition peritumoral lymphatic mapping were available, the predicted rates of positive sentinel nodes in the axilla (AN) and internal mammary chain (IMN) would be equal. We predicted the overall rate of IMN metastasis is approximately 39% the rate of positive sentinel AN. CONCLUSION: Simplified models and algorithms can predict IMN status.
AIM: Internal mammary node (IMN) metastases are an important prognostic factor in breast cancer. However due to difficulty of access, most surgeons ignore these nodes, hence adjuvant treatment decisions may be compromised. Through mathematical modeling based on large datasets this study aims to estimate the current rate of IMN and sentinel node metastasis. METHODS: Models were created to estimate the current rate of axillary and IM sentinel node metastasis. Data from historical extended radical mastectomy series were analyzed to project contemporary rates of IMN metastasis. This information was coupled with derived models and contemporary datasets: a single-institution breast lymphoscintigraphy database (1992-2007) to establish lymphatic anatomy; and the Surveillance, Epidemiology and End-Results (SEER) registries in the US (2000-2003). RESULTS: Rates of IMN metastasis and positive sentinel nodes were estimated and models derived to assist with predicting IMN status in patients. If high definition peritumoral lymphatic mapping were available, the predicted rates of positive sentinel nodes in the axilla (AN) and internal mammary chain (IMN) would be equal. We predicted the overall rate of IMN metastasis is approximately 39% the rate of positive sentinel AN. CONCLUSION: Simplified models and algorithms can predict IMN status.
Authors: Amanda L Kong; Welela Tereffe; Kelly K Hunt; Min Yi; Taewoo Kang; Kimberly Weatherspoon; Elizabeth A Mittendorf; Isabelle Bedrosian; Rosa F Hwang; Gildy V Babiera; Thomas A Buchholz; Funda Meric-Bernstam Journal: Cancer Date: 2012-05-30 Impact factor: 6.860
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