BACKGROUND: The accurate prediction of nonsentinel node (NSN) metastasis in breast cancer patients remains uncertain. METHODS: The medical records of 574 breast cancer patients from 2 different institutions (Mayo Clinic and University of Michigan) with sentinel lymph node biopsy examination and completion axillary lymph node dissection were reviewed for multiple clinicopathologic variables. The Memorial Sloan Kettering Cancer Center nomogram performance for prediction of NSN metastases was assessed. A new model was developed with clinically relevant variables and possible advantages. RESULTS: The Memorial Sloan Kettering Cancer Center nomogram predicted the likelihood of NSN metastasis with an area under the receiver operating characteristic curve of .72 and .86. For predicted probability cut-off points of 5% and 10%, the false-negative rates were 0% and 14% (Mayo), and 17% and 11% (Michigan). A new model was developed with similar area under the curve but lower false-negative rates for low-probability subgroups. CONCLUSIONS: Predictive models for NSN tumor burden are imperfect.
BACKGROUND: The accurate prediction of nonsentinel node (NSN) metastasis in breast cancerpatients remains uncertain. METHODS: The medical records of 574 breast cancerpatients from 2 different institutions (Mayo Clinic and University of Michigan) with sentinel lymph node biopsy examination and completion axillary lymph node dissection were reviewed for multiple clinicopathologic variables. The Memorial Sloan Kettering Cancer Center nomogram performance for prediction of NSN metastases was assessed. A new model was developed with clinically relevant variables and possible advantages. RESULTS: The Memorial Sloan Kettering Cancer Center nomogram predicted the likelihood of NSN metastasis with an area under the receiver operating characteristic curve of .72 and .86. For predicted probability cut-off points of 5% and 10%, the false-negative rates were 0% and 14% (Mayo), and 17% and 11% (Michigan). A new model was developed with similar area under the curve but lower false-negative rates for low-probability subgroups. CONCLUSIONS: Predictive models for NSN tumor burden are imperfect.
Authors: Elizabeth A Mittendorf; Kelly K Hunt; Judy C Boughey; Roland Bassett; Amy C Degnim; Robyn Harrell; Min Yi; Funda Meric-Bernstam; Merrick I Ross; Gildy V Babiera; Henry M Kuerer; Rosa F Hwang Journal: Ann Surg Date: 2012-01 Impact factor: 12.969
Authors: I Barco; A García-Fernández; C Chabrera; M Fraile; E Vallejo; J M Lain; J Deu; S González; C González; E Veloso; J Torres; M Torras; L Cirera; A Pessarrodona; N Giménez; M García-Font Journal: Clin Transl Oncol Date: 2016-02-26 Impact factor: 3.405
Authors: Oldrich Coufal; Tomás Pavlík; Pavel Fabian; Rita Bori; Gábor Boross; István Sejben; Róbert Maráz; Jaroslav Koca; Eva Krejcí; Iva Horáková; Vendula Foltinová; Pavlína Vrtelová; Vojtech Chrenko; Wolde Eliza Tekle; Mária Rajtár; Mihály Svébis; Vuk Fait; Gábor Cserni Journal: Pathol Oncol Res Date: 2009-05-15 Impact factor: 3.201