OBJECTIVE: The aim of this study was to conduct a systematic review and meta-analysis to clarify the diagnostic accuracy of intraoperative breast margin assessment (IMA) techniques against which the performance of emerging IMA technologies may be compared. SUMMARY OF BACKGROUND DATA: IMA techniques have failed to penetrate routine practice due to limitations, including slow reporting times, technical demands, and logistics. Emerging IMA technologies are being developed to reduce positive margin and re-excision rates and will be compared with the diagnostic accuracy of existing techniques. METHOD: Studies were identified using electronic bibliographic searches up to January 2016. MESH terms and all-field search terms included "Breast Cancer" AND "Intraoperative" AND "Margin." Only clinical studies with raw diagnostic accuracy data as compared with final permanent section histopathology were included. A bivariate model for diagnostic meta-analysis was used to attain overall pooled sensitivity and specificity. RESULTS: Eight hundred thirty-eight unique studies revealed 35 studies for meta-analysis. Pooled sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUROC) values were calculated per group (Sens, Spec, AUROC): frozen section = 86%, 96%, 0.96 (n = 9); cytology = 91%, 95%, 0.98 (n = 11); intraoperative ultrasound = 59%, 81%, 0.78 (n = 4); specimen radiography = 53%, 84%, 0.73 (n = 9); optical spectroscopy = 85%, 87%, 0.88 (n = 3). CONCLUSIONS: Pooled data suggest that frozen section and cytology have the greatest diagnostic accuracy. However, these methods are resource intensive and turnaround times for results have prevented widespread international adoption. Emerging technologies need to compete with the diagnostic accuracy of existing techniques while offering advantages in terms of speed, cost, and reliability.
OBJECTIVE: The aim of this study was to conduct a systematic review and meta-analysis to clarify the diagnostic accuracy of intraoperative breast margin assessment (IMA) techniques against which the performance of emerging IMA technologies may be compared. SUMMARY OF BACKGROUND DATA: IMA techniques have failed to penetrate routine practice due to limitations, including slow reporting times, technical demands, and logistics. Emerging IMA technologies are being developed to reduce positive margin and re-excision rates and will be compared with the diagnostic accuracy of existing techniques. METHOD: Studies were identified using electronic bibliographic searches up to January 2016. MESH terms and all-field search terms included "Breast Cancer" AND "Intraoperative" AND "Margin." Only clinical studies with raw diagnostic accuracy data as compared with final permanent section histopathology were included. A bivariate model for diagnostic meta-analysis was used to attain overall pooled sensitivity and specificity. RESULTS: Eight hundred thirty-eight unique studies revealed 35 studies for meta-analysis. Pooled sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUROC) values were calculated per group (Sens, Spec, AUROC): frozen section = 86%, 96%, 0.96 (n = 9); cytology = 91%, 95%, 0.98 (n = 11); intraoperative ultrasound = 59%, 81%, 0.78 (n = 4); specimen radiography = 53%, 84%, 0.73 (n = 9); optical spectroscopy = 85%, 87%, 0.88 (n = 3). CONCLUSIONS: Pooled data suggest that frozen section and cytology have the greatest diagnostic accuracy. However, these methods are resource intensive and turnaround times for results have prevented widespread international adoption. Emerging technologies need to compete with the diagnostic accuracy of existing techniques while offering advantages in terms of speed, cost, and reliability.
Authors: Wes M Allen; Ken Y Foo; Renate Zilkens; Kelsey M Kennedy; Qi Fang; Lixin Chin; Benjamin F Dessauvagie; Bruce Latham; Christobel M Saunders; Brendan F Kennedy Journal: Biomed Opt Express Date: 2018-11-19 Impact factor: 3.732
Authors: Maarten R Grootendorst; Anthony J Fitzgerald; Susan G Brouwer de Koning; Aida Santaolalla; Alessia Portieri; Mieke Van Hemelrijck; Matthew R Young; Julie Owen; Massi Cariati; Michael Pepper; Vincent P Wallace; Sarah E Pinder; Arnie Purushotham Journal: Biomed Opt Express Date: 2017-05-09 Impact factor: 3.732
Authors: Esther Kho; Behdad Dashtbozorg; Lisanne L de Boer; Koen K Van de Vijver; Henricus J C M Sterenborg; Theo J M Ruers Journal: Biomed Opt Express Date: 2019-08-07 Impact factor: 3.732
Authors: David M McClatchy; Elizabeth J Rizzo; Jeff Meganck; Josh Kempner; Jared Vicory; Wendy A Wells; Keith D Paulsen; Brian W Pogue Journal: Phys Med Biol Date: 2017-11-10 Impact factor: 3.609
Authors: Christina J Cocca; Laura E Selmic; Jonathan Samuelson; Pin-Chieh Huang; Jianfeng Wang; Stephen A Boppart Journal: Vet Surg Date: 2019-08-07 Impact factor: 1.495
Authors: Rebecca W Gao; Nutte T Teraphongphom; Nynke S van den Berg; Brock A Martin; Nicholas J Oberhelman; Vasu Divi; Michael J Kaplan; Steven S Hong; Guolan Lu; Robert Ertsey; Willemieke S F J Tummers; Adam J Gomez; F Christopher Holsinger; Christina S Kong; Alexander D Colevas; Jason M Warram; Eben L Rosenthal Journal: Cancer Res Date: 2018-07-02 Impact factor: 12.701