Nancy Yu1, Vivian W Y Leung1, Sarkis Meterissian2,3,4,5. 1. Faculty of Medicine, McGill University, Montréal, QC, H4A3T2, Canada. 2. Faculty of Medicine, McGill University, Montréal, QC, H4A3T2, Canada. sarkis.meterissian@mcgill.ca. 3. Department of Oncology, McGill University, Montréal, QC, H4A3T2, Canada. sarkis.meterissian@mcgill.ca. 4. Department of Surgery, McGill University, Montréal, QC, H3G1A4, Canada. sarkis.meterissian@mcgill.ca. 5. Research Institute of MUHC, Glen Site, 1001 Decarie Boulevard, Montreal, QC, H4A 3J1, Canada. sarkis.meterissian@mcgill.ca.
Abstract
BACKGROUND: MRI performance in detecting pathologic complete response (pCR) post-neoadjuvant chemotherapy (NAC) in breast cancer has been previously explored. However, since tumor response varies by molecular subtype, it is plausible that imaging performance also varies. Therefore, we performed a literature review on subtype-specific MRI performance in detecting pCR post-NAC. METHODS: Two reviewers searched Cochrane, PubMed, and EMBASE for articles published between 2013 and 2018 that examined MRI performance in detecting pCR post-NAC. After filtering, ten primary research articles were included. Statistical metrics, such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were extracted per study for triple negative, HR+/HER2-, and HER2+ patients. RESULTS: Ten studies involving 2310 patients were included. In triple negative breast cancer, MRI showed NPV (58-100%) and PPV (72.7-94.7%) across 446 patients and sensitivity (45.5-100%) and specificity (49-94.4%) in 375 patients. In HR+/HER2- breast cancer patients, MRI showed NPV (29.4-100%) and PPV (21.4-95.1%) across 851 patients and sensitivity (43-100%) and specificity (45-93%) across 780 patients. In HER2+-enriched subtype, MRI showed NPV (62-94.6%) and PPV (34.9-72%) in 243 patients and sensitivity (36.2-83%) and specificity (47-90%) in 255 patients. CONCLUSION: MRI accuracy in detecting pCR post-NAC by subtype is not as consistent, nor as high, as individual studies suggest. Larger studies using standardized pCR definition with appropriate timing of surgery and MRI need to be conducted. This study has shown that MRI is in fact not an accurate prediction of pCR, and thus, clinicians may need to rely on other approaches such as biopsies of the tumor bed.
BACKGROUND: MRI performance in detecting pathologic complete response (pCR) post-neoadjuvant chemotherapy (NAC) in breast cancer has been previously explored. However, since tumor response varies by molecular subtype, it is plausible that imaging performance also varies. Therefore, we performed a literature review on subtype-specific MRI performance in detecting pCR post-NAC. METHODS: Two reviewers searched Cochrane, PubMed, and EMBASE for articles published between 2013 and 2018 that examined MRI performance in detecting pCR post-NAC. After filtering, ten primary research articles were included. Statistical metrics, such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were extracted per study for triple negative, HR+/HER2-, and HER2+ patients. RESULTS: Ten studies involving 2310 patients were included. In triple negative breast cancer, MRI showed NPV (58-100%) and PPV (72.7-94.7%) across 446 patients and sensitivity (45.5-100%) and specificity (49-94.4%) in 375 patients. In HR+/HER2- breast cancerpatients, MRI showed NPV (29.4-100%) and PPV (21.4-95.1%) across 851 patients and sensitivity (43-100%) and specificity (45-93%) across 780 patients. In HER2+-enriched subtype, MRI showed NPV (62-94.6%) and PPV (34.9-72%) in 243 patients and sensitivity (36.2-83%) and specificity (47-90%) in 255 patients. CONCLUSION: MRI accuracy in detecting pCR post-NAC by subtype is not as consistent, nor as high, as individual studies suggest. Larger studies using standardized pCR definition with appropriate timing of surgery and MRI need to be conducted. This study has shown that MRI is in fact not an accurate prediction of pCR, and thus, clinicians may need to rely on other approaches such as biopsies of the tumor bed.
Authors: Jennifer F De Los Santos; Alan Cantor; Keith D Amos; Andres Forero; Mehra Golshan; Janet K Horton; Clifford A Hudis; Nola M Hylton; Kandace McGuire; Funda Meric-Bernstam; Ingrid M Meszoely; Rita Nanda; E Shelley Hwang Journal: Cancer Date: 2013-02-21 Impact factor: 6.860
Authors: Sara Sheikhbahaei; Tyler J Trahan; Jennifer Xiao; Mehdi Taghipour; Esther Mena; Roisin M Connolly; Rathan M Subramaniam Journal: Oncologist Date: 2016-07-08
Authors: John R Scheel; Eunhee Kim; Savannah C Partridge; Constance D Lehman; Mark A Rosen; Wanda K Bernreuter; Etta D Pisano; Helga S Marques; Elizabeth A Morris; Paul T Weatherall; Sandra M Polin; Gillian M Newstead; Laura J Esserman; Mitchell D Schnall; Nola M Hylton Journal: AJR Am J Roentgenol Date: 2018-04-30 Impact factor: 3.959
Authors: Woo Jung Choi; Won Kyung Kim; Hee Jung Shin; Joo Hee Cha; Eun Young Chae; Hak Hee Kim Journal: Clin Breast Cancer Date: 2017-08-18 Impact factor: 3.225
Authors: Michael L Marinovich; Petra Macaskill; Les Irwig; Francesco Sardanelli; Eleftherios Mamounas; Gunter von Minckwitz; Valentina Guarneri; Savannah C Partridge; Frances C Wright; Jae Hyuck Choi; Madhumita Bhattacharyya; Laura Martincich; Eren Yeh; Viviana Londero; Nehmat Houssami Journal: BMC Cancer Date: 2015-10-08 Impact factor: 4.430
Authors: Stephen F Sener; Rachel E Sargent; Connie Lee; Tejas Manchandia; Vivian Le-Tran; Yuliya Olimpiadi; Nicole Zaremba; Andrew Alabd; Maria Nelson; Julie E Lang Journal: J Surg Oncol Date: 2019-08-09 Impact factor: 3.454