BACKGROUND: Prior studies of the ability of magnetic resonance imaging (MRI) to predict pathologic response to neoadjuvant chemotherapy have shown conflicting results that vary depending on baseline molecular characteristics. This study examines the ability of MRI to predict pathologic complete response (pCR) and explores the influence of tumor molecular profiles on MRI sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). METHODS: Eighty-one patients with invasive breast cancer treated with neoadjuvantsystemic therapy between 2002 and 2009 who were imaged with breast MRI pre- and post-treatment were reviewed. Patient, tumor, and treatment characteristics were recorded. Comparisons of molecular subsets and their influence on MRI sensitivity, specificity, PPV, and NPV were made using χ(2)contingency tables. RESULTS: The sensitivity, specificity, PPV, and NPV of MRI for predicting pCR for the total group were 92%, 50%, 74%, and 80%, respectively. Patients had the following molecular subtypes: 33/81 (41%) HR+Her2-, 23/81 (28%) HR+/-Her2 +, and 25/81(31%) triple receptor negative (TN). Molecular subtype did not demonstrate a significant correlation of radiographic and pathologic response, although MRI NPV was highest in the TN subset (100%) followed by those with HR+/-Her2+ disease (87.5%). Multivariate analysis did not show that tumor characteristics (estrogen receptor status, progesterone receptor status, HER2 status) or neoadjuvant treatment (doxorubicin, cyclophosphamide, paclitaxel versus other or trastuzumab) had any effect on MRI sensitivity or specificity. CONCLUSIONS: In patients receiving neoadjuvant systemic therapy for invasive breast cancer, molecular subtype and systemic regimen administered did not significantly influence the sensitivity, specificity, PPV, or NPV of MRI in predicting pathologic response.
BACKGROUND: Prior studies of the ability of magnetic resonance imaging (MRI) to predict pathologic response to neoadjuvant chemotherapy have shown conflicting results that vary depending on baseline molecular characteristics. This study examines the ability of MRI to predict pathologic complete response (pCR) and explores the influence of tumor molecular profiles on MRI sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). METHODS: Eighty-one patients with invasive breast cancer treated with neoadjuvantsystemic therapy between 2002 and 2009 who were imaged with breast MRI pre- and post-treatment were reviewed. Patient, tumor, and treatment characteristics were recorded. Comparisons of molecular subsets and their influence on MRI sensitivity, specificity, PPV, and NPV were made using χ(2)contingency tables. RESULTS: The sensitivity, specificity, PPV, and NPV of MRI for predicting pCR for the total group were 92%, 50%, 74%, and 80%, respectively. Patients had the following molecular subtypes: 33/81 (41%) HR+Her2-, 23/81 (28%) HR+/-Her2 +, and 25/81(31%) triple receptor negative (TN). Molecular subtype did not demonstrate a significant correlation of radiographic and pathologic response, although MRI NPV was highest in the TN subset (100%) followed by those with HR+/-Her2+ disease (87.5%). Multivariate analysis did not show that tumor characteristics (estrogen receptor status, progesterone receptor status, HER2 status) or neoadjuvant treatment (doxorubicin, cyclophosphamide, paclitaxel versus other or trastuzumab) had any effect on MRI sensitivity or specificity. CONCLUSIONS: In patients receiving neoadjuvant systemic therapy for invasive breast cancer, molecular subtype and systemic regimen administered did not significantly influence the sensitivity, specificity, PPV, or NPV of MRI in predicting pathologic response.
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: Xia Li; Lori R Arlinghaus; Gregory D Ayers; A Bapsi Chakravarthy; Richard G Abramson; Vandana G Abramson; Nkiruka Atuegwu; Jaime Farley; Ingrid A Mayer; Mark C Kelley; Ingrid M Meszoely; Julie Means-Powell; Ana M Grau; Melinda Sanders; Sandeep R Bhave; Thomas E Yankeelov Journal: Magn Reson Med Date: 2013-05-09 Impact factor: 4.668
Authors: Samia Al-Hattali; Sarah J Vinnicombe; Nazleen Muhammad Gowdh; Andrew Evans; Sharon Armstrong; Douglas Adamson; Colin A Purdie; E Jane Macaskill Journal: Cancer Imaging Date: 2019-12-26 Impact factor: 3.909
Authors: M B I Lobbes; R Prevos; M Smidt; V C G Tjan-Heijnen; M van Goethem; R Schipper; R G Beets-Tan; J E Wildberger Journal: Insights Imaging Date: 2013-01-29