Stylianos Drisis1, Thierry Metens2, Michael Ignatiadis3, Konstantinos Stathopoulos4, Shih-Li Chao4, Marc Lemort4. 1. Radiology Department, Institute Jules Bordet, 1 Rue Heger Bordet, Brussels, 1000, Belgium. stylianos.drisis@bordet.be. 2. Radiology Department, Erasme University Hospital, Brussels, 1070, Belgium. 3. Oncology Department, Institute Jules Bordet, Brussels, 1000, Belgium. 4. Radiology Department, Institute Jules Bordet, 1 Rue Heger Bordet, Brussels, 1000, Belgium.
Abstract
OBJECTIVES: To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups. METHODS: Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2). Tumours were classified into different breast cancer groups, namely triple negative (TNBC), HER2+ and ER+/HER2-, and compared with the whole population (WP). PK parameters Ktrans and Ve were extracted using a two-compartment Tofts model. RESULTS: At EX1, Ktrans predicted pCR for WP and TNBC. At EX2, maximum diameter (Dmax) predicted pCR for WP and ER+/HER2-. Both PK parameters predicted pCR in WP and TNBC and only Ktrans for the HER2+. pCR was predicted from relative difference (EX1 - EX2)/EX1 of Dmax and both PK parameters in the WP group and only for Ve in the TNBC group. No PK parameter could predict response for ER+/HER-. ROC comparison between WP and breast cancer groups showed higher but not statistically significant values for TNBC for the prediction of pCR CONCLUSIONS: Quantitative DCE-MRI can better predict pCR after neoadjuvant treatment for TNBC but not for the ER+/HER2- group. KEY POINTS: • DCE-MRI-derived pharmacokinetic parameters can predict response status of neoadjuvant chemotherapy treatment. • Ktrans can better predict pCR for the triple negative group. • No pharmacokinetic parameter could predict response for the ER+/HER2- group.
OBJECTIVES: To assess whether DCE-MRI pharmacokinetic (PK) parameters obtained before and during chemotherapy can predict pathological complete response (pCR) differently for different breast cancer groups. METHODS: Eighty-four patients who received neoadjuvant chemotherapy for locally advanced breast cancer were retrospectively included. All patients underwent two DCE-MRI examinations, one before (EX1) and one during treatment (EX2). Tumours were classified into different breast cancer groups, namely triple negative (TNBC), HER2+ and ER+/HER2-, and compared with the whole population (WP). PK parameters Ktrans and Ve were extracted using a two-compartment Tofts model. RESULTS: At EX1, Ktrans predicted pCR for WP and TNBC. At EX2, maximum diameter (Dmax) predicted pCR for WP and ER+/HER2-. Both PK parameters predicted pCR in WP and TNBC and only Ktrans for the HER2+. pCR was predicted from relative difference (EX1 - EX2)/EX1 of Dmax and both PK parameters in the WP group and only for Ve in the TNBC group. No PK parameter could predict response for ER+/HER-. ROC comparison between WP and breast cancer groups showed higher but not statistically significant values for TNBC for the prediction of pCR CONCLUSIONS: Quantitative DCE-MRI can better predict pCR after neoadjuvant treatment for TNBC but not for the ER+/HER2- group. KEY POINTS: • DCE-MRI-derived pharmacokinetic parameters can predict response status of neoadjuvant chemotherapy treatment. • Ktrans can better predict pCR for the triple negative group. • No pharmacokinetic parameter could predict response for the ER+/HER2- group.
Entities:
Keywords:
Breast cancer; Neoadjuvant therapy; Oestrogen receptor; Perfusion magnetic resonance imaging; Triple negative breast cancer
Authors: Claudette E Loo; Marieke E Straver; Sjoerd Rodenhuis; Sara H Muller; Jelle Wesseling; Marie-Jeanne T F D Vrancken Peeters; Kenneth G A Gilhuijs Journal: J Clin Oncol Date: 2011-01-10 Impact factor: 44.544
Authors: M V Knopp; E Weiss; H P Sinn; J Mattern; H Junkermann; J Radeleff; A Magener; G Brix; S Delorme; I Zuna; G van Kaick Journal: J Magn Reson Imaging Date: 1999-09 Impact factor: 4.813
Authors: B K Linderholm; H Hellborg; U Johansson; G Elmberger; L Skoog; J Lehtiö; R Lewensohn Journal: Ann Oncol Date: 2009-06-23 Impact factor: 32.976
Authors: Chengyue Wu; Federico Pineda; David A Hormuth; Gregory S Karczmar; Thomas E Yankeelov Journal: Magn Reson Med Date: 2018-10-28 Impact factor: 4.668
Authors: Ella F Jones; Deep K Hathi; Rita Freimanis; Rita A Mukhtar; A Jo Chien; Laura J Esserman; Laura J Van't Veer; Bonnie N Joe; Nola M Hylton Journal: Cancers (Basel) Date: 2020-06-09 Impact factor: 6.575
Authors: Max Aa Ragusi; Gonneke Ao Winter-Warnars; Jelle Wesseling; Sabine C Linn; Regina G Beets-Tan; Bas Hm van der Velden; Sjoerd G Elias; Kenneth Ga Gilhuijs; Claudette E Loo Journal: Br J Radiol Date: 2021-07-01 Impact factor: 3.039