Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI

Stylianos Drisis, Mohammed El Adoui, Patrick Flamen, Mohammed Benjelloun, Roland Dewind, Mariane Paesmans, Michail Ignatiadis, Maria Bali, Marc Lemort

Résultats de recherche: Contribution à un journal/une revueArticleRevue par des pairs


Background: Early prediction of nonresponse is essential in order to avoid inefficient treatments. Purpose: To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24–72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response. Study Type: This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study. Population: Sixty patients were initially recruited, with 39 women participating in the final cohort. Field Strength/Sequence: A 1.5T scanner was used for MRI examinations. Assessment: Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24–72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T1 subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0. Statistical Tests: T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis. Results: PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference. Data Conclusion: PRM could be predictive of non-pCR 24–72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling. Level of Evidence: 1. Technical Efficacy Stage: 4. J. Magn. Reson. Imaging 2020;51:1403–1411.

langue originaleAnglais
Pages (de - à)1403-1411
Nombre de pages9
journalJournal of Magnetic Resonance Imaging
Numéro de publication5
Les DOIs
Etat de la publicationPublié - 1 mai 2020
Modification externeOui

Empreinte digitale

Examiner les sujets de recherche de « Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI ». Ensemble, ils forment une empreinte digitale unique.

Contient cette citation