DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
Rechercher
(titres de publication, de périodique et noms de colloque inclus)
2019-11-19 - Article/Dans un journal avec peer-review - Anglais - 9 page(s)

Stylianos Drisis, El Adoui Mohammed , Flamen Patrick, Benjelloun Mohammed , Dewind Roland, Paesmans Mariane, Ignatiadis Michail, Bali Maria, Lemort Marc, "Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI" in Journal of Magnetic Resonance Imaging

  • Edition : John Wiley & Sons, Hoboken (NY)
  • Codes CREF : Techniques d'imagerie et traitement d'images (DI2770), Intelligence artificielle (DI1180), Théorie de l'information (DI1161), Informatique mathématique (DI1160), Imagerie médicale, radiologie, tomographie (DI3243)
  • Unités de recherche UMONS : Informatique (F114)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)
Texte intégral :

Abstract(s) :

(Anglais) 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 morphologicalresponse (EMR) and pathological complete response (pCR) 24–72 hours after initiation of chemotherapy treatment andwhether 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 wereacquired at baseline (timepoint 1, TP1), 24–72 hours after thefirst chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T1subtractionimages 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) betweenTP1 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 predictionof non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P< 0.01) for non-pCRprediction (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 fornon-pCR tumors, information that could be used for optimal tissue sampling.