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Improving prediction of response to neoadjuvant treatment in patients with breast cancer by combining liquid biopsies with multiparametric MRI: protocol of the LIMA study - a multicentre prospective observational cohort study.
Janssen, Liselore M; Suelmann, Britt B M; Elias, Sjoerd G; Janse, Markus H A; van Diest, Paul J; van der Wall, Elsken; Gilhuijs, Kenneth G A.
Afiliação
  • Janssen LM; Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Suelmann BBM; Department of Medical Oncology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Elias SG; Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Janse MHA; Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • van Diest PJ; Department of Pathology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • van der Wall E; Department of Medical Oncology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Gilhuijs KGA; Image Sciences Institute, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands K.G.A.Gilhuijs@umcutrecht.nl.
BMJ Open ; 12(9): e061334, 2022 09 20.
Article em En | MEDLINE | ID: mdl-36127090
ABSTRACT

INTRODUCTION:

The response to neoadjuvant chemotherapy (NAC) in breast cancer has important prognostic implications. Dynamic prediction of tumour regression by NAC may allow for adaption of the treatment plan before completion, or even before the start of treatment. Such predictions may help prevent overtreatment and related toxicity and correct for undertreatment with ineffective regimens. Current imaging methods are not able to fully predict the efficacy of NAC. To successfully improve response prediction, tumour biology and heterogeneity as well as treatment-induced changes have to be considered. In the LIMA study, multiparametric MRI will be combined with liquid biopsies. In addition to conventional clinical and pathological information, these methods may give complementary information at multiple time points during treatment.

AIM:

To combine multiparametric MRI and liquid biopsies in patients with breast cancer to predict residual cancer burden (RCB) after NAC, in adjunct to standard clinico-pathological information. Predictions will be made before the start of NAC, approximately halfway during treatment and after completion of NAC.

METHODS:

In this multicentre prospective observational study we aim to enrol 100 patients. Multiparametric MRI will be performed prior to NAC, approximately halfway and after completion of NAC. Liquid biopsies will be obtained immediately prior to every cycle of chemotherapy and after completion of NAC. The primary endpoint is RCB in the surgical resection specimen following NAC. Collected data will primarily be analysed using multivariable techniques such as penalised regression techniques. ETHICS AND DISSEMINATION Medical Research Ethics Committee Utrecht has approved this study (NL67308.041.19). Informed consent will be obtained from each participant. All data are anonymised before publication. The findings of this study will be submitted to international peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT04223492.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Imageamento por Ressonância Magnética Multiparamétrica Tipo de estudo: Clinical_trials / Etiology_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Imageamento por Ressonância Magnética Multiparamétrica Tipo de estudo: Clinical_trials / Etiology_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article