Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up.

Journal: NeuroImage
PMID: 39800174

Abstract

RATIONALE AND OBJECTIVES: The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently time-consuming. Consequently, in clinical practice, monitoring patients in clinical trials in compliance with the RANO-BM criteria is rarely achieved. The objective of this study was to develop and validate an AI solution capable of delineating brain metastases (BM) on MRI to easily obtain, using an in-house solution, RANO-BM criteria as well as BM volume in a routine clinical setting.

Authors

  • Loïse Dessoude
    Radiotherapy Department, Centre François Baclesse, Caen 14000, France.
  • Raphaëlle Lemaire
    Medical Physics Department, Centre François Baclesse, Caen 14000, France.
  • Romain Andres
    Medical Physics Department, Centre François Baclesse, Caen 14000, France.
  • Thomas Leleu
    Radiotherapy Department, Centre François Baclesse, Caen 14000, France.
  • Alexandre G Leclercq
    Medical Physics Department, Centre François Baclesse, Caen 14000, France.
  • Alexis Desmonts
    Radiotherapy Department, Centre François Baclesse, Caen 14000, France.
  • Typhaine Corroller
    Medical Physics Department, Centre François Baclesse, Caen 14000, France.
  • Amirath Fara Orou-Guidou
    Medical Physics Department, Centre François Baclesse, Caen 14000, France.
  • Luca Laduree
    Medical Physics Department, Centre François Baclesse, Caen 14000, France.
  • Loic Le Henaff
    Radiology Department, Centre François Baclesse, Caen 14000, France.
  • Joëlle Lacroix
    Radiology Department, Centre François Baclesse, Caen 14000, France.
  • Alexis Lechervy
    ENSICAEN, CNRS, GREYC UMR6072, Normandie Université, Université Caen Normandie, Caen F-14000, France.
  • Dinu Stefan
    Radiotherapy Department, Centre François Baclesse, Caen 14000, France.
  • Aurélien Corroyer-Dulmont
    Medical Physics Department, Centre François Baclesse, 14000 Caen, France; Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP CYCERON, F-14000 Caen, France. Electronic address: a.corroyer-dulmont@baclesse.unicancer.fr.

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