publications

2025

1. NeuroImage

   

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

   Loïse Dessoude, Raphaëlle Lemaire, Romain Andres, Thomas Leleu , and 10 more authors

   NeuroImage, Feb 2025

   Dessoude, Lemaire and Andres contributed equally (co-first authors).

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   This study develops an AI solution that rapidly and accurately segments brain metastases on MRI, providing RANO-BM criteria and volume measurements for efficient clinical evaluation.

   @article{dessoude_development_2025,
     title = {Development and routine implementation of a deep learning algorithm for automatic brain metastases segmentation on {MRI} for {RANO-BM} criteria follow-up},
     author = {Dessoude, Loïse and Lemaire, Raphaëlle and Andres, Romain and Leleu, Thomas and Leclercq, Alexandre G. and Desmonts, Alexis and Corroller, Typhaine and Orou-Guidou, Amirath Fara and Laduree, Luca and Le Henaff, Loïc and Lacroix, Joëlle and Lechervy, Alexis and Stefan, Dinu and Corroyer-Dulmont, Aurélien},
     year = {2025},
     month = feb,
     journal = {NeuroImage},
     volume = {306},
     pages = {121002},
     doi = {10.1016/j.neuroimage.2025.121002},
     note = {Dessoude, Lemaire and Andres contributed equally (co-first authors).}
   }

2. Under review

   

   Explainability and Clinical Trust of Deep Learning in Glioblastoma Treatment Efficacy Prediction: A Comprehensive Framework

   Romain Andres, Noémie Moreau, Loïse Dessoude, Loïc Le Henaff , and 5 more authors

   Feb 2025

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   First-author manuscript presenting a multi-layered XAI pipeline (Grad-CAM, LRP, LIME, linear probing, causal activation patching) to audit a ResNet-51q model predicting glioblastoma treatment response. The study uncovers that the model relies on an unexpected heuristic based on a proxy of the surgical resection status, anchoring its long- versus short-survival predictions on the detection of the surgical site rather than on tumoral features. This finding was confirmed through targeted ablation studies and high-resolution saliency maps, and the overall framework was validated in a multi-reader, multi-case clinical study.

   @unpublished{andres_explainability_2025,
     title = {Explainability and Clinical Trust of Deep Learning in Glioblastoma Treatment Efficacy Prediction: A Comprehensive Framework},
     author = {Andres, Romain and Moreau, Noémie and Dessoude, Loïse and Le Henaff, Loïc and Missohou, Fernand and Stefan, Dinu and Desmonts, Alexis and Herault, Romain and Corroyer-Dulmont, Aurélien},
     year = {2025},
     journal = {Manuscript under review},
   }