ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?

• URL: http://arxiv.org/abs/2408.10966v2
• Date: Mon, 07 Jul 2025 17:34:46 GMT
• Title: ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?
• Authors: Ezequiel de la Rosa, Ruisheng Su, Mauricio Reyes, Evamaria O. Riedel, Hakim Baazaoui, Roland Wiest, Florian Kofler, Kaiyuan Yang, David Robben, Mahsa Mojtahedi, Laura van Poppel, Lucas de Vries, Anthony Winder, Kimberly Amador, Nils D. Forkert, Gyeongyeon Hwang, Jiwoo Song, Dohyun Kim, Eneko Uruñuela, Annabella Bregazzi, Matthias Wilms, Hyun Yang, Jin Tae Kwak, Sumin Jung, Luan Matheus Trindade Dalmazo, Kumaradevan Punithakumar, Moona Mazher, Abdul Qayyum, Steven Niederer, Jacob Idoko, Mariana Bento, Gouri Ginde, Tianyi Ren, Juampablo Heras Rivera, Mehmet Kurt, Carole Frindel, Susanne Wegener, Jan S. Kirschke, Benedikt Wiestler, Bjoern Menze,
• Abstract summary: ISLES24 challenge focuses on the prediction of final infarct volumes from pre-interventional acute stroke imaging and clinical data.<n>Top-performing model, a multimodal nnU-Net-based architecture, achieved a Dice score of 0.285 on hidden test set of 98 cases.
• Score: 5.354756727899756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate estimation of brain infarction (i.e., irreversibly damaged tissue) is critical for guiding treatment decisions in acute ischemic stroke. Reliable infarct prediction informs key clinical interventions, including the need for patient transfer to comprehensive stroke centers, the potential benefit of additional reperfusion attempts during mechanical thrombectomy, decisions regarding secondary neuroprotective treatments, and ultimately, prognosis of clinical outcomes. This work introduces the Ischemic Stroke Lesion Segmentation (ISLES) 2024 challenge, which focuses on the prediction of final infarct volumes from pre-interventional acute stroke imaging and clinical data. ISLES24 provides a comprehensive, multimodal setting where participants can leverage all clinically and practically available data, including full acute CT imaging, sub-acute follow-up MRI, and structured clinical information, across a train set of 150 cases. On the hidden test set of 98 cases, the top-performing model, a multimodal nnU-Net-based architecture, achieved a Dice score of 0.285 (+/- 0.213) and an absolute volume difference of 21.2 (+/- 37.2) mL, underlining the significant challenges posed by this task and the need for further advances in multimodal learning. This work makes two primary contributions: first, we establish a standardized, clinically realistic benchmark for post-treatment infarct prediction, enabling systematic evaluation of multimodal algorithmic strategies on a longitudinal stroke dataset; second, we analyze current methodological limitations and outline key research directions to guide the development of next-generation infarct prediction models.

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