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Enhancing decision-making in glioblastoma surgery through an explainable human-AI collaboration: an international multicenter model development and external validation study

Surgical resection improves survival in glioblastoma, yet predicting the extent of resection (EOR) remains highly challenging. We developed and externally validated an explainable AI model to generate personalized EOR estimates in 811 glioblastoma patients undergoing microsurgical resection. EOR was...

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Main Authors: Kernbach, Julius (Author) , Schroeder, Urte (Author) , Hakvoort, Karlijn (Author) , Ort, Jonas (Author) , Hamou, Hussam (Author) , Bzdok, Danilo (Author) , Temel, Yasin (Author) , Kubben, Pieter (Author) , Weyland, Charlotte (Author) , Wiesmann, Martin (Author) , Staartjes, Victor (Author) , Akeret, Kevin (Author) , Vieli, Moira (Author) , Serra, Carlo (Author) , Regli, Luca (Author) , Grau, Stefan (Author) , Dührsen, Lasse (Author) , Ricklefs, Franz (Author) , Schnell, Oliver (Author) , Ormond, David Ryan (Author) , Grote, Alexander (Author) , Simon, Matthias (Author) , Meredig, Hagen (Author) , Schell, Marianne (Author) , Bendszus, Martin (Author) , Neuloh, Georg (Author) , Clusmann, Hans (Author) , Heiland, Dieter-Henrik (Author) , Delev, Daniel (Author)
Format: Article (Journal)
Language:English
Published: 27 November 2025
In: npj precision oncology
Year: 2025, Volume: 9, Pages: 1-13
ISSN:2397-768X
DOI:10.1038/s41698-025-01183-2
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41698-025-01183-2
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41698-025-01183-2
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Author Notes:Julius M. Kernbach, Urte Schroeder, Karlijn Hakvoort, Jonas Ort, Hussam Hamou, Danilo Bzdok, Yasin Temel, Pieter Kubben, Charlotte Weyland, Martin Wiesmann, Victor Staartjes, Kevin Akeret, Moira Vieli, Carlo Serra, Luca Regli, Stefan Grau, Lasse Dührsen, Franz Ricklefs, Oliver Schnell, David Ryan Ormond, Alexander Grote, Matthias Simon, Hagen Meredig, Marianne Schell, Martin Bendszus, Georg Neuloh, Hans Clusmann, Dieter-Henrik Heiland, and Daniel Delev
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Summary:Surgical resection improves survival in glioblastoma, yet predicting the extent of resection (EOR) remains highly challenging. We developed and externally validated an explainable AI model to generate personalized EOR estimates in 811 glioblastoma patients undergoing microsurgical resection. EOR was categorized into gross-total (GTR), near-total (NTR), and subtotal resections (STR). An interpretable framework provided model explanations and sensitivity analyses to assess the model’s strengths and limitations. To demonstrate clinical impact, we compared the performance of the human expert (gold standard) with our AI model and a combined human-AI approach. External validation confirmed generalizability (AUC 0.78, CI 0.73-0.82). Class-specific AUCs were 0.75 (0.67-0.82) for GTR, 0.59 (0.50-0.69) for NTR, and 0.69 (0.53-0.85) for STR. Key predictors included KPS and NANO scores, age, tumor volume, and unfavorable anatomical locations. A combined human-AI collaboration outperformed human experts, with higher overall accuracies (0.53 to 0.94), F1 scores (0.30 to 0.92), and Cohen’s κ (0.41 to 0.84). Enhancing predictive performance through the clinician-AI collaboration, our explainable model supports preoperative planning and highlights the value of integrating machine intelligence into surgical decision-making.
Item Description:Gesehen am 10.03.2026
Physical Description:Online Resource
ISSN:2397-768X
DOI:10.1038/s41698-025-01183-2

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