Combining HIstology, Medical imaging and molEcular data for medical pRognosis and diAgnosis (CHIMERA)
Creators: name: Schouten, Daan
name: Spaans, Robert
name: Faryna, Khrystyna
name: Khalili, Nadieh
name: Litjens, Geert
name: Dille, Bas
name: Chia, Catherine
name: Vendittelli, Pierpaolo
name: Zuiverloon, Tahlita
Publisher: name: Zenodo
Year: 2025
Subjects: multimodal AI
prostate cancer
bladder cancer
recurrence
cancer survival
MICCAI 2025 challenge

description: CHIMERA (Combining HIstology, Medical imaging (radiology) and molEcular data for medical pRognosis and diAgnosis) aims to advance precision medicine in cancer care by addressing the critical need for multimodal data integration. However, integrating various modalities across clinical departments remains a complex challenge. Clinicians face large, heterogeneous datasets that are difficult to analyze effectively. Artificial Intelligence (AI) plays a crucial role in solving this problem, yet many technical barriers remain unsolved, including unifying multimodal data, defining appropriate fusion stages, and addressing challenges where data from one modality is missing. This challenge introduces a benchmark that combines genomics, pathology, and radiology data from multiple institutions to develop AI models. Previous research and challenges have focused on isolated modalities training AI single types of medical data. While effective treatment planning needs a holistic approach that bridges departments and integrates diverse data sources. The rise of foundation models powered by transformer architectures presents opportunities to encode large images, such as pathology and radiology data, into meaningful encodings. This makes it the ideal time to develop a multimodal benchmark that integrates genomics, pathology, and radiology. The proposed challenge includes datasets from prostate and bladder cancer, the multimodal data is collected from multiple centers focused on predicting recurrence in prostate and non-invasive bladder cancer.

Prostate cancer prognosis is currently assessed using PSA levels, a biomarker that remains controversial and is unable to predict biochemical recurrent reliably[1,2]. This challenge aims to predict recurrence by integrating multiparametric MRI and H&E-stained; histopathology slides, two modalities routinely applied in the clinic for initial screening and Gleason grading. Recent studies have shown that deep learning can uncover finer morphological features from histopathology, which hold significant prognostic value in recurrence prediction[3]. The second cancer type included in this challenge is non-invasive bladder cancer (NIBC). A standard treatment for NIBC is the Bacillus Calmette-Guérin (BCG) vaccine, a globally scarce resource. Unfortunately, approximately 50% of patients receiving BCG fail to respond to the treatment, with unresponsiveness typically identified only during follow-up evaluations, leading to delayed cystectomy. This uncertainty in treatment response complicates patient management, delays effective treatment, and leaves patients with side effects of unnecessary treatment. Recent trials demonstrated that classifying the molecular RNA analysis correlates with a high risk of response. These findings highlight the potential of integrating genomics, pathology, and imaging to improve prognostic accuracyand optimize treatment strategies[4].

The CHIMERA challenge will bring together six participating centers, Radboud University Medical Center (Radboudumc), Erasmus University Medical Center, Canisius Wilhelmina Hospital(CWZ), Isala Zwolle Academie, Bringham University and Stanford University to deliver benchmark AI solutions for recurrence prediction. CHIMERA represents a critical step toward realizing multimodal precision medicine by interoperability between domains and leveraging diverse clinical datasets. This effort aligns with the MICCAI society's goal of advancing AI integration into clinical workflows to improve healthcare outcomes globally. We will offer pre-extracted features from widely used foundation models, including UNI, Virchow, and GigaPath for digital pathology, as well as popular models in radiology[5,6,7]. These features will enable researchers toexplore multimodal fusion techniques without requiring extensive computational resources. This approach ensures the challenge remains sustainable and inclusive, providing accessibility to researchers without large GPU resources, even beyond MICCAI.

References



US Preventive Services Task Force. "Screening for prostate cancer: US Preventive Services Task Force recommendation statement." Jama 319, no. 18 (2018): 1901-1913.

Heijnsdijk, Eveline AM, Chris H. Bangma, Josep M. Borras, Tiago M. de Carvalho, Xavier Castells, Martin Eklund, Josep A. Espinas et al. "Summary statement on screening for prostate cancer in Europe." International journal of Combining HIstology, Medical imaging and molEcular data for cancer 142, no. 4 (2018): 741-746.

Pinckaers, Hans, Jolique van Ipenburg, Jonathan Melamed, Angelo De Marzo, Elizabeth A. Platz, Bram van Ginneken, Jeroen van der Laak, and Geert Litjens. "Predicting biochemical recurrence of prostate cancer with artificial intelligence." Communications Medicine 2, no. 1 (2022): 64.

de Jong, Florus C., Teemu D. Laajala, Robert F. Hoedemaeker, Kimberley R. Jordan, Angelique CJ van der Made, Egbert R. Boevé, Deric KE van der Schoot et al. "Non–muscle-invasive bladder cancer molecular subtypes predict differential response to intravesical Bacillus Calmette-Guérin." Science translational medicine 15, no. 697 (2023): eabn4118.

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Xu, Hanwen, Naoto Usuyama, Jaspreet Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong et al. "A whole-slide foundation model for digital pathology from real-world data." Nature (2024): 1-8.