Differential Diagnosis of Ewing Sarcoma and Other Bone and Soft Tissue Tumors Using Artificial Intelligence

In particular, the project focuses on Ewing sarcoma, a highly aggressive tumor that mainly affects young patients and whose diagnosis is especially challenging due to its similarity to other tumors with comparable morphological features.

To address this challenge, DEISA proposes the development of an advanced decision-support system based on computer vision and deep learning techniques. Through the automated analysis of digitized histological images, the system aims to assist pathologists in accurately identifying tumor patterns, reducing diagnostic variability and supporting clinical decision-making.

The technological core of the project lies in the design of artificial intelligence models capable of tackling two key challenges: distinguishing Ewing sarcoma from other small round cell tumors, and identifying less frequent variants known as Ewing-like. To this end, advanced approaches such as multiple instance learning and foundation models adapted to histopathological analysis are explored, enabling efficient processing of high-resolution, large-scale images.

In this context, the role of the Universitat Politècnica de València (UPV), through the CVBLab research group, is essential. The team contributes its extensive expertise in computer vision and deep learning, leading the development of predictive models and their optimization for real-world environments. This includes the design of architectures capable of extracting meaningful information from gigapixel images, as well as the incorporation of interpretability techniques to ensure that model decisions can be understood and validated in clinical settings.

The developed solutions will be validated on a large dataset of real cases with genetically confirmed diagnoses, ensuring the reliability of the system and its potential for clinical application. Furthermore, the integration of these models into dedicated visualization tools will enable their direct use by specialists, facilitating adoption in hospital environments.

The expected impact of DEISA is significant both at the clinical and technological levels. On the one hand, it aims to improve diagnostic accuracy and speed, reducing the need for additional tests and optimizing available resources. On the other hand, it promotes the adoption of AI-based solutions in healthcare, contributing to more efficient, accessible, and data-driven medicine.

The project consortium brings together complementary expertise ranging from clinical knowledge to technological development. The Department of Pathology at the Universitat de València (UV) leads the medical validation and provides access to high-quality clinical data, while ARTIKODE Intelligence is responsible for integrating the models into image analysis platforms. Within this ecosystem, UPV, through the CVBLab research group, plays a key role as a driver of innovation in artificial intelligence, developing solutions that transform complex data into practical tools for clinical use.