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QUIBIM is helping to advance knowledge of the most lethal pediatric tumors through EU-funded program PRIMAGE, which exploits precision information from medical imaging to establish tumor prognosis, and expected treatment response using radiomics, imaging biomarkers and artificial intelligence (AI).

Pediatric cancer is a rare disease, but treatment remains challenging. Improving knowledge is key to adequately plan therapy and boost survival, and the latest AI techniques have the potential to harness unprecedented information from medical images.

A few months ago, the European Commission funded the PRIMAGE (1) project with over €10M, to help identify the most efficient treatment and a tumor’s main characteristics without the need for biopsy, by using computational processing of medical images on the cloud.

The PRIMAGE consortium will create a bank of images obtained through AI, using an open cloud-based platform to support decision-making in the clinical management of Neuroblastoma (NB), the most frequent solid cancer of early childhood, and Diffuse Intrinsic Pontine Glioma (DIPG), the leading cause of brain tumor-related death in children. The PRIMAGE platform will implement the latest advancement of in-silico imaging biomarkers and modeling of tumor growth towards a personalized diagnosis, prognosis and therapies follow-up.
The project involves 16 European partners, including internationally recognized institutions, and four leading industrial partners, including Spanish biotechnology company QUIBIM, all working under the aegis of the Imaging Biomedical Research Group (GIBI230) based in La Fe Hospital, Valencia.

Sharing high-end knowledge of AI tools

QUIBIM is responsible for the central task of developing the PRIMAGE platform’s architecture, adaptation and design. The company recently obtained CE mark for its Chest X-Ray Classification AI-Tool and its imaging biomarker analysis algorithms, zero footprint DICOM viewer and platform within the QUIBIM Precision platform.

QUIBIM researchers are now bringing their expertise in medical image post processing and management to PRIMAGE, by passing on their knowledge of clinical trials design and validation, imaging biomarkers extraction and validation, radiomics, data clustering and visualization, and development of AI-fueled tools, such as organ segmentation models.
“Much work remains to be done to improve our knowledge of pediatric brain cancer. NB and DIPG have a complex therapeutic approach and we need proper tools to improve survival. Extracting quantitative information from medical images with AI can help visualize tumor growth with extreme precision, and help to tailor therapy to each individual patient,” Ángel Alberich-Bayarri  said.

QUIBIM’s input will also help to define the methodologies and standards to be used in the different development areas, to facilitate interoperability between the platform ́s modules and for future interoperability with their cloud-based platforms for functionality add-ons.

Transferrable knowledge to other cancers

Cancer has a very low incidence among children and experts estimate that 500,000 EU citizens will be pediatric cancer survivors by 2020. Nonetheless, cancer remains the first cause of non-traumatic death among children.

Neuroblastoma is the most common extracraneal tumor in children, representing 8-10% of all pediatric cancers. In Europe, 35,000 new cases are diagnosed each year, 1,000 in Spain alone.

Diffuse Intrinsic Pontine Glioma is a very rare disease in childhood and is associated with low survival (10%), despite many existing treatments and on-going research. Treatment is not curative, only palliative, i.e. radiotherapy to improve the patient’s life. 16 new cases are diagnosed each year in Spain, accounting for 2.5% of oncological pediatric patients and 13% of pediatric tumors of the central nervous system.

Because of the peculiarities of computational approximation in these two types of tumors that are proper to childhood, investigation done in that area will also be applicable to other types of tumors. Because it will gather considerable scientific effort, PRIMAGE should also help advance research on other types of cancer.
(1): PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (PRIMAGE)

Melisande Rouger