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QUIBIM to develop platform in leading research project to fight pediatric cancer

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.

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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)

QUIBIM_Symbiosis of Radiology and AI

QUIBIM at RSNA 2018!

We are happy to announce that QUIBIM will be attending this year’s annual meeting of the Radiological Society of North America – RSNA 2018. From November 25 to 29, many of our Quibimers will be in Chicago demoing, sharing and showing our Radiomic solution for Hospitals and Radiology departments.

This year, QUIBIM developments are focused on the Symbiosis of Radiology and Artificial Intelligence to seamlessly integrate imaging biomarkers into radiology workflows.


Attendees can find us at the Machine Learning Showcase – Booth #7367G  (North Building Level 3), where anyone is welcome to come over and explore the latest version of our QUIBIM Precision® Platform for medical images processing and imaging biomarkers analysis.




QUIBIM will be also taking part in the Machine Learning Showcase through a communication from our CEO and Founder, Ángel Alberich-Bayarri, on November 25 at 12:30 pm. The communication is entitled “QUIBIM Precision 3.0: AI as a Means, Not an End, for Imaging Biomarkers Integration in Clinical Practice” and shares QUIBIM insights about the role of AI on imaging biomarkers integration.


Furthermore, as members of the NVIDIA Inception Program, QUIBIM will be demoing at the NVIDIA booth #6568 on November 26 at 10:00 am. Don’t miss it!

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It is a great opportunity for QUIBIM to be engaged in such a recognised event and get in touch with professionals in the fields of radiology and medical imaging.



New Chest X-Ray Classification Tool

Despite the technological evolution of imaging modalities like CT, US and MRI, conventional X-ray remains the most performed examination in radiology departments, and remains a fundamental tool for anatomical analysis in the detection and diagnosis of respiratory diseases and bone tissue alterations. However, radiology departments have limitations in reporting the X-Rays due to the limited resources available (link).

QUIBIM has developed a Chest X-Ray Classification Tool that offers a solution to this problem which can help radiology departments become even more efficient. This classifier developed in collaboration with Hospital Universitario y Politécnico La Fe, estimates the probability of chest X-Rays of having a pathology using Artificial Intelligence.

How does it work?

This tool makes use of fourteen Convolutional Neural Networks trained with a database of more than 100,000 images (NIH ChestXray14 dataset) to estimate the probability of presence of the following pathologies in chest X-Rays: atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening and hernia. Afterwards, the probabilities are used by a Fully Connected Neural Network to get the final probability of the X-Ray of being abnormal.

XRAY chest

Because of this AI methodology the classifier understands the visual patterns that are most indicative of the different pathologies using the knowledge extracted from the large dataset of radiographs used to train the networks. QUIBIM’s Chest X-Ray Classification Tool is able to learn further using new images, which means that this system is continually improving and evolving with time.

 What information does it provide?

Once all the quantifications are performed this tool provides an intuitive Structured Report with the patient’s information, the abnormal probability of the radiographs analyzed and the representation of the findings. In addition, if the image is classified as abnormal, the report shows the three pathologies that are more likely to be found and a heatmap that highlights the most abnormal regions. This report is designed to be very user friendly, to assist the user in understanding the tool’s findings at a glance in order to make its usage highly efficient.

Why is this new tool so useful?

Using this technology it is possible to prioritize unreported, potentially pathological radiographs which allows radiologists to focus their efforts on studies that are more likely to have pathologies and thereby become more efficient. This tool essentially ensures that pathological findings, which could have been unreported due to the heavy workload of radiology departments, are correctly reported.

QUIBIM’s goal is to provide the radiology departments with an optimal solution for automatic reading of X-Rays without interfering in the  workflow of the department.

QUIBIM’S Chest X-Ray Analysis Tool is already available at QUIBIM Precision® Depending on the needs of the department, this tool is accessible through the cloud with just a few clicks  or  it can be fully integrated in the radiology department’s workflow as a local solution for  seamless interpretation of  chest X-Rays.

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