Imaging Biomarker



Pain, depression, slurred speech and feeling of numbness, tingling, or weakness. These are just some symptoms of Multiple Sclerosis (MS) disease, a long-term condition that affects the brain and spinal cord.

In MS, the immune system confuses myelin with a foreign body and attacks it. The loss of this protective sheath that covers the nerve fibers will disrupt the messages travelling between the brain and the body. These messages may be slowed down, interrupted, or may not occur at all.

Eventually, the person sees affected the ability of controlling their own actions. The signs and symptoms of MS may vary greatly depending on the stage of the disease and the location of the affected nerve fibers. Movement affections, vision problems, altered speech and dizziness are common consequences of this condition.

It is the most widespread neurological disorder of young adults globally. The disease can be developed at any age, but its main incidence appears in the range of 20-50 years old. The National Multiple Sclerosis Society estimates that near 2.3 million people are living with this disease around the world. It also calculates that 1 million of them are placed in the United States, where 200 new cases are diagnosed every week.

Together with blood tests, medical history and neurologic exams, imaging scans have also proven to be a key element for the diagnosis of MS, concretely the Magnetic Resonance Imaging (MRI) is the reference diagnostic technique for the identification of lesions in MS.

Damaged white matter has a prolonged T2 relaxation time due to increased tissue water content and to degradation of the myelin, being well depicted on MRI and concretely on Fluid Attenuated Inversion Recovery (FLAIR) images. In this MR-sequence, MS lesions are seen as white matter hyperintensities (WMH). Nowadays, manual segmentation of WMH areas is still the gold standard to quantify the total lesion volume and to know the number of lesions in the brain. However, this methodology turns MS patient’s diagnosis and follow-up in a cumbersome and time-consuming task with high intra- and inter- observer variabilities.

Zero-click tools based on Artificial Intelligence (AI) and, more concretely, Convolutional Neural Networks (CNN) can be used to automatically segment WMH on FLAIR images in a few minutes. Novel designed architectures are composed of an ensemble of CNNs built on standard convolutional, dilated and residual layers.

Multiple Sclerosis_QUIBIM

These tools are capable of fine segmentation of the lesion avoiding the physiological WMH as the ependymal layer. Physicians can obtain quantitative information that helps them to achieve a more accurate and earlier diagnosis, thus reducing the workload and improving the time-efficiency while enhancing patient assessment.

What information does it provide?

Once WMH are segmented, relevant lesion statistics are quantified: lesion number, total lesion volume, dominant lesion volume, dissemination, or entropy among others. All this information can be summarized in a structured report along with the most characteristic slices. These processes will easily assist physicians in the diagnosis of MS patients not in the future, but now.

QuibimStructuredReport_White matter lesions


Eduardo Camacho Ramos.

Ana Jiménez Pastor.



  • Chest X-ray classifier is added as a new CE cleared tool within QUIBIM Precision platform, which already received CE mark class IIa certification earlier in 2019.

  • QUIBIM’s Chest X-Ray Classification AI-Tool dynamically learns using new images, meaning the system is continuously evolving and improving over time.

Valencia, Spain – 27th May 2019. Spanish healthcare AI company QUIBIM today announced that its AI-powered Chest X-Ray Classification tool has received CE certification. The company already obtained the class IIa CE mark earlier this year for the imaging biomarker analysis algorithms, the zero footprint DICOM viewer and the platform within the QUIBIM Precision platform, becoming the first Spanish firm to ever receive the clearance.

QUIBIM applies machine learning and image processing techniques to extract imaging biomarkers from medical images in order to assist radiologists and physicians in daily practice. With its AI-based chest x-ray classifier, QUIBIM helps to detect pathological findings that could go unreported due to the heavy workload of radiology departments.

QuibimStructuredReport_Chest X Ray Classifier

“Our tool was designed to prioritize unreported, potentially pathological radiographs, to help radiologists be more efficient by focusing their efforts on studies that are more likely to have pathologies. We are sure this will have an impact in big healthcare systems,” Angel Alberich-Bayarri, QUIBIM CEO and founder, explained.

The solution makes use of a novel architecture based on referee networks combined with Convolutional Neural Networks that have been trained with a database of more than 500,000 images, to calculate the final probability of the X-Ray of being abnormal. Afterwards, the probabilities are used to estimate the presence of pathologies in chest X-Rays.

Because of this Artificial Intelligence 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,” Rafael López, Artificial Intelligence Engineer at QUIBIM, said.

QUIBIM’S Chest X-Ray Analysis Tool is already available at QUIBIM Precision®, 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.


QUIBIM at the annual Outsourcing in Clinical Trials Europe 2019

We are very happy to share with you QUIBIM´s experience at the last Outsourcing in Clinical Trials Europe 2019 from 14th to 15th of May in Milan (Italy).

Clinical Trials Manager and Chief Strategy Officer, Irene Mayorga and Kabir Mahajan, joined this annual meeting to introduce the services that QUIBIM offers to CRO’s and pharmaceutical companies conducting imaging clinical trials. They had the pleasure of interacting with many novel drug development companies & CRO’s focusing on glioblastoma, pancreatic cancer, other cancers and diffused liver disease and had the chance to show QUIBIM´s experience on this front.

Also, we had the opportunity to discuss QUIBIM´s services as an imaging core lab, not only with the scientific and technical aspects, but also with the design and development of imaging documentation such as the imaging trial charter, the medical imaging validation and imaging acquisition quality control,  statistical analysis and other standard clinical trials management services.

If you did not have the chance to join us in Milan, you can meet Irene Mayorga Ruiz and Kabir Mahajan at the BIO Convention in Philadelphia from June 3- June 6, 2019 where they would be showing the latest developments in the QUIBIM Precision platform for managing clinical trials. You can use the following link to book a demo.


Breast cancer QUIBIM

QUIBIM uses AI to boost
breast cancer research

As the first ESMO Breast Cancer Congress unfolded 2-4 May in Berlin, Germany, QUIBIM CEO highlighted the role played by artificial intelligence (AI) in breast cancer research.

The European Society of Medical Oncology focused on the progress in treatment options and improved outcomes for breast cancer (BC) patients during its first meeting dedicated to the topic (¹).

One of the key messages of the conference was that therapeutic innovations should go hand in hand with a multidisciplinary, fully integrated approach to patient care, a scenario in which AI can increasingly help make a difference, according to QUIBIM CEO & founder Angel Alberich-Bayarri.

Breast cancer_QUIBIM

QUIBIM breast cancer structured report

AI, and in particular image quantification, can help significantly advance knowledge of this multi-faceted disease, by enabling earlier and better detection,” he said.

From the Pacific to the rest of the world

Recently QUIBIM partnered with the AI Precision Health Institute (AI-PHI) at the University of Hawai‘i Cancer Center in Honolulu, to manage, store and quantitatively analyze medical images and algorithms in breast cancer research.

Using the QUIBIM Precision® image analysis platform, AI-PHI researchers will create large scale imaging repositories with automated extraction of imaging biomarkers to characterize patients’ status. The solution will first be used as the central repository for the mammography studies conducted in the Pacific and will include mammograms from over 5 million women.

The University of Hawai‘i Cancer Center is one of 69 NCI designated cancer centers in the United States and the only one in Hawai‘i and all of the Pacific Islands. It is the only institution that uses AI to analyze medical images to assess health and predict risk of disease in the region.

The project is expected to grow exponentially as its principal investigator Dr. John Shepherd is keen on inviting selected reference centers all across the world to join the network. “It is a very interesting and pioneering work for QUIBIM, and our first big cooperation in the breast cancer setting,” Alberich said.

Seamless integration into clinical workflow

The QUIBIM Precision® image analysis platform enables to incorporate AI algorithms regardless of the institution where it is deployed, to facilitate integration into workflow.

“Researchers can program their own algorithm and code, and add it as a plugin to the platform. This feature, combined with the advanced storage and multi-user annotation capabilities of the platform, allows for a wide adoption within research groups and institutions working with AI. Time for deployment of algorithms in the real world is shortened by 25%, since you have the whole AI pipeline in just one place,” he explained.

The solution, which includes not only quantitative image analysis but also structured reporting capabilities, can be integrated into the radiology workflow and add value to this service. It can for instance be used to integrate AI algorithms to detect cancer without the need of radiologists in a first-read.

Earlier this year the platform received CE Mark certification as class IIa Medical Device, including the imaging biomarker analysis algorithms, the zero footprint DICOM viewer and the platform hosting these components and medical imaging data. The platform is now available for commercial deployment in European hospitals and diagnostic imaging centers.

In addition to the platform, the certification includes 15 algorithms in the five key areas of interest of the company: oncology, neurology, musculoskeletal, liver and lung

Also included in the CE Mark certification is the DataMiner, which was designed in collaboration with the team of Prof. Daniel Keim from Konstanz University to provide advanced visual analytics of large databases of patients for population health management and scientific exploitation. The DICOM web viewer that enables the user to visualize the images of a sequence, draw ROIs or apply filters is also included in the certification.

QUIBIM applies machine learning to develop tools for imaging data quantification, to accelerate image reconstruction, segmentation, detection and data mining. Beyond the breast, the company has created AI algorithms to help detect changes produced by brain and prostate cancer, but also other diseases in the hematological scenario such as non-Hodgkin’s Lymphoma.



Comprehensive solutions will boost AI use in medical imaging

Integrating artificial intelligence (AI) into the radiologist’s workflow is still challenging, but all-inclusive solutions can help unfold algorithms’ power, QUIBIM CEO and founder Angel Alberich-Bayarri explained during the ESR AI Premium Event, which was hosted by the European Society of Radiology (ESR) and the European School of Radiology (ESOR) earlier this month in Barcelona.

Medical imaging AI is a bubbling field but very few solutions are used in daily practice today, Alberich told delegates during the busy meeting, which gathered top researchers in medical imaging AI and thousands of online attendees. “We have a lot of research, AI algorithms and start-ups, but few are really embedded in the radiology workflow,” he said.

A main obstacle to AI integration is a lack of knowledge of utilities. For most imaging biomarkers, the real application relationship with clinical endpoints on a large scale – diagnostic, prognostic and treatment response – remains unknown. Clinicians don’t want to integrate biomarkers that have not been validated, but if they don’t gather information massively and try to understand how biomarkers relate to the disease, they will never help advance healthcare, Alberich explained.

“We have to change our minds and not wait for biomarkers to be validated before they can be extracted on a daily basis. Similarly to genomics: we have to do sequencing and study diseases to detect unknown mutations by ourselves,” he said.

The lack of annotated data is another challenge. Radiology reports are not filled with a focus on data annotation, but on descriptive language that needs to be processed by natural language processing (NLP). However they would be an ideal source of knowledge, and not just for the clinicians.

One-stop-shop platform using biomarkers

QUIBIM was created in 2015 as a spinoff company of La Fe Hospital in Valencia, Spain, to help radiologists make the most of AI in their everyday practice, by providing a one-stop-shop solution using imaging biomarkers and powerful algorithms.

“Offering a single solution embedded in workflows is key because radiologists will not buy all start-up micro developments but the best platform,” he said.

The very name of the company is an acronym and stands for Quantitative Imaging Biomarkers in Medicine. Imaging biomarkers enable to measure everything happening in the body to extract parameters that can provide information on the tissue of the lesion type beyond classification. Fuelled by AI, these biomarkers can deliver unprecedented information on disease.

“Many different imaging pipelines have dramatically changed thanks to AI integration. For example, we used to have lots of problems doing segmentation with traditional algorithms in tissues and organs such as the liver in MRI. Thanks to AI, segmentation has improved and with it our knowledge of liver disease,” he said.

A prerequisite is to integrate data mining solutions to make radiomics easy to everyone. This means it must be embedded in the same platform, as current solutions are not able to treat this amount of quantitative data from patient cohorts. “A lot of parameters are quantified in daily routine, but there is still no way to store and process them massively. Our current PACS systems are simply not prepared for quantitative data,” Alberich said.

There is a lot of sense in working within a structured report (SR), as it enables to annotate data that will further advance research. Prospectively the studies can be very well annotated if AI imaging biomarkers are integrated in the fields of the SR. Working with the SR would tremendously facilitate communication between clinicians and with patients.

“We are building the radiology report of the future, only we’re doing it now. Patients do not understand radiology reports, so we have to change the way we communicate. We are very much aligned with the standardized way blood test findings are reported, everyone understands whether findings are in or out of range and if there is any abnormality. We have to be more intuitive in our communication,” Alberich said.

QUIBIM is developing quantitative, one-page long structured reports that are actionable, quantitative and automated. The reports are designed with KOLs in each speciality, to make sure they reflect the reality of clinical practice.

Clinical input and powerful technology

Cooperation with clinicians is a key axis for the company, which uses a stepwise model to create new solutions with clinicians in the loop.

The company develops highly performing AI algorithms using unprecedented network architectures, for instance, multiscale convolutional networks ensemble in brain MR, 2.5D network in liver MR and referee network in chest x-ray, and hundreds of thousands of annotated images that have been acquired through cooperation with researchers and university hospitals.

QUIBIM has launched algorithms in different product areas to provide a head to toe imaging biomarker solution; in neuro for Alzheimer, ALS, MS, stroke and cerebrovascular events; in MSK for articulations; in chest for screening, COPD and fibrosis; and in breast for screening. More products will soon be released that will focus on other body areas.

More than 60 hospitals worldwide currently use QUIBIM tools, most of which have received CE marks and/or are FDA pending. The company has notably developed the new ESOR teaching platform and has supplied its QUIBIM Precision® image analysis platform to the AI Precision Health Institute at the University of Hawai‘i Cancer Center in Honolulu. QUIBIM has received €3.5m funding ever since its creation and has offices in Spain and the U.S.


ECR 2019 sets the trend in Artificial Intelligence

The city of Vienna was at the real core of Medical Imaging and AI at the 25th European Congress of Radiology (ECR). At QUIBIM we always enjoy being at this annual meeting because it is the perfect combination of a science and industry exhibition. In this edition, the congress reached the milestone of 30,000 attendees, and the numbers are expected to go up with each year.

QUIBIM was glad to be participating in both, the scientific and industry sessions. Our oral presentations were mainly addressing current challenges of artificial intelligence (AI) and convolutional neural networks (CNN) in clinical needs like metabolic disorder, prostate cancer and osteoarthritis. Ana Jiménez-Pastor, Rafael López-González and Fabio García-Castro, R&D Engineers at QUIBIM presented our new research in image processing pipelines aiming to perform a virtual dissection of the organs through an automated segmentation combined with features extraction.

Personally,  I was happy to give 3 lectures focused on the future of radiology: 3D Post-processing in 2019, Deep Learning in Medical Imaging and Start-up in Radiology. In the 3D Post-processing lecture I introduced, what I think is the main revolution of AI in our field the concept of Virtual In-Vivo Dissection (VIVID), a name coined by my team and I at QUIBIM, which is a strategy of isolating human body organs in medical images for  characterization through features such as imaging biomarkers. This has several applications, challenges and is difficult to solve by traditional computer vision algorithms like liver or cartilage segmentation in Magnetic Resonance Imaging but it has become a reality thanks to the use of CNN architectures such as U-Net combined with deep supervision. In the Deep Learning in Medical Imaging session, I  focused on using other presentation formats and I was glad to give a TED talk and share the podium with Dr. Wiro Niessen, who spoke about Machine Learning in a Pecha Kucha format. This session was organized by the European School of Radiology (ESOR) and was chaired by Prof. Dr. Valérie Vilgrain, and I must say the atmosphere was excellent and the room was really packed! Finally, in a session chaired by Prof. Dr. Elmar Kotter,  we shared our insights on how to create a start-up company in Radiology from scratch, how to get funding from investors and the main considerations when scaling-up.

QUIBIM was also invited by the ECR to give a presentation within the Artificial Intelligence Exhibition (AIX) sessions, a new space for innovative AI companies. During this session, moderated by Dr Hugh Harvey and Dr Wim Van Hecke, we presented the newest version of the QUIBIM Precision V3.0 platform launched at the last RSNA 2018.

MRS QUIBIM Analysis_2

QUIBIM now offering advanced MR spectroscopy analysis including 2-HG for IDH1 mutation detection

Continuing its focus in the field of Oncology, QUIBIM is proud to offer an advanced MR Spectroscopy analysis tool. This tool developed in house by QUIBIM will help oncologists to improve diagnosis and treatment of brain gliomas, a disease that has an incidence of 22.6 per 100.000 population in United States.

In order to have reproducible, reliable and accurate information from tumors, we provide physicians with quantification of the metabolite by analyzing Magnetic Resonance Spectroscopy (MRS) images. MRS is a non-invasive imaging technique widely used to obtain chemical information by detecting relevant metabolites concentration from a spectrum.

Even though it is possible to detect almost any metabolite in the body, QUIBIM is currently focusing on the detection of the 2-Hydroxyglurate (2HG) because of its influence in the prediction of low-grade brain gliomas. 2HG is a metabolite, normally present at very low levels in healthy cells and tissues because of the reduction of α-ketoglutarate (α-KG) catalyzed by isocitrate dehydrogenase (IDH) protein. The IDH enzyme mutation in low-grade brain gliomas produces an accumulation of 2-HG which makes the measurement of this oncometabolite crucial to distinguish IDH-mutant gliomas from other brain mass (Stefan, et al., 2010).

MRI quality assurance

The quality assurance of the Magnetic Resonance Imaging (MRI) images is important and mandatory to ensure consequent and reliable results from the MR analysis. For that reason, a qualitative evaluation of the images is performed by QUIBIM to guarantee a strict and standard control of the images by checking the accomplishment of the MRI acquisition protocol set and the absence of artifacts.

MRS quality assurance

The first technical information QUIBIM provides with, is the graphical representation of the voxel placement in the sagittal, axial and coronal view depicted in a structural image. This quality review is important in order to assure the correct position of the voxel in the MRS acquisition avoiding necrotic tissue or non-tumor substances what can affect MRS results.

MRS quantification

Once the quality check of the MRS acquisition is done, QUIBIM produces a technical structured report, quantitative and graphical information about the MRS analysis.

QUIBIM_MRS Structured report

The report, apart from the main images and quantitative information, includes a spectrum graph and the voxel placement. Each peak of the spectrum corresponds to a different substance or metabolite with a different resonance frequency. This difference is usually measured in an independent scale besides the principal magnetic field (parts per million). The intensity of each peak is related to the concentration of the substances in the studied volume (Sánchez, et al., 2001).

To perform the analysis, the voxel in the tumor area and a reference acquisition corresponding to water are required. An automatic analysis of the spectrum is done providing this information and setting the up and down boundary value in ppm according to the metabolite searched. Then, we automatically process the spectrum filtering the water peak in the time domain, reducing the spectrum noise, adjusting the ppm scale according to Creatine and Choline position, and correcting the phase of the signal in order to also correct the baseline offset level to ensure the reliability and accuracy of metabolites’ concentration (Martí-Bonmatí & Alberich-Bayarri, 2013).

Therefore, it is possible to obtain from the studied metabolite its absolute concentration, the percentage of standard deviation (%SD) and its relative concentration using Creatine as a reference value (/Cr), sometimes adding other reference metabolites like PCr as the example of Figure 1 (/Cr+PCr).

The information provided by the standard deviation is essential to read MRS results because absolute concentration reliability depends on them: metabolites with a %SD less than 50 are practically undetectable with this data whereas a %SD<20 is a rough criterion for estimates of acceptable reliability. In addition, the third column indicates ratio relative to /Cr. This quantitative information will appear in the lower part of the report followed by a space for the signature of the scientist from QUIBIM once the analysis is done.

MRS analysis is now available in QUIBIM Precision® Platform to support oncologists and radiologists in brain gliomas treatment and diagnosis.

boton try quibim


  • Francesca Branzoli, *. A. (2018). Highly specific determination of IDH status using edited in vivo magnetic resonance spectroscopy. Neuro-Oncology, 907-916.
  • Martí-Bonmatí, L., & Alberich-Bayarri, Á. (2013). Disease Biomarkers: Modelling MR Spectroscopy and Clinical Applications in Bioinformatics of Human Proteomics. Valencia: Springer.
  • Min Zhou, *. Y. (2018). Diagnostic accuracy of 2-hydroxyglutarate magnetic resonance spectroscopy in newly diagnosed brain mass and suspected recurrent gliomas. Neuro-Oncology, 1262-1271.
  • Sánchez, J., Santos, A., SantaMarta, C., Benito, C., Benito, M., & Desco, M. (2001). Herramienta de Análisis de Espectros de RM. CASEIB. Madrid.
  • Stefan, G., Rob A., C., Mark D., M., Edward M., D., Mark A., B., Hyun Gyung, J., . . . David P., S. (2010). Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. Journal of experimental Medicine, 207(2), 339–344.