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Bianca Muresan

INTERVIEW | Quantification of body composition to detect malnutrition in oncological patients

Bianca Tabita Muresan, master in personalized and community nutrition by the University of Valencia and currently PhD student in nutrition and endocrinology, took time to answer some questions about the relation between malnutrition in oncology and quantitative imaging after her one week training period at QUIBIM.

Tell us about yourself and how did you come to know QUIBIM before your training period with us?

I am a PhD student at the Faculty of Medicine (University of Valencia) and my doctorate thesis is about the diagnosis of sarcopenia and measurement of body composition in cancer patients, using the CT scans previously done to the patient for the disease diagnose or the radiotherapy treatment planning. Specifically, I use the powerful information hidden in CT scans to detect malnutrition in those cancer patients with high risk of developing it, as for example happens in patients suffering from head and neck or lung cancer, or cancers that affects the digestive system.  After detecting malnourished cancer patients, I work together with the Endocrinology and Nutrition Department to translate nutrition goals for improving cancer treatment collateral effects (as for example fatigue, vomiting, diarrhea, dysphagia, among others), or to assist with nutritional supplements of feeding tubes to help keep up a patient’s strength during treatment.

Due to my Master practicum and some research projects I collaborate since four years ago with many investigators of La Fe Health Research Institute in Valencia. Moreover, my thesis director (Dr. Alegría Montoro Pastor), recommended me to contact QUIBIM and GIBI for a short internship during my university studies and I knew that could improve my performance at work, as well as help me to reach new skills about imaging biomarkers.


Your field of expertise is malnutrition in Oncology. The Quantitative Medical Imaging field has significantly increased in the last years, but in hospitals, we still use anthropometric mediations as for example body mass index (kg/m2) for evaluating body composition, so what do you think is the main reason for using medical imaging?

Anthropometric variables, as for example body mass index, are the most commonly employed measures for detecting nutritional status in epidemiology, because of their simplicity and easy data collection. The problem is that in clinical practice, these parameters have a significant inter and intra observer variability. Moreover, they don’t allow the detection of major body compartments (such as lean body mass or adipose tissue), the estimation of body composition or to description of body fat distribution, which have been proved to be highly correlated to different degrees of malnutrition and sarcopenic obesity. In those cases, medical imaging, as for example computed tomography (CT) and magnetic resonance imaging (MRI) would facilitate the quantification of body fat and muscle mass distribution. For this purpose, 3D image segmentation techniques are applied, which allow the differentiation of subcutaneous fat, visceral fat, muscles and fatty infiltration within the muscular tissue. As of today, most of the 3D image segmentation techniques require manual correction but new artificial intelligence (AI) algorithms based on Convolutional Neural Networks (CNN) are providing promising results in the field.

What means sarcopenia and sarcopenic obesity and which are the effects of these terms to oncological outcomes? How could you measure these terms with medical imaging?

Sarcopenia is defined as a loss of skeletal muscle mass and decrease muscle function with or without loss of body weight and body fat. This condition could also occur in patients who present overweight, coexisting both pathologies: sarcopenia and obesity. The prevalence of sarcopenia in cancer patients was found to be a bad prognostic factor for disease progression and survival, as well as a negative predictor of toxicity levels and treatment complications.

In clinical practice we use bio–impedance analysis (BIA) for the evaluation of lean body mass and total adipose tissue, and this could also be analyzed by dual x-ray absorptiometry (DEXA). In addition, CT and MRI have shown to be excellent tools in assessing muscle mass tissue and different fat areas inside the body (subcutaneous, visceral and intramuscular). In the last years, several studies have suggested different cut-off values for detecting low muscle mass and low muscle density.  With this information, we try to detect pathological processes as for example pre-sarcopenia (which means loss of muscle mass without loss of muscle strength), myosteatosis (which means fat within and around skeletal muscle) and visceral obesity. The evaluation of muscle function for the diagnosis of sarcopenia is completed by measuring handgrip strength.

Which indicators are you working with at present and which ones do you think are the best by incorporating quantitative imaging in the future to analyze body composition in hospitals?

At the moment, we measure skeletal muscle mass, intramuscular adipose tissue, visceral adipose tissue and subcutaneous adipose tissue in cancer patients before starting cancer treatment. On the other hand, we try to study full body composition before starting radio – chemotherapy. As being a nutritionist, this technique helps to improve my work using the best technology. Related to the quantification of these imaging biomarkers, we have already sent different communications to Spanish congresses and written scientific articles for my PhD thesis, which are now in review.  Moreover, our department also works trying to find out different anatomical locations with major loss of muscle mass before cancer treatment, as well as correlating the prevalence of sarcopenia with toxicity levels and quality of life after antineoplastic treatment.

 For the future, I believe it would be helpful to include bone measurements to identify patients with osteopenia and in those patients with MRI, determine fat concentration in the liver (steatosis) would also be important for detecting important nutritional problems.

Finally, tell us shortly how did you find the experience at QUIBIM and GIBI these days and how could this training period improve your work?

 First of all, I would like to thank all the teamwork of QUIBIM and GIBI for offering me the opportunity to learn about the most advanced technology. I have definitely improved my skills about discovering interesting quantitative imaging biomarkers as for example fat liver and iron concentration, which are very important for future nutrition researches. It was absolutely an enriching experience. Thank you! 

Thank you Bianca for your time!

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!

nvidia banner

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.


Presentation of QUIBIM Precision platform at PARENCHIMA meeting in Prague, October 2018

QUIBIM continua su liderazgo en imagen médica e inteligencia artificial gestionando los datos y los algoritmos de un proyecto europeo en 25 países

QUIBIM ha sido seleccionado como el socio principal para gestionar todos los datos de imagen médica y los algoritmos de análisis de la iniciativa PARENCHIMA, un proyecto europeo de la acción COST (European Cooperation in Science and Technology) que se inició en abril de 2017 con el objetivo de impulsar el uso de biomarcadores de imagen calculados con resonancia magnética para mejorar el manejo de pacientes con enfermedad renal crónica. Multiples Investigadores científicos de 25 países europeos, líderes en imagen médica y enfermedad renal, utilizarán en esta iniciativa la plataforma QUIBIM Precision® para la realización de análisis avanzados. Uno de los desafíos del proyecto es centralizar los datos de imágenes médicas y los algoritmos de todos los socios para permitir la comparación entre protocolos de adquisición y algoritmos computacionales en términos de calidad y precisión. El resultado del proyecto consistirá en dos nuevos estándares para la adquisición y análisis de imágenes de resonancia magnética en la enfermedad renal crónica.

En la pasada reunión anual en Praga, los días 4 y 5 de octubre, se formalizó el acuerdo entre los socios del consorcio PARENCHIMA y QUIBIM. La plataforma QUIBIM Precision fue seleccionada como el mejor sistema para la gestión de las imágenes y de integración de los nuevos algoritmos de análisis.

Steven Sourbron, profesor de resonancia magnética en la Universidad de Leeds y coordinador del proyecto, manifestó: “Estoy encantado de que QUIBIM haya elegido asociarse con PARENCHIMA para ayudarnos a mejorar los resultados en beneficio de los pacientes, al hacer accesibles estas innovadoras y prometedoras técnicas para su uso en ensayos clínicos y manejo asistencial del paciente”. Frank Zöllner, profesor adjunto de física médica en la Universidad de Heidelberg y líder del grupo de trabajo encargado de la base de datos y el software de PARENCHIMA, comentói que “QUIBIM tiene una gran experiencia en algoritmos de inteligencia artificial y gestión de datos de imagen médica y estamos seguros de que son el colaborador adecuado que necesita este proyecto”. Angel Alberich-Bayarri, CEO de QUIBIM expresó que “QUIBIM está orgulloso de ser parte de este proyecto donde se generarán nuevos estándares de adquisición y análisis de imágenes en la enfermedad renal crónica, un escenario clínico no abordado previamente de forma conjunta. El impacto de este estudio será global y mejorará la vida de millones de pacientes, ya que esta enfermedad afecta al 10% de la población mundial.”



Hoy en día, los biomarcadores de resonancia magnética renal están infrautilizados no sólo en la investigación, pero también en la práctica clínica, principalmente debido a la falta de difusión y a la necesidad de desarrollo de técnicas propias. Transferir soluciones a otros centros donde funcionen y estén validadas es, por lo tanto, un reto todavía no resuelto, lo que conlleva una replicación significativa de esfuerzos, una falta de estandarización en los métodos y dificultades para comparar los resultados entre los centros. Esto también limita la comercialización y dificulta la creación de ensayos multicéntricos y la traslación a la práctica clínica.

El objetivo general de PARENCHIMA es eliminar las principales barreras para un estudio clínico más extenso y la consiguiente explotación comercial de los biomarcadores de resonancia magnética renal.

PARENCHIMA coordinará la investigación de los principales grupos europeos en esta área para:

  • mejorar la reproducibilidad y estandarización de los biomarcadores de resonancia magnética renal;
  • aumentar su disponibilidad desarrollando un conjunto de herramientas de acceso abierto con herramientas software y datos;
  • demostrar la validez biológica y la utilidad clínica en un estudio clínico prospectivo multicéntrico.

Para aumentar el impacto de este proyecto, hemos decidido unir nuestros esfuerzos. Más información en:



QUIBIM es una empresa de Valencia (España) que aplica inteligencia artificial y modelos computacionales avanzados a las imágenes radiológicas para medir de manera objetiva los cambios producidos por una lesión o por los tratamientos farmacológico, y ofrece información cuantitativa adicional al enfoque cualitativo de la radiología. La tecnología y los servicios de QUIBIM se aplican en la práctica clínica, los ensayos clínicos, la formación en radiología y los proyectos de investigación. Más información en:

Presentation of QUIBIM Precision platform at PARENCHIMA meeting in Prague, October 2018

QUIBIM to manage imaging data and AI for European project PARENCHIMA across 25 countries

QUIBIM has been selected as the main partner to manage all imaging data and analysis algorithms of the PARENCHIMA initiative, a COST action project initiated in April 2017 with the objective of boosting the use of renal MRI biomarkers to improve the management of chronic kidney disease patients. The leading scientific researchers in medical imaging of the kidney from 25 European countries will be using QUIBIM Precision® platform for highly advanced image analysis. One of the challenges of the project is to centralize the medical imaging data and algorithms from all partners to allow for acquisition protocols and algorithms comparison in terms of quality and precision. The outcome of the project will consist of two new standards for image acquisition and analysis in MR of the kidney.

In the past annual meeting of the consortium in Prague, which took place on 4-5 October, an agreement was established between QUIBIM and PARENCHIMA consortium partners for the selection of QUIBIM Precision platform as the best system for images management and integration of new analysis algorithms.

Steven Sourbron, Lecturer in Magnetic Resonance Imaging at the University of Leeds and project coordinator, expressed “I am delighted that QUIBIM has chosen to partner with PARENCHIMA, helping us to improve outcomes for patients by opening up these promising new methods for use in clinical trials and patient management”. Frank Zöllner, adjunct Professor for medical physics at the University of Heidelberg and leader of working group for PARENCHIMA database and software said “QUIBIM has strong expertise in AI algorithms and managing data and we are confident that they are the right collaborator for this project”. Angel Alberich-Bayarri, CEO of QUIBIM said that ”QUIBIM is excited to be part of this project, which will lead to new standards of image acquisition and analysis in Chronic Kidney Disease (CKD), an unaddressed clinical scenario. I am confident that the impact of this study will be global and improve lives of millions of patients, since it is a disease affecting 10% of population worldwide”



Renal MRI biomarkers are today underused in research and in clinical practice due to the need for dedicated in-house expertise and development. Transferring solutions to other centres is therefore a challenge, and this leads to a significant duplication of efforts, a lack of standardisation in the methods, and difficulties in comparing results between centres. This also limits commercial exploitation, and hinders the set-up of multi-centre trials or translation into clinical practice.

The overall aim of PARENCHIMA is to eliminate the main barriers to the broader study, commercial exploitation and clinical use of renal MRI biomarkers.

PARENCHIMA will coordinate the research of leading European groups in this area to:

  • improve the reproducibility and standardisation of renal MRI biomarkers;
  • increase their availability by developing an open-access toolbox with software and data;
  • demonstrate biological validity and clinical utility in a prospective multicentre clinical study.

In order to increase the impact of this project we are reaching out to join the efforts. More info at:



QUIBIM is a company from Valencia (Spain) which applies artificial intelligence and advanced computational models to radiological images to objectively measure changes produced by a lesion or by a pharmacological treatment, offering additional quantitative information to the qualitative approach of radiology. QUIBIM technology and services are applied in clinical practice, clinical trials, radiology education and research projects. More info at:


QUIBIM provides the platform for the GALEN Advanced Course organized by European School of Radiology

  • QUIBIM provided the software for the hands-on workshop activities, allowing a much more dynamic and interactive case discussion

QUIBIM was honored with the chance to participate in the last edition of the “GALEN Advanced Course on Oncologic Imaging of the Abdomen”. The event was organized by the European School of Radiology (ESOR), the educational initiative of the European Society of Radiology (ESR).

This course was aimed at senior residents, board-certified radiologists and fellows interested in abdominal oncologic imaging and focused on the application of the latest technical advancements and the new European guidelines for imaging.

ESOR_QUIBIM PrecisionQUIBIM provided the software for the hands-on workshop activities, allowing a much more dynamic and interactive case discussion. Based on our QUIBIM Precision® cloud Platform, this tool provides a powerful framework for the creation of new users, the uploading of imaging studies and relevant documentation and for the administration of the course.

Furthermore, the platform has been developed to provide lecturers with convenient features to share studies with students, visualize and edit them using our zero-footprint embedded DICOM Web Viewer and, most important, analyze the studies with any of the imaging biomarker plugins available at QUIBIM Precision®.

This event represents a great milestone for QUIBIM, as ESOR, with over 19.000 participants in more than 250 ESOR courses, has become the major provider of complementary radiological education in Europe and worldwide.



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.

boton try quibim

ESGAR Conference CCD Dublin Ireland 2018.

Co-Founder Prof. Luis Martí-Bonmatí receives ESGAR Gold Medal

During the 29th Annual Meeting and Postgraduate Course of ESGAR (European Society of Gastrointestinal and Abdominal Radiology) held in Dublin, our co-founder, Prof. Luis Marti-Bonmati received the ESGAR Gold Medal, the society’s highest award for outstanding contributions to the scientific community.

All QUIBIM team is proud and honoured. We congratulate Prof. Martí-Bonmatí on this achievement.

ESGAR Conference CCD Dublin Ireland 2018.

ESGAR Conference CCD Dublin Ireland 2018.

Photo credit: Roger Kenny Photography

Photo legend (from left to right): Steve Halligan (ESGAR President), Luis Martí-Bonmatí (Gold Medallist), Celso Matos (ESGAR Past President), Helen Fenlon (ESGAR Meeting President 2018).

Link to Insights into Imaging post:


Ines Perea, Strategic Advisor at QUIBIM

Quibim’s Strategic Advisor Inés Perea discusses the radical disruptions that quantitative imaging analysis can provide to cancer research

Quibim’s Strategic Advisor Inés Perea discusses the radical disruptions that quantitative imaging analysis can provide to cancer research amongst other areas

Inés Perea is a Doctor that graduated with honors in the Pharmacy and Executive Business Program by Instituto de Empresa of Madrid. Mrs. Perea has extensive and versatile experience spanning more than 20 years in the Pharmaceutical industry working within different roles within the Medical, Access and Commercial (Marketing, Sales, Strategic Planning) areas and international experience in Global and regional teams. Her main area of expertise is oncology and biologics where she has been involved in the strategic planning and commercialization of more than 10 drugs/indications, most of them linked to some form of biomarker diagnosis.

How did you come to know QUIBIM before becoming their strategic advisor?

I was in contact with founder and CEO, Angel Bayarri months prior to my start with Quibim. A mutual friend of ours, a director of a large genetics biomarker laboratory in Spain was working specifically on oncology genetic biomarker testing and I would get involved with his organization in the arenas of strategic value and development, as those are two of my core competencies. I was involved in the development and strategic value of genetic/biologic biomarkers in clinical trials in oncology. It was back in the 90’s when biomarkers were a thing of science fiction. Well, he introduced me to Angel and I really thought he had a fantastic idea when it came to implementation of 3-d imaging biomarkers and using algorithms to quantitatively analyze tumors. Once I learned about Quibim’s technology, I knew that the growth could be exponential if he partnered with strategic partners.

Your field of expertise is Oncology. The Quantitative Medical Imaging field has evolved significantly but we still use RECIST criteria for evaluation of treatment responses, what do you think is the main reason for this? Standardization?

This is the foundation for therapeutic trials. It is a standard when examining patients, however, it has some practical limitations. For example, with the latest breakthrough area in oncology research, immunotherapy, many immuno-oncology drugs are known to cause fluctuations in tumor size, which when analyzed simply with RECIST criteria, would appear as an increase to the tumor size, but it is now understood that tumor fluctuations occur regularly during immunotherapy treatments, often causing the tumor to grow prior to cell DE progression, which is in fact a good sign that would be captured with RECIST criteria well after the fact. In those cases, RECIST does not allow for factoring in the context of the holistic viewpoint that quantitative digital image analysis would facilitate.

What therapeutic indications are best served by incorporating quantitative imaging analysis into clinical research trials?

There is a very simple answer to this question: any therapeutic indication where standardized radiological assessments are a primary or secondary safety or efficacy endpoint of a study. This includes solid tumors, liver diseases, brain diseases, lung diseases, and many others.

Let’s discuss costs. Quantitative imaging analysis sounds expensive. However, automation and extraction of biomarkers as soon as a hospital acquires and uploads images related to a clinical trial can and do have a macroeconomic effect of lowering costs, therefore long-term costs decrease. What do you respond with when someone brings up this objection?

You are right, it is a tremendous cost saver from a macroeconomic point of view. What will continue to help this cause are national health systems, private funding, insurers, and other payers to see the long-term cost savings and efficiencies that quantitative imaging analysis facilitates. Essentially, we need to get quantitative image analysis included in the health system reimbursement system much like traditional biomarkers of a decade ago. As a matter of fact, it took health systems quite a bit of time to include standard biomarkers that we have all grown accustomed to, included on their reimbursement schedules. While it essentially all boils down to payers and health authorities, us researchers and technology pioneers need to continue to prove medium and long-term efficiencies and validate it with radiologists, clinicians and imaging researchers. Once we are able to do that effectively, the market will adjust accordingly, it always does.

The value seems to be in real-time results that can help in both safety and efficacy outcomes. Please expand further on this thought.

Real time results are everything if you really think about it. The main benefit is obviously how this technology facilitates the real time patient care capabilities of healthcare practitioners. Safety and efficacy instant results from treatments are critical when analyzing real time clinical research data. Imaging biomarkers can and will play a tremendous role in improving patient standard of care, and ultimately can make a real impact on disease outcomes.

by: Dan Sfera, also available at:

QUIBIM Imaging Biomarkers made transparent

QUIBIM, AI imaging disruption to showcase the value of Precision

We are in the era of Precision Medicine, and so is Radiology. Nowadays, main imaging modalities like X-ray, computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET) and hybrid machines, among others, have become measurement instruments. Images are not only pictures anymore, but thanks to the application of computational analysis and artificial intelligence, they are data, as you can learn in this excellent manuscript in Radiology.

Nowadays, when radiologists perform measurements of different organs, tissues and lesion properties using a workstation, they are used to get a number (i.e. lesion volume or perfusion). If they take the same images and they get to analyze them in a workstation from another vendor (not straightforward), it is pretty sure that they will obtain different results. This issue has introduced a sense of lack of standardization and homogenization in the quantitative medical imaging field.

I like to say that value is to trust in the product, and we have decided to be the first company in the world to open the validation process and tests results of our imaging biomarkers. Every time we buy a measurement device for daily life purposes (i.e. thermometer) we know the degree of uncertainty, why wouldn’t we do the same in AI algorithms and quantitative imaging?

We are proud to make this announcement at ECR 2018: Now it is possible to see the precision, accuracy and clinical evaluation results of our imaging biomarkers. We provide the precision (through Coefficient of Variation, CoV) and accuracy (through relative error, e) values through the publication of QUIBIM Technical Datasheets that you can find in the resources section of our webpage.

With this strategy QUIBIM is going a step further by being the first multi-vendor, web-based and real precision Medicine company of the medical imaging & AI market.

Concerned by the accuracy of your measurements? Let’s work together.