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Katherine Wilisch Ramírez

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!

FEDER+Ivace+Declaracion-CS

QUIBIM recibe la ayuda del IVACE – Certificación I+D+i 2018

QUIBIM ha obtenido financiación del Institut Valencià de Competitivitat Empresarial (IVACE) dentro del programa Certificación I+D+i 2018. Una ayuda financiera de la Unión Europea (Expediente: IMACPA/2018/205) para la realización del proyecto “DESARROLLO DE NUEVOS ALGORITMOS DE INTELIGENCIA ARTIFICIAL APLICADOS AL ANÁLISIS AUTOMATIZADO DE BIOMARCADORES”. 

El objetivo del proyecto es el desarrollo de nuevos algoritmos para la segmentación automática de distintas áreas anatómicas del cuerpo humano (espina dorsal, hígado, pulmón, corazón, etc.). Estos algoritmos, basados en nuevas técnicas de Inteligencia Artificial, están específicamente diseñados y entrenados para discernir las regiones a analizar, segmentar órganos específicos y realizar mediciones automatizadas de biomarcadores de imagen.

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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.

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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.

WANT TO DISCOVER OUR PLATFORM?

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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.

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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.

MEET US!

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Welcome our new team members!

We are very pleased to announce the last 4 incorporations to our QUIBIM Team. QUIBIM is in a continuous growth process, thanks to our customers, collaborators and partners like investors and funding agencies.

These new incorporations represent an important milestone for the company as they will support the development department and the business development unit to boost the company scale-up phase.

The new Quibimers:

One of the profiles incorporated comes from far away. Mr. Kabir Mahajan has a Master’s in Business Administration (MBA) from IESE Business School (Barcelona) and has studied Commerce and International Marketing from the University of Delhi (India). Prior to his MBA he was the Associate Director at Mahajan Imaging, India’s leading radiology and nuclear medicine services provider, and the co-founder of Mahajan Imaging Education and Research Academy. He has come to Valencia from New Delhi, India to support QUIBIM in strategy & business development as Chief Strategy Officer.

Also, we bring experience in the field of business development in hospitals and clinical trials with the incorporation of Mrs. Inmaculada Segarra Granell, a Pharmacist by the University of Valencia with a Master’s Degree in Development and Monitoring of National and International Clinical Trials. She previously worked for GLAXOSMITHKLINE during 15 years as account manager and as study coordinator in Hospital Universitario y Politecnico La Fe of Valencia in the Pediatrics. She has incorporated to QUIBIM team in the Area of Business Development looking for new business opportunities.

A solid science track record for our oncology pillar is the key value of Dr. Ismael Gonzalez Valverde, Chemical Engineer with a Master’s Degree in Bioengineering and a PhD in Biomedical Engineering. Previously, he was part of the M2BE team (Aragon Institute of Engineering Research) managing complex and interdisciplinary problems in the in silico modelling and biomechanics field. Ismael has experience in chemical and biological laboratories, as well as extensive programming knowledge in various programming languages and the administration of high-performance computing systems and Linux servers. Moreover, he is author of several scientific articles and communications to international congresses. He has joined QUIBIM team as the leading Image Analysis Scientist in oncology.

Last but not least, we also take care on incorporating talent with a great career ahead, young profiles who have significantly outperformed for their young age. This is the case of Mrs. Paula Moreno Ruiz, a Biomedical Engineer with a Master’s Degree in Biomedical Engineering by the Polytechnic University of Valencia. She has recently developed her Master Thesis about Medical Imaging Registration in the Vrije University of Brussels. She was working in a Research Center for Health Economics and Management (CIEGS) and in the Biomedical Imaging Research Group (GIBI230) of the Hospital Universitario y Politécnico La Fe as a scholarship. As she knows the company and this job environment, she has joined QUIBIM as the Team Coordinator supporting the Marketing, Communication, Administration and Management department.

Congratulations! We officially welcome them and hope they have a prosperous career in the company.

 

Meet our team
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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.

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QUIBIM recibe la ayuda del IVACE – Proyectos de I+D de PYME (PIDI-CV)

QUIBIM ha obtenido financiación del Institut Valencià de Competitivitat Empresarial (IVACE) dentro del programa “Proyectos de I+D de PYME (PIDI-CV)” para la realización del proyecto “DESARROLLO DE ALGORITMOS DE INTELIGENCIA ARTIFICIAL PARA LA DETECCIÓN AUTOMATIZADA DE VÉRTEBRAS A PARTIR DE IMÁGENES DE TC EN OSTEOPOROSIS” con número de Expediente: IMITDA/2017/120.

OBJETIVOS Y RESULTADOS DEL PROYECTO

El presente proyecto tiene el objetivo de desarrollar nuevas técnicas de análisis de imagen y algoritmos de Inteligencia Artificial (Machine Learning y Deep Learning) aplicados  a la caracterización de la columna vertebral a través de la segmentación automática de vértebras en imágenes de TC (tomografía computarizada), que permita el soporte al radiodiagnóstico en pacientes con osteoporosis. Para ello, se crea una nueva herramienta software de soporte que permita detectar e identificar automáticamente las vértebras en una imagen de TC. Una vez integrado este nuevo desarrollo como un nuevo módulo en nuestra plataforma Quibim Precission, estaremos en disposición de  caracterizar la microarquitectura ósea de cada una de las vértebras, realizar una evaluación de la misma y ofrecer un radiodiagnóstico avanzado capaz de aportar mayor información sobre la enfermedad ósea del paciente. 

La propuesta de valor para QUIBIM es contribuir con el presente proyecto a completar sus líneas de I+D, en concreto su línea  musculoesquelética. Así este proyecto ha supuesto la creación de un sistema especializado en la caracterización automática de la microarquitectura ósea vertebral, ofreciendo al radiólogo información cuantitativa sobre ésta, para mejorar la evaluación de tratamientos médicos y agilizar el control y seguimiento de pacientes con osteoporosis.

QUIBIM RECIBE LA AYUDA DEL IVACE-INTERNACIONALIZACION 2017

QUIBIM es un proyecto empresarial de alto impacto social y sanitario, que extrae información cuantitativa de las imágenes médicas radiológicas, mediante técnicas innovadoras y avanzadas de procesado computacional, con el objetivo de mejorar los procesos de diagnóstico de enfermedades con alta incidencia y evaluar adecuadamente los cambios que producen los tratamientos farmacológicos en el organismo.

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Durante 2017 el proyecto de internacionalización QUIBIM ha recibido la ayuda IVACE – “ACCIONES DE PROMOCIÓN EN EL EXTERIOR QUIBIM 2017” (ITAPIN/2017/447) con el apoyo del Fondo Europeo de Desarrollo Regional (FEDER) por un importe de 4.405,16€

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El proyecto QUIBIM recibe el apoyo del FONDO EUROPEO DE DESARROLLO FEDER

QUIBIM es un proyecto empresarial de alto impacto social y sanitario, que extrae información cuantitativa de las imágenes médicas radiológicas, mediante técnicas innovadoras y avanzadas de procesado computacional, con el objetivo de mejorar los procesos de diagnóstico de enfermedades con alta incidencia y evaluar adecuadamente los cambios que producen los tratamientos farmacológicos en el organismo.

Durante 2016 el proyecto de internacionalización QUIBIM ha recibido la ayuda IVACE (ITAPIN IT16 PLANES DE INTERNACIONALIZACIÓN PYME CV 2016) con el apoyo del Fondo Europeo de Desarrollo Regional (FEDER) con el objetivo de presentar nuestros servicios de diagnóstico y análisis avanzado de imagen médica y dar a conocer nuestra plataforma de análisis QUIBIM Precision® para el análisis de Biomarcadores de Imagen.

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