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!