A bioimpedance analysis of head-and-neck cancer patients undergoing radiotherapy

Original Article

A bioimpedance analysis of head-and-neck cancer patients undergoing radiotherapy

K. Kohli, PhD*, R. Corns, PhD, K. Vinnakota, MD, P. Steiner, BSc*, C. Elith, PhD*, D. Schellenberg, MD*, W. Kwan, MD*, A. Karvat, MD*

doi: http://dx.doi.org/10.3747/co.25.3920


Malnutrition is a frequent manifestation in patients with head-and-neck cancer undergoing radiation therapy and a major contributor to morbidity and mortality. Thus, body composition is an important component of an overall evaluation of nutrition in cancer patients. Malnutrition is characterized by weight loss, loss of muscle mass, changes in cell membrane integrity, and alterations in fluid balance. Bioelectrical impedance analysis is a method to analyze body composition and includes parameters such as intracellular water content, extracellular water content, and cell membrane integrity in the form of a phase angle (Φ). Bioelectrical impedance analysis has consistently been shown to have prognostic value with respect to mortality and morbidity in patients undergoing chemotherapy. The goal of the present study was to evaluate the relationship between Φ, time, intracellular water content, and weight for head-and-neck cancer patients undergoing radiotherapy. The results demonstrate that Φ decreases with time and increases with intracellular water content and weight.

KEYWORDS: Head-and-neck cancer, radiation therapy, bioimpedance analysis, phase angle, intracellular water, extracellular water, malnutrition


Head-and-neck squamous cell cancer (hnscc) includes cancer of the oral cavity, oropharynx, hypopharynx, and larynx1. It constitutes the 7th most common malignancy in the world, and it is typically treated with external-beam radiation. Common treatment-related complications in hnscc patients include dysphagia, mucositis, and generalized nausea1,2. Because of those side effects, daily food intake is often diminished, leading to unintentional weight loss and malnutrition. Malnutrition is a frequent manifestation in patients with advanced cancer and a major contributor to morbidity and mortality3. It is reported that, before the start of radiotherapy (rt) or chemoradiotherapy, 3%–52% of patients are malnourished1,4. During rt and chemoradiotherapy, the percentage of malnourished patients rises to 44%–88%1,5. Malnutrition in hnscc is not only affected by radiation, it can also be further exaggerated by mechanical difficulties (swallowing or chewing, for example) resulting from mass effect or prior surgery, by poor nutrition related to heavy smoking and excessive alcohol consumption, and by the toxicities of multimodal cancer treatment such as chemoradiotherapy6.

Malnutrition is characterized by weight loss, loss of muscle mass, changes in cell membrane integrity, and alterations in fluid balance7. Body composition is an important component of overall nutrition evaluation in cancer patients710. However, there is no standard clinical mechanism of assessing nutrition status other than measuring current weight and attempting to quantify weight loss—a technique that is fraught with difficulty because it relies on patient memory and does not take into account nutrition status at the patient’s own “baseline.” Of various available techniques, bioelectrical impedance analysis (bia) is a method for body composition analysis. It uses parameters such as body fat mass, extracellular water content (ecw), intracellular water content (icw), and phase angle (Φ). It is able to overcome the challenges of commonly used methods in nutrition evaluation such as anthropometry (weight change, arm muscle circumference, triceps-fold thickness, and so on) and laboratory measurements (serum albumin, transferrin assay, and nitrogen-balance studies). In addition, bia is noninvasive and easily reproducible, and it can be performed quickly at bedside11,12.

Bioelectrical impedance analysis measures body component resistance and capacitance by recording a voltage drop in applied current8. Capacitance causes the voltage to lag behind the current, and the measurement of that lag is the Φ shift. The shift is quantified geometrically as the angular transformation of the ratio of capacitance to resistance. The Φ reflects the relative contributions of fluid (resistance) and cellular membranes (capacitance) of the human body, and by definition, it is positively associated with capacitance and negatively associated with resistance. That analysis suggests that a lower Φ implies cell death or decreased cell integrity and that a higher Φ suggests larger numbers of intact cell membranes. The Φ for a healthy individual varies between 4 and 10 degrees9.

The bioelectrical Φ has consistently been shown in several studies to have great prognostic relevance with respect to morbidity and mortality in various disease processes, including hiv, sepsis, breast cancer, lung cancer, and colon cancer1016. In addition, Φ has been found to be an indicator for cell membrane integrity, distribution of water between the intracellular and extracellular spaces, and prediction of body cell mass in various other disease conditions1719.

Although extensive studies have been conducted on the properties of Φ in cancer patients, no studies have looked at the changes in phase angle that occur in patients who are undergoing rt treatment for head-and-neck cancer. In the present work, we characterized changes in Φ with time elapsed (t) since the onset of rt treatment, with percentage icw content (w), and with percentage weight (m) for head-and-neck cancer patients. The goal was to correlate those variables in terms of the overall population rather than the individuals. We extracted population statistics by performing repeated measures of t, w, m, and Φ for each patient. The slopes and intercepts from univariate and multivariate linear models of those variables are normally distributed, and the means and standard deviations of the distributions are characteristic of the population.

Considering the strong association between poor nutrition and side effects of rt, bia can be a powerful prognostic tool. The focus is to understand the changes in Φ that occur for patients who are undergoing rt.



After approval was obtained through the Research Ethics Board of BC Cancer, 20 patients were recruited for the study. All participants signed informed consent forms agreeing to participate. All patients were being treated with radical curative-intent rt, with or without concurrent chemotherapy, for head-and-neck cancer. Patients were accrued from August 2012 until July 2015. Those receiving postsurgical rt or palliative rt, those with recurrent or metastatic disease, and those with a history of irregular heart rhythm or seizure disorder were excluded. In addition, patients with an implanted electrical device (for example, a pacemaker) were excluded because of the possibility that the current induced by the impedance measurements would interfere with the device.

BIA Measurements

The bia measurements were performed using the BIA 450 Bioimpedance Analyzer from Biodynamics Corporation (Seattle, WA, U.S.A.). For safety during the bia measurements, the frequency of the alternating current is typically 50 kHz, because a small current of this frequency passing through a body is unlikely to stimulate electrically excitable tissues such as nerve cells and cardiac muscles. In practice, the magnitude of the current, about 800 μA, is chosen to be low enough so as not to be perceived by the participant, but high enough to produce voltages that are above interfering “noise.” Thus, for the present study, we applied alternating electrical currents of 800 μA at 50 kHz.

Measurements were made according to the standardized protocol found in the operating manual for the analyzer. In brief, participants were measured in the supine position with arms and legs abducted from the body. Four adhesive electrocardiographic electrodes were placed on the dorsum of both the wrist and the ankle on the dominant side of the body.

Three separate measurements of electrical impedance were taken, all by a member of the health care team. The first measurement was taken just before treatment on the first treatment day; the second was taken at the halfway point of the treatment course (depending on the treatment duration for each patient, this measurement was taken approximately 2–3 weeks after the start of rt); and the third was taken immediately after rt on the last treatment day. The resistance, reactance, Φ, icw, and ecw values were recorded for each patient at each of those time points. Data for 2 patients (patients 8 and 15) were incorrectly recorded and not analyzable, and therefore had to be omitted from further analysis and discussion. Demographic characteristics such as weight, height, age, and sex were also recorded for all subjects.

The Φ (in degrees) was calculated using the equation

where XC is the measured reactance, and R is the measured resistance. The analyzer uses an equation developed by multiple linear regression analysis to provide an estimate of water compartments.


Patient Demographics

Table i presents descriptive statistics for the 20 male patients diagnosed with hnscc who participated in the study.

TABLE I Patient characteristics


Univariate Analysis

We started with univariate linear models to describe Φ as a function of t, w, and m. The icw and ecw are inversely related when they are expressed as a percentage of total body water because the ecw equals 100% – w. We therefore restricted our focus to icw alone.

The goal of the univariate model was to determine population properties. A linear fit was determined for each patient’s data, and in Figure 1, plots of the fitted lines are shown separately from the data points for clarity.



FIGURE 1 Univariate analysis of phase angle by time (days), intracellular water content (% of total water content), and weight (% of initial weight). (A,C,E) Data plots. (B,D,F) Plots of the fitted lines for individual patients. The purpose of the fitted line plots is to show a distribution of slopes and intercepts that are characteristic of the population and to suggest that a hierarchical model is appropriate.

The plot of the fitted lines shows a distribution of lines with differing slopes and intercepts that is representative of our patient group. We can describe this behaviour with a hierarchical model in which the slopes are normally distributed around a mean value μ1G, and the intercepts are normally distributed around a mean value μ0G. The mean and variance of those normal distributions are characteristic of the population. Bayesian statistical analysis is particularly useful for estimating means and variances in a hierarchical model, and we adapted code from Kruschke 201120, which we ran using the software applications RStudio (version 1.0.136: RStudio, Boston, MA, U.S.A.), R (version 3.3.3: The R Foundation, Vienna, Austria), rjags (version 4.3.0, http://mcmc-jags.sourceforge.net), and JMP (version 10.0.2: SAS Institute, Cary, NC, U.S.A.).

The outputs from the Bayesian models are posterior distributions for the believability of the mean values. However, those values are not independent and are negatively correlated, in that larger positive slopes tend to have larger negative intercepts. Care is therefore required to avoid choosing group slope and intercept values independently of one other. Table ii sets out the mean values and the 95% highest-density intervals.

TABLE II Group slope and intercept values


The slopes are of particular interest when they have a non-zero value, which implies a relationship between the Φ and the predictor. In Table ii, zero is excluded from the 95% highest-density intervals in all three relationships, and we can conclude that Φ depends significantly on t, w, and m. There is a possibility that the predictors are confounded, which could lead to misinterpretation about the relationship between Φ and a predictor. For example, if a patient loses weight over time and the Φ is very sensitive to m, then a significant relation will be seen between Φ and t in addition to Φ and m.

We formulated hierarchical models between w and t, m and t, and w and m to determine whether any significant relationships existed. We speculated that if both w and m are related to nutrition status, then they might be correlated. The results presented in Figure 2 and Table iii show that w and t, and m and t, are significantly correlated, but that w and m are not.



FIGURE 2 Univariate analysis of intracellular water content (% of total water content) by time (days), of weight (% of the initial weight) by time, and of intracellular water content by weight. (A,C,E) Data plots. (B,D,F) Plots of the fitted lines for the individual patients. The purpose of the fitted line plots is to show a distribution of slopes and intercepts that are characteristic of the population and to suggest that a hierarchical model is appropriate.

TABLE III Group slope and intercept values


When looking at the behaviour of Φ, adjustments have to be made for the interrelationships between t, w, and m. Multivariate linear models can look at the behaviour of Φ for any of those parameters while adjusting for the influence of the others.

The intercepts are not particularly meaningful at this point, except perhaps for predictive modelling or data simulation. Moreover, the intercepts are meaningless in some circumstances. For example, the intercept value when m is 0 has no physical meaning. That observation suggests the need to consider a shifted set of variables. The intercepts will become more important in future work when patients are compared with a control group.

Multivariate Analysis

The multivariate approach considers fitting Φ as a function of the other 3 parameters taken together:

In principle, a hierarchical approach could also be used, except that, in our experiment, with 3 data points per patient and 4 parameters to fit, we are over-fitted. Patient data were pooled, and combinatorial methods were used for the analysis. Table iv shows the results.

TABLE IV Distributions of the slopes and intercepts for the multilinear fit of phase angle by time, intracellular water (ICW), and weighta


The multilinear fit shows that t,w, and m are all significant variables, in that none of the slopes include 0 in their respective 95% confidence intervals. The prediction for the Φ is

The coefficient of determination is R2 = 0.698, and the model-fit p value is 0.0055.


With the goal of understanding the behaviour of Φ for a population of head-and-neck cancer patients, we used hierarchical and combinatorial linear models to study Φ as a function of w, t, and m. The resulting population parameters will be important for future work comparing this patient group with a control group. All 3 variables were statistically significant in predicting Φ in both the univariate and multivariate models. Table v summarizes the univariate and multivariate slopes. The multivariate slopes are smaller in magnitude than the univariate slopes, which is not unexpected because of correlations between t, w, and m. The w parameter shows the least relative change in slope from the univariate models to the multivariate models (Table v), reflecting the fact w has the highest correlation with Φ. Ranking the predictor variables in terms of influence on prediction of Φ, w is highest, followed by t and then by m.

TABLE V Univariate and multivariate slopes for phase versus time, intracellular water (ICW), and weight


Our study demonstrates a change in Φ for head-and-neck cancer patients undergoing a course of rt. The Φ has multivariate relations with t, w, and m such that it decreases with t, increases with w, and increases with m. As a consequence, future studies will require information about all the covariates and a minimum of 4 repeated bia measurements and weighings per patient.

Our findings are consistent with the results of other studies that have shown the prognostic potential of Φ21. Because a great body of evidence indicates that malnutrition is a predictor of shortened survival in cancer, the association between Φ and survival is not surprising. Malnutrition has a strong effect on the electrical properties of tissues and thus on the Φ. That is, reduced reactance with maintained resistance indicates comparable hydration, but loss of cell mass21.

Our study adds to the growing body of evidence about change in Φ and the potential to use the bia-derived Φ in clinical applications such as prognostic indices, beyond body-composition equations. The biologic meaning of the Φ is not well understood, but there is concrete evidence to prove its usefulness in reflecting a relation with cell-membrane function and also with the ratio between ecw and icw15. One study of Φ with respect to rt22 recommend using a Φ cut-off point of 5.2 as a criterion for identifying nutritional risk in pre-rt cancer patients. The diagnosis resulting from the 5.2 cut-off value was significantly associated with risk of death for patients in the sample group22. However, literature further evaluating that cut-off is limited. Despite the lack of standardized cut-off values in the literature, it is evident that Φ can play an important role as a marker of morbidity and mortality in a wide range of disease conditions, with a higher Φ being a general indicator of wellness23.

In our patient population, we observed a downward trend in icw content of −0.0121% daily over time, with a corresponding upward trend in ecw. Changes in hydrostatic pressure, oncotic pressure, and vascular permeability can result in abnormal fluid shifts in the body, resulting in altered icw and ecw concentrations.

Being objective, noninvasive, low in cost, and simple to obtain, bioimpedance-measured parameters such as Φ, icw, and ecw can easily be repeatedly measured during the daily clinical routine.

Study Limitations

Our study has some limitations. First, the sample size was small, because this was a feasibility study. In future, a large number of patients should be recruited for a robust statistical analysis. Second, our analysis could benefit from more data points per patient. We have seen evidence that a multivariate approach is necessary for analyzing the Φ dependencies. With 4 parameters (1 intercept and 3 slopes), at least 4 measurements per patient will be required during the course of treatment. In the present work, we did not have 4 measurements per patient, and we addressed that issue by grouping the patient data and then using combinatorial techniques to analyze group behaviour. Third, the same analysis should be conducted in a control group of healthy participants with the intent to compare the Φ between the groups. Fourth, the demographics of our patient population are narrow, although inadvertently so, with all recruited patients having been male.

Considering the importance of identifying safe ways to assess cell health and nutrition status in cancer patients undergoing treatment, parameters such as Φ, icw, ecw, and weight should be studied periodically during the course of treatment such that, when those parameters cross a designated cut-off point, a clinical intervention can be implemented in a timely manner to potentially improve outcomes of treatment.

Further study to define clinical parameters for prognostication with bia in rt patients is needed.


In 20 hnscc patients studied during their respective courses of rt (18 available for analysis), extensive univariate and multivariate analyses were used to determine changes in cell membrane integrity and cellular properties. Population-based parameters for Φ can be described in a multilinear model in which the Φ decreases with time (−0.00255 degrees daily) and increases with icw content (0.2072 degrees per percentage point) and weight (0.01698 degrees per percentage point). The multivariate relationship between Φ, t, w, and m requires the acquisition of data for all those covariates in future studies of cancer patients and control groups.


We have read and understood Current Oncology’s policy on disclosing conflicts of interest, and we declare that we have none.


*BC Cancer–Fraser Valley Centre, Surrey, BC;,
Brody School of Medicine, East Carolina University, Greenville, North Carolina, U.S.A.;,
University College Dublin, School of Medicine and Medical Science, Dublin, Ireland..


1. Langius JAE, Doornaert P, Spreeuwenberg MD, Langendijk JA, Leemans CR, van Bokhorst–de van der Schueren MA. Radiotherapy on the neck nodes predicts severe weight loss in patients with early stage laryngeal cancer. Radiother Oncol 2010;97:80–5.
cross-ref  pubmed  

2. Elting LS, Cooksley CD, Chambers MS, Garden AS. Risk, outcomes, and costs of radiation-induced oral mucositis among patients with head-and-neck malignancies. Int J Radiat Oncol Biol Phys 2007;68:1110–20.

3. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005;55:74–108.
cross-ref  pubmed  

4. Matthews TW, Lampe HB, Dragosz K. Nutritional status in head and neck cancer patients. J Otolaryngol 1995;24:87–91.

5. Unsal D, Mentes B, Akmansu M, Uner A, Oguz M, Pak Y. Evaluation of nutritional status in cancer patients receiving radiotherapy: a prospective study. Am J Clin Oncol 2006;29:183–8.

6. Chasen MR, Bhargava R. A descriptive review of the factors contributing to nutritional compromise in patients with head and neck cancer. Support Care Cancer 2009;17:1345–51.

7. Sarhill N, Mahmoud FA, Christie R, Tahir A. Assessment of nutritional status and fluid deficits in advanced cancer. Am J Hosp Palliat Care 2003;20:465–73.

8. Zarowitz BJ, Pilla AM. Bioelectrical impedance in clinical practice. DICP 1989;23:548–55.

9. Barbosa-Silva MC, Barros AJ, Wang J, Heymsfield SB, Pierson RN Jr. Bioelectrical impedance analysis: population reference values for phase angle by age and sex. Am J Clin Nutr 2005;82:49–52.

10. Gupta D, Lammersfeld CA, Vashi PG, et al. Bioelectrical impedance phase angle in clinical practice: implications for prognosis in stage iiib and iv non–small cell lung cancer. BMC Cancer 2009;9:37.

11. Diaz EO, Villar J, Immink M, Gonzales T. Bioimpedance or anthropometry? Eur J Clin Nutr 1989;43:129–37.

12. Wang L, Hui SS. Validity of four commercial bioelectrical impedance scales in measuring body fat among Chinese children and adolescents. Biomed Res Int 2015;2015:614858.

13. Gupta D, Lammersfeld CA, Burrows JL, et al. Bioelectrical impedance phase angle in clinical practice: implications for prognosis in advanced colorectal cancer. Am J Clin Nutr 2004;80:1634–8.
cross-ref  pubmed  

14. Hui D, Bansal S, Morgado M, Dev R, Chisholm G, Bruera E. Phase angle for prognostication of survival in patients with advanced cancer: preliminary findings. Cancer 2014;120:2207–14.
cross-ref  pubmed  pmc  

15. Gupta D, Lammersfeld CA, Vashi PG, et al. Bioelectrical impedance phase angle as a prognostic indicator in breast cancer. BMC Cancer 2008;8:249.
cross-ref  pmc  

16. Castanho IA, Lopes AJ, Koury JC, Tessarollo B, Silva AC, Nunes RA. Relationship between the phase angle and volume of tumours in patients with lung cancer. Ann Nutr Metab 2013;62:68–74.

17. Faisy C, Rabbat A, Kouchakji B, Laaban JP. Bioelectrical impedance analysis in estimating nutritional status and outcome of patients with chronic obstructive pulmonary disease and acute respiratory failure. Intensive Care Med 2000;26:518–25.
cross-ref  pubmed  

18. Selberg O, Selberg D. Norms and correlates of bioimpedance phase angle in healthy human subjects, hospitalized patients, and patients with liver cirrhosis. Eur J Appl Physiol 2002;86:509–16.

19. Isenring E, Bauer J, Capra S, Davies PS. Evaluation of foot-to-foot bioelectrical impedance analysis for the prediction of total body water in oncology outpatients receiving radiotherapy. Eur J Clin Nutr 2004;58:46–51.

20. Kruschke JK. Doing Bayesian Data Analysis: A Tutorial in R and BUGS. Burlington, MA: Academic Press/Elsevier; 2011.

21. Norman K, Stobäus N, Zocher D, et al. Cutoff percentiles of bioelectrical phase angle predict functionality, quality of life, and mortality in patients with cancer. Am J Clin Nutr 2010;92:612–19.

22. Motta RST, Castanho IA, Velarde LGC. Cutoff point of the phase angle in pre-radiotherapy cancer patients. Nutr Hosp 2015;32:2253–60.

23. Kyle UG, Soundar EP, Genton L, Pichard C. Can phase angle determined by bioelectrical impedance analysis assess nutritional risk? A comparison between healthy and hospitalized subjects. Clin Nutr 2012;31:875–81.
cross-ref  pubmed  

Correspondence to: Kirpal S. Kohli, Department of Medical Physics, BC Cancer–Fraser Valley Centre, 13750 96th Avenue, Surrey, British Columbia V3V 1Z2. E-mail: kkohli@bccancer.bc.ca

(Return to Top)

Current Oncology, VOLUME 25, NUMBER 3, June 2018

Copyright © 2018 Multimed Inc.
ISSN: 1198-0052 (Print) ISSN: 1718-7729 (Online)