The aim of precision medicine, also known as precision health, is to discover the right treatment for the right patient at the right time, and to determine factors contributing to or protecting from common and complex diseases.1 Breast cancer-related lymphedema (hereafter, lymphedema) is a progressive, complex and chronic syndrome of abnormal swelling and multiple symptoms, resulting from abnormal accumulation of protein-rich lymph fluid in the interstitial tissue spaces after cancer treatment.2-4 Even with advancements in cancer treatment, lymphedema remains an ongoing major complex health problem affecting more than 40% of the 3.1 million breast cancer survivors in the United States.2-5 Cancer treatment (e.g., surgery, lymph node procedure, radiation) can create obstruction or disruption in the lymphatic system, leading to the accumulation of lymph fluid or lymphedema.6-8 Inflammation or infection and higher body mass index (BMI > 30) can burden the lymphatic system by creating a disproportion in lymph transport and capacity due to excess extracellular fluid, leading to lymph fluid accumulation.6-8
No cure for lymphedema has been identified.9 Genomic discoveries that have mapped out the human genes (genome) offer the potential for a cure or precision treatment through the identification and implementation of genomic-based risk assessment, treatment and management.10 The genomic discovery of a cure or precision treatment for lymphedema necessitates a priori precision assessment of phenotype (i.e., a patient’s clinical characteristics related to a given disease).
Inconsistency in the criteria defining lymphedema and the use of different measures in research has presented tremendous challenges in the assessment of lymphedema phenotype.11 Often, lymphedema is diagnosed via provider observation of swelling in clinical practice. Lymphedema following breast cancer treatment can occur in the shoulder, breast and thoracic regions. No epidemiological studies have explored the incidence of lymphedema in these difficult-to-measure body areas, mainly due to lack of instruments to quantify swelling. As a result, precision assessment of lymphedema phenotype remains an ongoing challenge in research and clinical practice.
A critical need exists to understand heterogeneity (i.e., different types) of lymphedema phenotype to advance precision phenotype assessment of lymphedema and provide insights to biological mechanisms. This article describes methods to optimize the precision assessment of heterogeneity of lymphedema phenotypes among breast cancer survivors.
Arm Lymphedema Phenotype
Arm lymphedema has been the focus of lymphedema phenotype assessment due to its visible and quantifiable nature. Sequential circumference limb measurement, water displacement and infrared perometry are widely used methods to quantify arm lymphedema in terms of limb size (limb girth) or limb volume.11,12 Bioelectrical impedance measurement has emerged as an alternative method for assessing arm lymphedema.13,14
Arm lymphedema is often arbitrarily defined as > 2 centimeters in limb girth or > 200 mL difference in limb volume as compared to the unaffected limb or 10% volume increase in the affected limb from presurgery baseline limb volume.11-14 Recent research has shown that among breast cancer survivors, > 5% limb volume increase from presurgical baseline enables detectable differences in quality of life and better clinical outcomes with interventions to promote lymph flow.12,15
A flexible nonstretch tape measure should be used for sequential circumferential arm measurements to assure consistent tension over soft tissue, muscle and bony prominences.11 Sequential circumferential arm measurements should be applied to both affected and unaffected limbs at the hand proximal to the metacarpals, wrist and then every 4 centimeters from the wrist to axilla. Limb size and limb volume are calculated based on sequential circumferential arm measurements. The formula often used for calculating limb volume with a presurgery baseline limb volume is: Limb Volume Increase = (Affected Frustum Limb VolumeFollow-up /Affected Frustum Limb Volumebaseline)/(Contralateral Frustum Limb VolumeFollow-up /Contralateral Frustum Limb Volumebaseline).11,12,15 If presurgical baseline limb volume is not available, the following formula can be used: Limb Volume Ratio = Affected Frustum Limb Volume/Contralateral Frustum Limb Volume; Limb Volume Difference = Affected Frustum Limb Volume – Contralateral Frustum Limb Volume.11,13
Infrared perometry applies an optoelectronic device that works similarly to computer-assisted tomography, but it makes use of light instead of x-rays.11-12 The perometer maps a three-dimensional graph of the affected and unaffected extremities using numerous rectilinear light beams, and interfaces with a computer for data analysis and storage. A three-dimensional limb image is generated, and limb volume is calculated automatically. This optoelectronic method has a standard deviation of 8.9 mL (arm), less than 0.5% of limb volume with repeated measuring.11,12
Spillover and hygienic concerns present challenges in using water displacement as a measurement in busy clinical settings. Patients submerge limbs in a water-filled container; the overflowing water is caught in another container and weighed or measured. This method does not provide data about localization of swelling or arm shape.11 Water displacement is contraindicated in patients with wounds. Patients may find it difficult to hold the position for the necessary time for the overflow to stop.11
Bioelectrical impedance analysis (BIA) measures impedance or resistance of the extracellular fluid using low frequency electrical current.13,14 BIA provides an impedance ratio to calculate a Lymphedema Index (L-Dex ratio). With the development of lymphedema, the limb impedance decreases and the L-Dex ratio therefore increases. The L-Dex ratio ranges from -10 to +10. Using an arbitrary scale relevant to normative standards, a patient has arm lymphedema if the L-Dex ratio exceeds the normal value of +10. The diagnostic cutoff point of >+10 can only identify true cases of lymphedema among breast cancer survivors with 66% accuracy, missing 34% of true arm lymphedema cases (AUC = 0.81 [95% CI: 0.74-0.88]; sensitivity=0.66 [95% CI: 0.51-0.79]; specificity=0.95 [95% CI: 0.90-0.98]).
A recent study demonstrated that the L-Dex ratio with a cutoff point of > +7.1 can identify true cases of arm lymphedema among breast cancer survivors with 80% sensitivity and 90% specificity (AUC = 0.86).13 Since approximately 20% of true arm lymphedema cases are still missed by BIA with a cutoff point of >+7.1, it is critical for healthcare professionals to incorporate other assessment methods, including self-report, clinical observation or perometry to ensure precision assessment of arm lymphedema.13
Lymphedema Symptom Phenotype
Lymphedema is a sign of an impaired lymphatic system.3,6-8 Impairment in the lymphatic system leads to a chronic disease state with multiple associated symptoms that require ongoing symptom management.9,10 Literature supports that breast cancer survivors with a lymphedema diagnosis experience multiple symptoms related to fluid accumulation (hereafter, lymphedema symptoms): pain (45.2%), tenderness (52.4%), aching (61.9%), soreness (31%), tightness (71.4%), limited shoulder movement (52.4%), arm firmness (69%), arm swelling (100%), or arm heaviness (71.4%) in the affected upper limb or body.10,16 The experience of lymphedema symptoms is an ongoing debilitating complication that impacts quality of life.12,16,17 As a result, lymphedema phenotypes should include lymphedema symptoms. Importantly, lymphedema symptom phenotype may indicate an early stage of lymphedema in which only minimal changes cannot be detected by current objective measures of limb volume.16,18
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A recent study examined the validity, sensitivity and specificity of symptoms for assessing lymphedema symptom phenotype.10 The study used Youden’s method, a statistical test that helps determine the optimal cutoff point for a diagnostic test with numerical results (such as the number of symptoms), using a receiver-operating characteristic curve. According to this method, three symptoms is the best clinical cutoff point to differentiate healthy participants from breast cancer survivors with lymphedema, and nine symptoms is the best cutoff point to differentiate breast cancer survivors at risk for lymphedema from patients with lymphedema. A diagnostic cutoff of three symptoms discriminated breast cancer survivors with lymphedema from healthy women with a sensitivity of 94% and a specificity of 97%, while a diagnostic cutoff of nine symptoms identified survivors with lymphedema with a sensitivity of 64% and a specificity of 80%.
As early intervention leads to better clinical outcomes, patients with > 9 symptoms or any of the symptoms with more than two times the odds ratio for lymphedema should be evaluated immediately and treated in a timely manner (see table). This study provided evidence that self-reported symptoms actually have the capability to identify true cases of lymphedema.10
Another recent study used exploratory factor analysis of principal component analysis with a varimax rotation to examine if symptoms denoted clusters as groups of more than two symptoms to indicate different biological mechanism.18 Three major symptom cluster phenotypes were identified: fluid accumulation, impaired limb mobility and discomfort. The identification of different symptom cluster phenotypes provides a foundation for future research to explore the biological mechanism, so that targeted precision intervention for each symptom cluster phenotype can be developed to achieve optimal symptom management.
Meeting the Challenge
The advancement of precision health for lymphedema can only be possible when precision assessment of phenotype permits researchers to investigate not only specific genes but also minor changes in a gene (i.e. genetic variation or single nucleic polymorphisms [SNPs]) to make a definitive determination of genetic connection to heterogeneity of lymphedema phenotype. Currently, treatment of lymphedema continues to be a major health care challenge. Precision health allows further discovery of the effect of genetic variations on heterogeneity of lymphedema phenotype among breast cancer survivors. This may bring hope to millions of breast cancer survivors and those who may be diagnosed with and treated for breast cancer in the future. If specific genetic variations are found to be associated with a given or different lymphedema phenotype, a genetic test could identify breast cancer survivors with potentially higher risk of different phenotypes of lymphedema for whom precision interventions could be prescribed.
1. The White House. Fact Sheet: President Obama’s Precision Medicine Initiative. www.whitehouse.gov/precision-medicine
, et al. The epidemiology of arm and hand swelling in premenopausal breast cancer survivors. Cancer Epidemiol Biomarkers Prev. 2007;16(4):775-782.
3. Stanton AW, et al. Recent advances in breast cancer-related lymphedema of the arm: lymphatic pump failure and predisposing factors. Lymphat Res Biol. 2009:7(1):29-45.
4. Armer J, Stewart B. Post-breast cancer lymphedema: Incidence increases from 12 to 30 to 60 months. Lymphology. 2010;43(3):118-122.
5. American Cancer Society. Breast Cancer Facts & Figures 2015-2016. http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-046381.pdf
6. Johansson K, et al. Factors associated with the development of arm lymphedema following breast cancer treatment: a match pair case-control study. Lymphology. 2002;35(2):59-71.
7. Tsai RJ, et al. The risk of developing arm lymphedema among breast cancer survivors: a meta-analysis of treatment factors. Ann Surg Oncol. 2009;16(7):1959-1972.
8. Mak SS, et al. Predictors of lymphedema in patients with breast cancer undergoing axillary lymph node dissection in Hong Kong. Nurs Res. 2008;57(6):416-425.
9. Fu MR, et al. Putting evidence into practice: Cancer-related lymphedema. Clin J Oncol Nurs. 2014;18(s6):68-79.
10. Fu MR, et al. Symptom reporting in detecting breast cancer-related lymphedema. Breast Cancer. 2015;7:345-352.
11. Armer JM, Stewart BR. A comparison of four diagnostic criteria for lymphedema in a post-breast cancer population. Lymphatic Res Bio. 2005;3(4):208-217.
12. Cormier JN, et al. Minimal limb volume change has a significant impact on breast cancer survivors. Lymphology. 2009;42(4):161-175.
13. Fu MR, et al. L-Dex Ratio in Detecting Breast Cancer-Related Lymphedema: Reliability, Sensitivity, and Specificity. Lymphology. 2013:46(2)85-96.
14. Fu MR, et al. Patterns of obesity and lymph fluid level during the first year of breast cancer treatment: A prospective study. J Pers Med. 2015;5(3):326-340.
15. Fu MR, et al. Proactive approach to lymphedema risk reduction: a prospective study. Ann Surg Oncol. 2014;21(11):3481-3498.
16. Fu MR, Rosedale M. Breast cancer survivors’ experiences of lymphedema-related symptoms. J Pain Symp Manage. 2009;38(6):849-859.
17. Armer JM, et al. Predicting breast cancer-related lymphedema using self-reported symptoms. Nurs Res. 2003;52(6):370-379.
18. Hi S, et al. Psychometric properties of the breast cancer and lymphedema symptom experience index: the Chinese version. Euro J Oncol Nurs. 2015. doi: 10.1016/j.ejon.2015.05.002. [Epub ahead of print]
Mei R. Fu is a tenured associate professor at New York University College of Nursing in New York City. Jie Deng is an assistant professor at Vanderbilt University School of Nursing in Nashville. Jane M. Armer is a professor at University of Missouri Sinclair School of Nursing in Columbia, Mo. Suzy Lockwood is a professor at Texas Christian University Harris College of Nursing & Health Sciences in Fort Worth, Texas. Marcia Beck is the breast center coordinator at University Health-Truman Medical Centers in Kansas City, Mo. Pamela Ostby is a doctoral nursing student at the University of Missouri Sinclair School of Nursing. Brenda S. Burns is a radiation oncology nurse at TriHealth Cancer Institute in Cincinnati. Ellen Poage is a nurse practitioner and lymphedema therapist at 21st Century Oncology in Fort Myers, Fla.