Breast Cancer-Related Lymphedema: a Review of Risk Factors, Radiation Therapy Contribution, and Management Strategies

Comparison of Volume Measurements and Bioimpedance Spectroscopy Using A Stand-on Device for Assessment of Unilateral Breast Cancer-Related Lymphedema

Objective

Breast cancer related lymphedema (BCRL) may be assessed through objective measurement of limb swelling with common techniques including volumetric measurement using a tape measure or perometry, and measurement of extracellular water using bioimpedance spectroscopy (BIS). This study aimed to evaluate the performance of a stand-on BIS device for detection of BCRL, introduce a novel graphical method to compare volumetric and BIS methods alongside traditional specificity and sensitivity analysis, and determine and compare BIS thresholds with those published previously.

Materials and Methods

Female participants with indocyanine green lymphography confirmed unilateral arm lymphedema (n = 197) and healthy controls (n = 267) were assessed using a cross-sectional study design. BIS and volumetric measures were obtained in a single session.

Results

The BIS lymphedema index (L-Dex) method had a significantly higher sensitivity than the excess volume approach (area under the curve = 0.832 vs. 0.649, p = 0.0001). A threshold of L-Dex 6.5 had a higher true positive rate (70.6%) than L-Dex 10 (68.5%) although false positive rate increased from 0.4% to 2.6%. A threshold of 5% excess volume improved the true positive rate (68.5%) compared with 10% excess volume (49.7%) however the false positive rate increased to an unacceptable 47%. The L-Dex ranges in this study were not significantly different from previously published ranges.

Conclusion

BIS was superior for identifying BCRL compared with volume measurements, reaffirming the value of this technique. However, it is recommended that BIS be used in conjunction with comprehensive evaluation of symptoms and clinical presentation. The proposed graphical method provides a simple and easily interpretable approach to compare and define concordance between the two commonly used methods for BCRL assessment namely limb volume and BIS L-Dex indices. The existing BIS (L-Dex) thresholds for presence of BCRL were also validated.

The Role of Spirituality on Physical Activity and Sleep Among African American Long-Term Breast Cancer Survivors

BACKGROUND

African Americans with chronic conditions have reported the importance of spirituality in their lives. Aspects of spirituality have been shown to be related to physical activity (PA) and sleep, and PA and sleep affect quality of life (QOL). This study examined the association between spirituality, PA, and sleep in long-term African American breast cancer survivors.

METHODS

This cross-sectional study included 323 breast cancer survivors who previously participated in a case-only study. During 2015-2016, participants completed a questionnaire focused on survivorship that used validated measures for spirituality, PA, and sleep. Adjusted binary and multinomial logistic regression models estimated odds ratios (aORs) and 95% confidence intervals (CIs) for the associations of spirituality with total PA, meeting PA guidelines, sleep duration, and sleep medication.

RESULTS

The mean age at diagnosis was 54.8 (SD = 9.89) years. The range of spirituality scores was 7-48 (median = 44). Among participants who had a score ≥ 44, 59% had high total PA, 61% met PA guidelines, 59% had high sleep duration, and 55% did not use sleep medication. Higher spirituality score was associated with higher total PA (aOR for ≥ 681 min/week: 1.90, 95% CI: 1.03-3.50), meeting PA guidelines (aOR: 1.78, 95% CI: 1.06-2.98), sleep duration > 7 h/night (aOR: 1.72, 95% CI 1.05-2.83), and lack of sleep medication use (aOR: 0.45, 95% CI: 0.24-0.84).

CONCLUSION

In African American long-term breast cancer survivors, a higher spirituality score increased the likelihood of greater PA and high sleep duration. These results indicate that interventions surrounding spirituality may benefit the QOL of African American breast cancer survivors.

Development of a Mobile-Based Self-care Application for Patients with Breast Cancer-Related Lymphedema in Iran

BACKGROUND

Due to the chronic, progressive, and debilitating nature of breast cancer-related lymphedema (BCRL), it is necessary to observe and maintain self-care management accordingly. This study was conducted to develop a mobile application based on the Android operating system for self-care management of Iranian patients with BCRL.

METHODS

An applied developmental study was conducted in 2020. The users' information needs assessment as well as design, development, implementation, and evaluation of the mobile app for self-care management of patients with BCRL was done by searching the literature, reviewing the existing mobile applications, and surveying the users' needs. The mobile app was designed using the Android Studio environment and Java programming language in the Android operating system. The usability of the app was evaluated by 30 patients with BCRL using the questionnaire for user interface satisfaction-seventh version (QUIS 7).

RESULTS

The mobile app for BCRL included demographic information, clinical information, lifestyle and system functions (drug use, nutrition, exercise, smoking cessation, communication, and test time reminder). User usability evaluation of the app content and functions confirmed that it was appropriate and satisfactory for the self-management of women with BCRL.

CONCLUSION

The mobile app was appropriate in terms of the content, function, and quality for improving the patients' lifestyle and education and self-management of BCRL symptoms according to its usability evaluation from the end-users' (patients) perspective. It is suggested that studies should be performed to confirm the effectiveness and identify the clinical significance of the app.

Prediction of lymphedema occurrence in patients with breast cancer using the optimized combination of ensemble learning algorithm and feature selection

Background

Breast cancer-related lymphedema is one of the most important complications that adversely affect patients' quality of life. Lymphedema can be managed if its risk factors are known and can be modified. This study aimed to select an appropriate model to predict the risk of lymphedema and determine the factors affecting lymphedema.

Method

This study was conducted on data of 970 breast cancer patients with lymphedema referred to a lymphedema clinic. This study was designed in two phases: developing an appropriate model to predict the risk of lymphedema and identifying the risk factors. The first phase included data preprocessing, optimizing feature selection for each base learner by the Genetic algorithm, optimizing the combined ensemble learning method, and estimating fitness function for evaluating an appropriate model. In the second phase, the influential variables were assessed and introduced based on the average number of variables in the output of the proposed algorithm.

Result

Once the sensitivity and accuracy of the algorithms were evaluated and compared, the Support Vector Machine algorithm showed the highest sensitivity and was found to be the superior model for predicting lymphedema. Meanwhile, the combined method had an accuracy coefficient of 91%. The extracted significant features in the proposed model were the number of lymph nodes to the number of removed lymph nodes ratio (68%), feeling of heaviness (67%), limited range of motion in the affected limb (65%), the number of the removed lymph nodes ( 64%), receiving radiotherapy (63%), misalignment of the dominant and the involved limb (62%), presence of fibrotic tissue (62%), type of surgery (62%), tingling sensation (62%), the number of the involved lymph nodes (61%), body mass index (61%), the number of chemotherapy sessions (60%), age (58%), limb injury (53%), chemotherapy regimen (53%), and occupation (50%).

Conclusion

Applying a combination of ensemble learning approach with the selected classification algorithms, feature selection, and optimization by Genetic algorithm, Lymphedema can be predicted with appropriate accuracy. Developing applications by effective variables to determine the risk of lymphedema can help lymphedema clinics choose the proper preventive and therapeutic method.