Risk prediction models for breast cancer-related lymphedema: A systematic review and meta-analysis

Arm symptom pattern among breast cancer survivors with and without lymphedema: a contemporaneous network analysis

BACKGROUND

Arm symptoms commonly endure in post-breast cancer period and persist into long-term survivorship. However, a knowledge gap existed regarding the interactions among these symptoms. This study aimed to construct symptom networks and visualize the interrelationships among arm symptoms in breast cancer survivors (BCS) both with and without lymphedema (LE).

PATIENTS AND METHODS

We conducted a secondary analysis of 3 cross-sectional studies. All participants underwent arm circumference measurements and symptom assessment. We analyzed 17 symptoms with a prevalence >15%, identifying clusters and covariates through exploratory factor and linear regression analysis. Contemporaneous networks were constructed with centrality indices calculated. Network comparison tests were performed.

RESULTS

1116 cases without missing data were analyzed, revealing a 29.84% prevalence of LE. Axillary lymph node dissection [ALND] (vs sentinel lymph node biopsy [SLNB]), longer post-surgery duration, and radiotherapy significantly impacted overall symptom severity (P < .001). "Lymphatic Stasis," "Nerve Injury," and "Movement Limitation" symptom clusters were identified. Core symptoms varied: tightness for total sample network, firmness for non-LE network, and tightness for LE network. LE survivors reported more prevalent and severe arm symptoms with stronger network connections than non-LE group (P = .010). No significant differences were observed among different subgroups of covariates (P > .05). Network structures were significantly different between ALND and SLNB groups.

CONCLUSION

Our study revealed arm symptoms pattern and interrelationships in BCS. Targeting core symptoms in assessment and intervention might be efficient for arm symptoms management. Future research is warranted to construct dynamic symptom networks in longitudinal data and investigate causal relationships among symptoms.

Development and validation of a nomogram for breast cancer-related lymphedema

To establish and validate a predictive model for breast cancer-related lymphedema (BCRL) among Chinese patients to facilitate individualized risk assessment. We retrospectively analyzed data from breast cancer patients treated at a major single-center breast hospital in China. From 2020 to 2022, we identified risk factors for BCRL through logistic regression and developed and validated a nomogram using R software (version 4.1.2). Model validation was achieved through the application of receiver operating characteristic curve (ROC), a calibration plot, and decision curve analysis (DCA), with further evaluated by internal validation. Among 1485 patients analyzed, 360 developed lymphedema (24.2%). The nomogram incorporated body mass index, operative time, lymph node count, axillary dissection level, surgical site infection, and radiotherapy as predictors. The AUCs for training (N = 1038) and validation (N = 447) cohorts were 0.779 and 0.724, respectively, indicating good discriminative ability. Calibration and decision curve analysis confirmed the model's clinical utility. Our nomogram provides an accurate tool for predicting BCRL risk, with potential to enhance personalized management in breast cancer survivors. Further prospective validation across multiple centers is warranted.

Characterization of Immune Cell Infiltration and Collagen Type III Disorganization in Human Secondary Lymphedema: A Case-control Study

Background

Secondary lymphedema (SL) affects 120 million people globally, posing a lifelong burden for up to 37% of cancer survivors. Chronic inflammation and progressive fibrosis are key drivers of SL, yet detailed characterization of immune cell subpopulations across lymphedema stages is lacking. This study aimed to investigate the immunologic profile of lymphedematous skin and its association with extracellular matrix changes, which could serve as clinical biomarkers or therapeutic targets.

Methods

This case-control study analyzed the skin from 36 patients with and without SL, using immunofluorescence to quantify T cells, B cells, macrophages, and their subpopulations. Collagen quantity and composition were examined using picrosirius red staining, and mast cell infiltration was assessed with toluidine blue staining. Early and late SL stages were compared to identify histomorphological and immunologic correlates of stage progression.

Results

We found a predominance of CD4+ T cells and mast cells in SL skin (1.4/mm² versus 1.0/mm², P < 0.01; 1.2/mm² versus 0.2/mm², P < 0.0001) and a higher ratio of collagen III to collagen I fibers (51.6% versus 75.0%, P < 0.001). M2 macrophages were more abundant in late-stage than in early-stage lymphedema (1.7/mm² versus 1.0/mm², P = 0.02).

Conclusions

This study demonstrated a shift toward CD4+ T cell and mast cell infiltration in SL skin, correlating with extracellular matrix disorganization and an altered collagen III/I ratio. These findings enhance our understanding of the cellular and morphological changes in SL, potentially guiding future diagnostic and therapeutic strategies.

Primary surgical prevention of lymphedema

Lymphedema in the upper and lower extremities can lead to significant morbidity in patients, resulting in restricted joint movements, pain, discomfort, and reduced quality of life. While physiological lymphatic reconstructions such as lymphovenous anastomosis (LVA), lymphovenous implantation (LVI), and vascularized lymph node transfer (VLNT) have shown promise in improving patients' conditions, they only provide limited disease progression control or modest reversal. As lymphedema remains an incurable condition, the focus has shifted toward preventive measures in developed countries where most cases are iatrogenic due to cancer treatments. Breast cancer-related lymphedema (BCRL) has been a particular concern, prompting the implementation of preventive measures like axillary reverse mapping. Similarly, techniques with lymph node-preserving concepts have been used to treat lower extremity lymphedema caused by gynecological cancers. Preventive lymphedema measures can be classified into primary, secondary, and tertiary prevention. In this comprehensive review, we will explore the principles and methodologies encompassing lymphatic microsurgical preventive healing approach (LYMPHA), LVA, lymphaticolymphatic anastomosis (LLA), VLNT, and lymph-interpositional-flap transfer (LIFT). By evaluating the advantages and limitations of these techniques, we aim to equip surgeons with the necessary knowledge to effectively address patients at high risk of developing lymphedema.

Identification Of A Higher Risk Lymphedema Phenotype And Associations With Cytokine Gene Polymorphisms

CONTEXT

Breast cancer-related lymphedema (BCRL) is chronic condition that occurs in 5% to 75% of women following treatment for breast cancer. However, little is known about the risk factors and mechanisms associated with a worse BCRL profile.

OBJECTIVES

Identify distinct BCRL profiles in women with the condition (i.e., lower vs. higher risk phenotype) and evaluate for associations with pro- and anti-inflammatory genes.

METHODS

Latent class profile analysis (LCPA) was used to identify the BCRL profiles using phenotypic characteristics evaluated prior to surgery. Candidate gene analyses were done to identify cytokine genes associated with the two BCRL profiles.

RESULTS

Of the 155 patients evaluated, 35.5% (n = 55) were in the Lower and 64.5% (n = 100) were in the Higher Risk classes. Risk factors for membership in the Higher class included: lower functional status, having sentinel lymph node biopsy, axillary lymph node dissection, mastectomy, higher number of positive lymph nodes, and receipt of chemotherapy. Polymorphisms for interleukin (IL)1-beta and IL6 were associated with membership in the Higher Risk class.

CONCLUSION

The readily available and clinically relevant phenotypic characteristics associated with a worse BCRL profile can be used by clinicians to identify higher risk patients. If confirmed, these characteristics can be tested in predictive risk models. In addition, the candidate gene findings may guide the development of mechanistically-based interventions to decrease the risk of BCRL.