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

Shen, Aomei; Wei, Xiaoxia; Zhu, Fei; Sun, Ming; Ke, Sangsang; Qiang, Wanmin; Lü, Qian

doi:10.1016/j.ejon.2023.102326

Cited by 9 publications

(5 citation statements)

References 45 publications

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“…Decision tree models have been employed to disentangle the intricate relationships between lifestyle choices, such as dietary habits and physical activity, and breast cancer susceptibility. The work of [12][13][14][15][16][17][18][19] stands out in this regard, demonstrating how decision tree modeling can unveil non-linear associations that might be overlooked by traditional analytical methods. However, our study seeks to not only predict risk but also focus on understanding the hierarchy of risk factor importance through in-depth analysis of decision tree structures.…”

Section: Literature Surveymentioning

confidence: 99%

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Analyzing the Impact of Breast Cancer Risk Factors Using Decision Tree Modeling

2023

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Breast cancer remains a significant global health concern, emphasizing the need for comprehensive investigations into its risk factors. This study employs decision tree modeling to analyze the impact of various factors on breast cancer incidence, aiming to contribute valuable insights for prevention and early intervention. Our findings reveal compelling patterns in breast cancer risk factors, shedding light on key variables that significantly influence susceptibility. Through rigorous decision tree modeling, we identify high-risk groups and highlight novel associations that warrant attention. The implications of these findings extend to both clinical practice and public health initiatives, providing a foundation for targeted prevention strategies and personalized healthcare approaches. This study not only enhances our understanding of breast cancer etiology but also underscores the utility of decision tree modeling in unraveling complex relationships within large datasets. As the field of breast cancer research continues to evolve, the insights presented here pave the way for future investigations, emphasizing the importance of tailored risk assessment and intervention strategies.

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Section: Literature Surveymentioning

confidence: 99%

“…The choice of algorithms, such as CART, C4.5, or random forests, introduces variability in model performance and interpretability. Comparative analyses, like that conducted by [17][18][19][20], have shed light on the strengths and limitations of different decision tree algorithms in the context of breast cancer risk analysis.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing the Impact of Breast Cancer Risk Factors Using Decision Tree Modeling

2023

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“…9 Although retrospective case-control analyses have identified several risk factors, including lymph node resection, radio- and chemotherapy, obesity, and infection, 4 no definitive correlation has been proven between these risk factors and SL clinical progression, according to a recent meta-analysis. 10 …”

Section: Introductionmentioning

confidence: 99%

“…9 Although retrospective casecontrol analyses have identified several risk factors, including lymph node resection, radio-and chemotherapy, obesity, and infection, 4 no definitive correlation has been proven between these risk factors and SL clinical progression, according to a recent meta-analysis. 10 It has been suggested that protein accumulation and lymphatic stasis are triggers for inflammation. 11,12 The role of T cells and the Th2 response in the development of lymphedema have been described in both basic research and clinical studies.…”

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confidence: 99%

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

Spörlein,

Hirche,

Berner

et al. 2024

Plastic and Reconstructive Surgery - Global Open

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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.

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“…Previous research efforts have focused on pinpointing various risk factors associated with BCRL, including surgical procedures with an emphasis on axillary lymph node dissection (ALND), the application of radiotherapy and chemotherapy, along with patient-specific factors like tumor dimensions, age, and body mass index (BMI) 10 , 11 . While these studies have provided crucial insights into understanding lymphedema, there remains an absence of comprehensive assessment tools that integrate multiple factors to predict an individual patient’s risk of lymphedema.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang,

Hu,

Liao

et al. 2024

Sci Rep

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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.

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

Cited by 9 publications

References 45 publications

Analyzing the Impact of Breast Cancer Risk Factors Using Decision Tree Modeling

Analyzing the Impact of Breast Cancer Risk Factors Using Decision Tree Modeling

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

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

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