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

Efficient risk-based collection of biospecimens in cohort studies: designing a prospective study of diagnostic performance for multicancer detection tests

In cohort studies, it can be infeasible to collect specimens on an entire cohort. For example, to estimate sensitivity of multiple multi-cancer detection (MCD) assays, we desire an extra 80 mL of cell-free DNA (cfDNA) blood, but this much extra blood is too expensive for us to collect on everyone. We propose a novel epidemiologic study design that efficiently oversamples those at highest baseline disease risk from whom to collect specimens, to increase the number of future cases with cfDNA blood collection. The variance reduction ratio from our risk-based subsample versus a simple random (sub)sample (SRS) depends primarily on the ratio of risk model sensitivity to the fraction of the cohort selected for specimen collection subject to constraining the risk model specificity. In a simulation where we chose 34% of the Prostate, Lung, Colorectal, and Ovarian Screening Trial cohort at highest risk of lung cancer for cfDNA blood collection, we could enrich the number of lung cancers 2.42-fold. The standard deviation of lung-cancer MCD sensitivity was 31%-33% reduced versus SRS. Risk-based collection of specimens on a subsample of the cohort could be a feasible and efficient approach to collecting extra specimens for molecular epidemiology.

Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review

Rationale & Objective

Accurate estimation of in-hospital outcomes for patients with acute kidney injury (AKI) is crucial for aiding physicians in making optimal clinical decisions. We aimed to review prediction models constructed by machine learning methods for predicting AKI prognosis using administrative databases.

Study Design

A systematic review following PRISMA guidelines.

Setting & Study Populations

Adult patients diagnosed with AKI who are admitted to either hospitals or intensive care units.

Search Strategy & Sources

We searched PubMed, Embase, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health for studies published between January 1, 2014 and February 29, 2024. Eligible studies employed machine learning models to predict in-hospital outcomes of AKI based on administrative databases.

Data Extraction

Extracted data included prediction outcomes and population, prediction models with performance, feature selection methods, and predictive features.

Analytical Approach

The included studies were qualitatively synthesized with assessments of quality and bias. We calculated the pooled model discrimination of different AKI prognoses using random-effects models.

Results

Of 3,029 studies, 27 studies were eligible for qualitative review. In-hospital outcomes for patients with AKI included acute kidney disease, chronic kidney disease, renal function recovery or kidney failure, and mortality. Compared with models predicting the mortality of patients with AKI during hospitalization, the prediction performance of models on kidney function recovery was less accurate. Meta-analysis showed that machine learning methods outperformed traditional approaches in mortality prediction (area under the receiver operating characteristic curve, 0.831; 95% CI, 0.799-0.859 vs 0.772; 95% CI, 0.744-0.797). The overlapping predictive features for in-hospital mortality identified from ≥6 studies were age, serum creatinine level, serum urea nitrogen level, anion gap, and white blood cell count. Similarly, age, serum creatinine level, AKI stage, estimated glomerular filtration rate, and comorbid conditions were the common predictive features for kidney function recovery.

Limitations

Many studies developed prediction models within specific hospital settings without broad validation, restricting their generalizability and clinical application.

Conclusions

Machine learning models outperformed traditional approaches in predicting mortality for patients with AKI, although they are less accurate in predicting kidney function recovery. Overall, these models demonstrate significant potential to help physicians improve clinical decision making and patient outcomes.

Registration

CRD42024535965.

Risk factors and prediction model of breast cancer-related lymphoedema in a Chinese cancer centre: a prospective cohort study protocol

INTRODUCTION

Lymphoedema is a distressing and long-term complication for breast cancer survivors. However, the reported incidence of lymphoedema varies, and its risk factors remain underexplored. Currently, a well-established risk prediction model is still lacking. This study aims to describe the rationale, objectives, protocol and baseline characteristics of a prospective cohort study focused on examining the incidence and risk factors of breast cancer-related lymphoedema (BCRL), as well as developing a risk prediction model.

METHODS AND ANALYSIS

This study is an ongoing single-centre prospective observational cohort study recruiting 1967 patients with breast cancer scheduled for surgery treatment in northern China between 15 February 2022 and 21 June 2023. Assessments will be conducted presurgery and at 1, 3, 6, 12, 18, 24, 30 and 36 months postsurgery. Bilateral limb circumferences will be measured by patients at home or by researchers at the outpatient clinics during follow-up visits. The diagnosis of lymphoedema is based on a relative limb volume increase of ≥10% from the preoperative assessment. Self-reported symptoms will be assessed to assist in diagnosis. Potential risk factors are classified into innate personal traits, behavioural lifestyle, interpersonal networks, socioeconomic status and macroenvironmental factors, based on health ecology model. Data collection, storage and management were conducted using the online 'H6WORLD' data management platform. Survival analysis using the Kaplan-Meier estimate will determine the incidence of BCRL. Risk factors of BCRL will be analysed using log-rank test and COX-LASSO regression. Traditional COX regression analysis and seven common survival analysis machine learning algorithms (COX, CARST, RSF, GBSM, XGBS, SSVM and SANN) will be employed for model construction and validation.

ETHICS AND DISSEMINATION

The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-21124) and the Research Ethics Committee of Tianjin Medical University Cancer Institute and Hospital (bc2023013). The results of this study will be published in peer-reviewed journals and will be presented at several research conferences.

TRIAL REGISTRATION NUMBER

ChiCTR2200057083.

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.

Cohort profile: Guangzhou breast cancer study (GBCS)

The Guangzhou Breast Cancer Study (GBCS) is a patient-based prospective cohort study designed to identify risk factors and underlying mechanisms for breast cancer (BC) incidence and prognosis, specifically addressing the need for individualized prevention in South China, where BC incidence is notably high. Based in Guangzhou, China, the GBCS began recruitment in 2008, comprises three complementary studies: the Guangzhou breast cancer cohort with 5471 breast cancer patients, a case-control study with 1551 cases and 1605 controls, and an immunohistochemistry (IHC) cohort with 1063 breast cancer patients. Participants are primarily aged 41-60 years. Cohort follow-up is conducted every three months in the first year, every six months in the second and third years, and annually thereafter. High follow-up rates have been achieved until 2023, with 73.5% for the Guangzhou breast cancer cohort and 98.6% for the IHC cohort still active. Baseline data collection included demographic characteristics and breast cancer risk factors, while follow-up data included survival, treatment details, disease history, occupational history, post-diagnostic lifestyle, and laboratory measures, including genetic markers, proteins, and environmental exposures. The study encourages global collaborations and invites interested researchers to contact the corresponding author at xulin27@ mail.sysu.edu.cn with specific research ideas or proposals.

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.