Development and Validation of an Intraoperative Nomogram to Predict Breast Cancer-Related Lymphedema Based on the Arm Lymphatics Distribution

Feasibility and Clinical Utility of Prediction Models for Breast Cancer-Related Lymphedema Incorporating Racial Differences in Disease Incidence

Importance

Breast cancer-related lymphedema (BCRL) is a common complication of axillary lymph node dissection (ALND) but can also develop after sentinel lymph node biopsy (SLNB). Several models have been developed to predict the risk of disease development before and after surgery; however, these models have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, low sensitivity or specificity, and lack of risk assessment for patients treated with SLNB.

Objective

To create simple and accurate prediction models for BCRL that can be used to estimate preoperative or postoperative risk.

Design, Setting, and Participants

In this prognostic study, women with breast cancer who underwent ALND or SLNB from 1999 to 2020 at Memorial Sloan Kettering Cancer Center and the Mayo Clinic were included. Data were analyzed from September to December 2022.

Main Outcomes and Measures

Diagnosis of lymphedema based on measurements. Two predictive models were formulated via logistic regression: a preoperative model (model 1) and a postoperative model (model 2). Model 1 was externally validated using a cohort of 34 438 patients with an International Classification of Diseases diagnosis of breast cancer.

Results

Of 1882 included patients, all were female, and the mean (SD) age was 55.6 (12.2) years; 80 patients (4.3%) were Asian, 190 (10.1%) were Black, 1558 (82.8%) were White, and 54 (2.9%) were another race (including American Indian and Alaska Native, other race, patient refused to disclose, or unknown). A total of 218 patients (11.6%) were diagnosed with BCRL at a mean (SD) follow-up of 3.9 (1.8) years. The BCRL rate was significantly higher among Black women (42 of 190 [22.1%]) compared with all other races (Asian, 10 of 80 [12.5%]; White, 158 of 1558 [10.1%]; other race, 8 of 54 [14.8%]; P < .001). Model 1 included age, weight, height, race, ALND/SLNB status, any radiation therapy, and any chemotherapy. Model 2 included age, weight, race, ALND/SLNB status, any chemotherapy, and patient-reported arm swelling. Accuracy was 73.0% for model 1 (sensitivity, 76.6%; specificity, 72.5%; area under the receiver operating characteristic curve [AUC], 0.78; 95% CI, 0.75-0.81) at a cutoff of 0.18, and accuracy was 81.1% for model 2 (sensitivity, 78.0%; specificity, 81.5%; AUC, 0.86; 95% CI, 0.83-0.88) at a cutoff of 0.10. Both models demonstrated high AUCs on external (model 1: 0.75; 95% CI, 0.74-0.76) or internal (model 2: 0.82; 95% CI, 0.79-0.85) validation.

Conclusions and Relevance

In this study, preoperative and postoperative prediction models for BCRL were highly accurate and clinically relevant tools comprised of accessible inputs and underscored the effects of racial differences on BCRL risk. The preoperative model identified high-risk patients who require close monitoring or preventative measures. The postoperative model can be used for screening of high-risk patients, thus decreasing the need for frequent clinic visits and arm volume measurements.

Accessory Lymphatic Drainage Pathways on Indocyanine Green Lymphography in Patients with Breast Cancer-Related Lymphedema

BACKGROUND

Current predictive models of lymphedema risk cannot predict with 100% certainty which patients will go on to develop lymphedema and which will not. Patient-specific anatomic and physiologic differences may be the missing factor. The authors hypothesize that patients with accessory lymphatic pathways may have improved lymphatic drainage, resulting in smaller limb volumes.

METHODS

The authors reviewed indocyanine green (ICG) lymphography images of all patients who presented to their institution for evaluation of breast cancer-related lymphedema. Patients with unilateral upper extremity lymphedema, a full set of bilateral limb measurements, and ICG images of both limbs were included. Other variables of interest included patient demographics and length of follow-up. Patients with accessory pathways were determined independently, and conflicts were resolved with discussion. Abnormal images were also evaluated for common drainage pathways.

RESULTS

Thirty patients were identified as having accessory lymphatic drainage pathways. These patients had significantly smaller limb volume differences [8.19% (SD, 11.22)] compared with patients who did not exhibit these pathways [20.74% (SD, 19.76); P < 0.001]. The most common pathway was absence or rerouting of the radial bundle to the ulnar or volar bundles ( n = 16).

CONCLUSIONS

The ability to create accessory lymphatic drainage pathways may be associated with improved lymphatic drainage, resulting in smaller limb volumes. Furthermore, certain drainage pathways appear to be more common than others. Description of these pathways should be considered for inclusion in ICG lymphography image grading criteria. Further study is needed to clarify the nature of these pathways and whether these pathways affect subjective symptoms and quality of life.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Risk, II.

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

PURPOSE

To review and critically evaluate currently available risk prediction models for breast cancer-related lymphedema (BCRL).

METHODS

PubMed, Embase, CINAHL, Scopus, Web of Science, the Cochrane Library, CNKI, SinoMed, WangFang Data, VIP Database were searched from inception to April 1, 2022, and updated on November 8, 2022. Study selection, data extraction and quality assessment were conducted by two independent reviewers. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability. Meta-analysis of AUC values of model external validations was performed using Stata 17.0.

RESULTS

Twenty-one studies were included, reporting twenty-two prediction models, with the AUC or C-index ranging from 0.601 to 0.965. Only two models were externally validated, with the pooled AUC of 0.70 (n = 3, 95%CI: 0.67 to 0.74), and 0.80 (n = 3, 95%CI: 0.75 to 0.86), respectively. Most models were developed using classical regression methods, with two studies using machine learning. Predictors most frequently used in included models were radiotherapy, body mass index before surgery, number of lymph nodes dissected, and chemotherapy. All studies were judged as high overall risk of bias and poorly reported.

CONCLUSIONS

Current models for predicting BCRL showed moderate to good predictive performance. However, all models were at high risk of bias and poorly reported, and their performance is probably optimistic. None of these models is suitable for recommendation in clinical practice. Future research should focus on validating, optimizing, or developing new models in well-designed and reported studies, following the methodology guidance and reporting guidelines.

Prediction models for breast cancer-related lymphedema: a systematic review and critical appraisal

PURPOSE

The development of risk prediction models for breast cancer lymphedema is increasing, but few studies focus on the quality of the model and its application. Therefore, this study aimed to systematically review and critically evaluate prediction models developed to predict breast cancer-related lymphedema.

METHODS

PubMed, Web of Science, Embase, MEDLINE, CNKI, Wang Fang DATA, Vip Database, and SinoMed were searched for studies published from 1 January 2000 to 1 June 2021. And it will be re-run before the final analysis. Two independent investigators will undertake the literature search and screening, and discrepancies will be resolved by another investigator. The Prediction model Risk Of Bias Assessment Tool will be used to assess the prediction models' risk of bias and applicability.

RESULTS

Seventeen studies were included in the systematic review, including 7 counties, of which 6 were prospective studies, only 7 models were validation studies, and 4 models were externally validated. The area under the curve of 17 models was 0.680~0.908. All studies had a high risk of bias, primarily due to the participants, outcome, and analysis. The most common predictors included body mass index, radiotherapy, chemotherapy, and axillary lymph node dissection.

CONCLUSIONS

The predictive factors' strength, external validation, and clinical application of the breast cancer lymphedema risk prediction model still need further research. Healthcare workers should choose prediction models in clinical practice judiciously.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021258832.