Validation of a breast cancer nomogram to predict lymphedema in a Chinese population

Li, Xiaoping; Huang, Hui; Lin, Qimou; Yu, Qihe; Zhou, Yi; Long, Wansheng; Wang, Ningxia

doi:10.1016/j.jss.2016.11.009

Cited by 15 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A prospective cohort study of 276 patients found that compared to preoperative surgery, patients undergoing ALND with NAC treatment had a twofold increased risk of BCRL development ( 27 ). Another study involving 409 ALND patients also discovered that NAC increased the risk of BCRL by 3.76 times and was independently associated with BCRL ( 28 ). A recent retrospective analysis of 596 breast cancer patients who underwent ALND and chemotherapy revealed that, compared to preoperative surgery, patients undergoing NAC with ALND had a 1.5-fold increased risk of BCRL ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China

Du,

Yang,

Yang

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

ObjectiveThe aim of this study was to develop and validate a series of breast cancer-related lymphoedema risk prediction models using machine learning algorithms for early identification of high-risk individuals to reduce the incidence of postoperative breast cancer lymphoedema.MethodsThis was a retrospective study conducted from January 2012 to July 2022 in a tertiary oncology hospital. Subsequent to the collection of clinical data, variables with predictive capacity for breast cancer-related lymphoedema (BCRL) were subjected to scrutiny utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The entire dataset underwent a randomized partition into training and test subsets, adhering to a 7:3 distribution. Nine classification models were developed, and the model performance was evaluated based on accuracy, sensitivity, specificity, recall, precision, F-score, and area under curve (AUC) of the ROC curve. Ultimately, the selection of the optimal model hinged upon the AUC value. Grid search and 10-fold cross-validation was used to determine the best parameter setting for each algorithm.ResultsA total of 670 patients were investigated, of which 469 were in the modeling group and 201 in the validation group. A total of 174 had BCRL (25.97%). The LASSO regression model screened for the 13 features most valuable in predicting BCRL. The range of each metric in the test set for the nine models was, in order: accuracy (0.75–0.84), sensitivity (0.50–0.79), specificity (0.79–0.93), recall (0.50–0.79), precision (0.51–0.70), F score (0.56–0.69), and AUC value (0.71–0.87). Overall, LR achieved the best performance in terms of accuracy (0.81), precision (0.60), sensitivity (0.79), specificity (0.82), recall (0.79), F-score (0.68), and AUC value (0.87) for predicting BCRL.ConclusionThe study established that the constructed logistic regression (LR) model exhibits a more favorable amalgamation of accuracy, sensitivity, specificity, recall, and AUC value. This configuration adeptly discerns patients who are at an elevated risk of BCRL. Consequently, this precise identification equips nurses with the means to undertake timely and tailored interventions, thus averting the onset of BCRL.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China

Du,

Yang,

Yang

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…These measures include pseudo R 2 values, which were relatively low for both models, suggesting that the models explain a minority of the variation in lymphedema development. These values, in addition to accuracy statistics that fall short of 100%, highlight the fact that the tradeoff for simplifying model inputs is omission of potential predictors of BCRL, such as genetic information, type of chemotherapy, field of radiation, and postoperative complications . Sensitivity and specificity were reported in a single prior study of BCRL predictive models yet are key features of clinical prediction models that are needed to calculate NPV.…”

Section: Discussionmentioning

confidence: 99%

“…At least 13 BCRL predictive models have been published (eTable 4 in Supplement 1), although all have at least 1 conceptual issue that inhibits functionality. In addition to inclusion of data points that are not readily available, omission of SLNB, and diagnoses based on patient-reported symptoms, some studies define lymphedema based on retrospective identification via ICD - 9 or ICD - 10 diagnosis codes or treatment associated with lymphedema, both of which introduce bias.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

ImportanceBreast 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.ObjectiveTo create simple and accurate prediction models for BCRL that can be used to estimate preoperative or postoperative risk.Design, Setting, and ParticipantsIn 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 MeasuresDiagnosis 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.ResultsOf 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 &lt; .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 RelevanceIn 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.

show abstract

“…Comparison of the prediction model tested here with others in the literature (see Table 1) shows—based on the AUC, a combined measure of sensitivity and sensitivity—that the majority achieve a similar capacity to discriminate at‐risk patients. Among studies conducted in the Asian population, for example, Li et al (2017) reported an AUC of 0.706 in a nomogram validation study involving 409 Chinese breast cancer patients who underwent ALND. The 5‐year cumulative incidence of lymphoedema in this cohort was 22.3%, with the significant risk factors being higher BMI, neoadjuvant chemotherapy, more extensive axillary surgery and radiotherapy.…”

Section: Discussionmentioning

confidence: 99%

“…The most recent consensus document of the International Society of Lymphology lists higher BMI, more extensive lymph node dissection, more extensive surgical procedures, receipt of adjuvant therapy (including radiotherapy or chemotherapy) and a sedentary lifestyle as being more firmly supported as risk factors for lymphoedema (International Society of Lymphology, 2020). Various studies conducted in Asia (Byun et al, 2022; Li et al, 2017, 2020; Liu et al, 2021; Wang et al, 2016; Wei et al, 2021; Yuan et al, 2021), the Americas (Basta et al, 2017; Bevilacqua et al, 2012; Gross et al, 2019; Soran et al, 2016) and Europe (Bundred et al, 2020; Martínez‐Jaimez et al, 2022) have used risk factors such as these to develop prediction models for BCRL (see Table 1), which can then help to design strategies for preventing its onset (Snell et al, 2018). Importantly, the content of these prediction models differs depending on the population in which they were developed.…”

Section: Introductionmentioning

confidence: 99%

Temporal validation of a risk prediction model for breast cancer‐related lymphoedema in European population: A retrospective study

Martínez-Jaimez

Linares

Masià

et al. 2023

Journal of Advanced Nursing

View full text Add to dashboard Cite

AimsTo perform temporal validation of a risk prediction model for breast cancer‐related lymphoedema in the European population.DesignTemporal validation of a previously developed prediction model using a new retrospective cohort of women who had undergone axillary lymph node dissection between June 2018 and June 2020.MethodsWe reviewed clinical records to identify women who did and did not develop lymphoedema within 2 years of surgery and to gather data regarding the variables included in the prediction model. The model was calibrated by calculating Spearman's correlation between observed and expected cases. Its accuracy in discriminating between patients who did versus did not develop lymphoedema was assessed by calculating the area under the receiver operating characteristic curve (AUC).ResultsThe validation cohort comprised 154 women, 41 of whom developed lymphoedema within 2 years of surgery. The value of Spearman's coefficient indicated a strong correlation between observed and expected cases. Sensitivity of the model was higher than in the derivation cohort, as was the value of the AUC.ConclusionThe model shows a good capacity to discriminate women at risk of lymphoedema and may therefore help in developing improved care pathways for individual patients.Implications for the Profession and/or Patient CareIdentifying risk factors for lymphoedema secondary to breast cancer treatment is vital given its impact on women's physical and emotional well‐being.ImpactWhat problem did the study address? Risk of BCRL. What were the main findings? The prediction model has a good capacity to discriminate women at risk of lymphoedema. Where and on whom will the research have an impact? In clinical practice with women at risk of BCRL.Reporting MethodSTROBE checklist.What Does this Paper Contribute to the Wider Global Clinical Community?It presents a validated risk prediction model for BCRL.No Patient or Public ContributionThere was no patient or public contribution in the conduct of this study.

show abstract

Validation of a breast cancer nomogram to predict lymphedema in a Chinese population

Cited by 15 publications

References 25 publications

Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China

Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China

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

Temporal validation of a risk prediction model for breast cancer‐related lymphoedema in European population: A retrospective study

Contact Info

Product

Resources

About