Development and Validation of a Risk Model for Breast Cancer–Related Lymphedema

Kwan, Jennifer; Famiyeh, Petra; Su, Jie; Xu, Wei; Kwan, Benjamin Y. M.; Jones, Jennifer; Chang, Eugene; Yip, Kenneth W.; Liu, Feifei

doi:10.1001/jamanetworkopen.2020.24373

Cited by 22 publications

(38 citation statements)

References 59 publications

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“…However, the assessment of patients with lymphedema probably has bias. Kwan et al [ 31 ] conducted a prospective cohort study and did not have an outcome in advance, and there was no risk of bias in the assessment of predictors.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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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

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Section: Resultsmentioning

confidence: 99%

“…Question 8 is about the model performance. Most studies have internally validated the use of the training data indirectly, while Li et al and Kwan et al [ 30 , 31 ] were only randomly split-sample for validation.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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“…Hence, a series of explorations of replacement therapies such as targeted therapy, anti-angiogenic therapy, and immunotherapy have been motivated. With the rapid development of bioinformatics analysis technology, a growing number of attentions have been focused on the values of the risk characteristics including clinical and molecular features on the prognosis assessment of cancer patients ( 5 , 6 ). Although accumulated risk factors including glycolysis-related gene characteristics ( 7 , 8 ), autophagy-related gene characteristics ( 9 , 10 ), and long noncoding RNAs (lncRNAs)-related characteristics ( 11 , 12 ) have been identified to show satisfactory results on prediction outcomes of patients with various malignancies including RCC, discovering more novel evaluating methods to help make clinical decisions still received enormous interest.…”

Section: Introductionmentioning

confidence: 99%

A Novel Prognostic Model Based on Autophagy-Related Long Non-Coding RNAs for Clear Cell Renal Cell Carcinoma

Wei

et al. 2021

Front. Oncol.

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BackgroundRenal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, of which the clear cell renal cell carcinoma (ccRCC) accounts for the most subtypes. The increasing discoveries of abundant autophagy-related long non-coding RNAs (ARLNRs) lead to a resurgent interest in evaluating their potential on prognosis prediction. Based on a large number of ccRCC gene samples from TCGA and clinics, ARLNRs analysis will provide a novel perspective into this field.MethodsWe calculated the autophagy scores of each sample according to the expression levels of autophagy-related genes (ARGs) and screened the survival-related ARLNRs (sARLNRs) of ccRCC patients by Cox regression analysis. The high-risk group and the low-risk group were distinguished by the median score of the autophagy-related risk score (ARRS) model. The functional annotations were detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA). The expression levels of two kinds of sARLNRs in the renal tumor and adjacent normal tissues and cell lines were verified.ResultsThere were 146 ARLNRs selected by Pearson analysis. A total of 30 sARLNRs were remarkably correlated with the clinical outcomes of ccRCC patients. Eleven sARLNRs (AC002553.1, AC092611.2, AL360181.2, AP002807.1, AC098484.1, AL513218.1, AC008735.2, MHENCR, AC020907.4, AC011462.4, and AC008870.2) with the highest prognosis value were recruited to establish the ARRS in which the overall survival (OS) in the high-risk group was shorter than that in the low-risk group. ARRS could be treated as an independent prognostic factor and has significant correlations with OS. The distributions of autophagy genes were different between the high-risk group and the low-risk group. In addition, we also found that the expression levels of AC098484.1 in ccRCC cell lines and tumor tissues were lower than those in HK-2 and adjacent normal tissues, but AL513218.1 showed the inverse level. Furthermore, the AC098484.1 expressed decreasingly with the more advanced T-stages, but AL513218.1 gradually increased.ConclusionOur study identified and verified some sARLNRs with clinical significances and revealed their potential values on predicting prognoses of ccRCC patients, which may provide a novel perspective for autophagy-related research and clinical decisions.

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“…a) en primer lugar, el que tras la revisión bibliográfica nos parece el más sensible y con buena especificidad, que es el establecer el diagnóstico de linfedema cuando la diferencia volumétrica entre cada segmento o cada porción de la extremidad es superior al 10%, que es el límite generalmente establecido como clínicamente significativo (Sun et al 2016); b) en segunda lugar, uno menos sensible y muy específico que es establecer linfedema cuando la diferencia absoluta, en este caso en la extremidad completa, es superior a 200 ml, (Kwan et al 2020).…”

Section: Sobre El Cálculo Del Volumen Y Criterio Diagnóstico De Linfeunclassified

“…c) en tercer lugar, un criterio usado en la clínica, que creemos es el menos específico que es el de establecer el diagnóstico de linfedema cuando la diferencia entre cualquiera de los perímetros al mismo nivel entre las extremidades, sea superior a 2 cm, (Kwan et al 2020).…”

Section: Sobre El Cálculo Del Volumen Y Criterio Diagnóstico De Linfeunclassified

Efectos del entrenamiento de fuerza de larga duración sobre el volumen de las extremidades superiores y el grado de linfedema en supervivientes al cáncer de mama = Effects of long-term strength training on upper extremity volume and degree of lymphedema in breast cancer survivors

Tapia¹,

Manuel²

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Development and Validation of a Risk Model for Breast Cancer–Related Lymphedema

Cited by 22 publications

References 59 publications

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

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

A Novel Prognostic Model Based on Autophagy-Related Long Non-Coding RNAs for Clear Cell Renal Cell Carcinoma

Efectos del entrenamiento de fuerza de larga duración sobre el volumen de las extremidades superiores y el grado de linfedema en supervivientes al cáncer de mama = Effects of long-term strength training on upper extremity volume and degree of lymphedema in breast cancer survivors

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